基于解析模型预测控制的欠驱动船舶路径跟踪控制研究
|
摘要
现代造船业、航运业的蓬勃发展,对船舶的控制性能提出了越来越高的要求,传统的航向控制已远远不能满足实际需求,研究更精确的船舶运动控制技术日益受到人们的重视。本文以常规水面船舶为研究对象,研究了欠驱动船舶路径跟踪控制问题,设计了欠驱动船舶在不同控制要求下的路径跟踪控制器,使得欠驱动船舶在控制器的作用下能够跟踪并最终稳定在预定的参考路径上。欠驱动船舶路径跟踪控制系统具有欠驱动特性、强非线性、易受到模型参数变化及外界干扰影响等特点,是一个典型的非线性控制系统,对该控制系统的研究有助于探讨一般非线性系统的控制问题,具有重要的学术研究价值。同时,对欠驱动船舶路径跟踪控制的研究,也有助于解决船舶在复杂环境中的操纵控制、自动靠离泊等实际工程问题。
本文采用解析模型预测控制这一先进的非线性控制方法对欠驱动船舶路径跟踪控制问题进行了深入的研究,针对船舶控制系统中存在的非线性、欠驱动特性、外界干扰、模型参数的不确定性,开发了具有鲁棒性能和自适应性能的欠驱动船舶路径跟踪控制器。本文主要完成了以下工作:
1.建立了欠驱动船舶路径跟踪控制系统的数学模型。建模是控制器设计的基础,本文总结了前人的研究成果,建立了多种欠驱动船舶路径跟踪控制系统的数学模型,包括欠驱动船舶直线路径跟踪控制、曲线路径跟踪控制模型,有外界干扰及模型参数不确定的路径跟踪控制模型,基于非线性滤波的路径跟踪控制模型,为后面的控制器设计奠定了基础。
2.针对一阶非线性K-T模型的欠驱动船舶直线路径跟踪控制系统,分别基于状态空间非线性(离散)广义预测控制(Generalized Predictive Control, GPC)和解析模型预测控制方法研究了欠驱动船舶直线路径跟踪控制算法。在基于(离散)GPC的控制算法中,控制模型中未考虑外界干扰的影响,控制算法能够使得欠驱动船舶渐近稳定在设定的直线参考路径上,并对设定的系统的输出变化具有一定的鲁棒性。在基于解析模型预测控制的路径跟踪控制算法中,首先考虑了无外界干扰的欠驱动船舶直线路径跟踪控制的数学模型,通过引入重定义输出变量,使得原单输入多输出控制系统转化为等价的单输入单输出控制系统,基于解析模型预测控制技术提出了具有全局渐近收敛能力的欠驱动船舶直线路径跟踪控制算法,该控制算法能够保证重定义变量及其组成元素渐近收敛到零,使得欠驱动船舶渐近稳定在给定的直线参考路径上。在此基础上,将解析模型预测控制方法与高增益观测器(High Gain Observer, HGO)技术相结合,研究了有外界干扰影响的欠驱动船舶直线路径跟踪控制问题,所提出的路径跟踪控制算法能够使得船舶在外界干扰的影响下最终稳定在给定的直线路径上。并系统的总结了解析模型预测控制与(离散)GPC、反馈线性化方法之间的联系与区别。
3.针对欠驱动船舶曲线路径跟踪控制中存在的不确定相关度问题,提出了一种基于非切换解析模型预测控制方法的欠驱动船舶路径跟踪控制算法。该控制算法是连续的、非奇异的,避免了系统振荡,解决了不确定相关度问题。所提出的路径跟踪控制算法能够使得船舶跟踪并最终稳定在设定的曲线路径上。
4.在Serret-Frenet标架下研究了欠驱动船舶曲线路径跟踪控制算法。将船舶运动模型转化到Serret-Frenet标架下进行研究,并通过引入重定义输出,将单输入多输出控制系统转化为等价的单输入单输出控制系统,简化了控制器设计。在Serret-Frenet标架下,首先,研究了模型参数确定且无外界干扰情况的欠驱动船舶曲线路径跟踪控制问题,基于解析模型预测控制、Serret-Frenet标架以及重定义输出技术提出的控制算法能够使得欠驱动船舶跟踪并稳定在设定的曲线参考路径上,路径跟踪误差(包括位置误差和方位误差)及其对应的重定义输出能够渐近收敛到零。其次,研究了风浪等有界干扰、均匀流干扰影响下的欠驱动船舶路径跟踪控制问题,应用解析模型预测控制与非线性观测器方法设计了欠驱动船舶路径跟踪控制器,提出了对外界干扰具有鲁棒性的路径跟踪算法,使得欠驱动船舶能够抵抗风浪、均匀流的干扰,跟踪并收敛到设定的参考路径。最后,针对模型中参数不确定的情况,采用解析模型预测控制与模型参考自适应辨识联合控制的方法,提出了具有自适应性能的欠驱动船舶路径跟踪控制器,该控制器由两部分组成,即针对对象标称模型设计的解析模型预测控制器和针对对象不确定参数设计的模型参考自适应辨识算法,在模型参数发生变化甚至是“突变”时,该控制器能够保证船舶稳定在设定的参考路径上,不确定参数的估计值以及控制输入平稳变化,避免了系统由于参数“突变”发生剧烈振荡。
5.针对欠驱动船舶路径跟踪控制系统中存在的随机干扰以及模型参数的不确定性,应用UKF(Unscented Kalman Filter)方法对状态和不确定参数进行联合估计,并结合解析模型预测控制技术,设计了对随机干扰和不确定参数具有鲁棒性能的欠驱动船舶自适应路径跟踪控制算法。
同已有的研究相比,本文的创新点在于:
1.首次将解析模型预测控制方法应用于欠驱动船舶路径跟踪控制的研究。
2.针对欠驱动船舶曲线路径跟踪控制中存在的不确定相关度问题,提出了一种基于连续非切换解析模型预测控制方法的欠驱动船舶路径跟踪控制算法。
3.在Serret-Frenet标架下研究了欠驱动船舶路径跟踪控制,并通过引入重定义输出,使得单输入多输出控制系统转化为等价的单输入单输出控制系统,简化了控制器设计;将解析模型预测控制方法与非线性干扰观测器、模型参考自适应辨识方法相结合,提出了对外界干扰、不确定参数具有鲁棒性、自适应性的欠驱动船舶路径跟踪控制算法。
4.将解析模型预测控制方法与UKF相结合,其中UKF用于对控制系统中的状态和参数进行联合估计,所提出的欠驱动船舶路径跟踪控制算法对控制系统中存在的随机干扰和不确定参数具有鲁棒性和自适应性。
通过本文的研究,验证了解析模型预测控制方法用于欠驱动船舶路径跟踪控制器设计的有效性,为研究精确的船舶运动控制提供了一种新的非线性控制方法。
The rapid development of modern shipbuilding and shipping has brought higher and higher requirement of ships’control performances. The conventional course control is far from satisfying the practical requirements. Therefore, the research on more precise ship motion control techniques has received increasing attention in recent years. The problem of path following control for underactuated surface ships is studied in this thesis. For the different control aims, path following controllers are designed to drive the underactuated ships to follow and stabilize onto the desired reference path. The control system is a typical nonlinear control system which has characters such as underactuated, strong nonlinear, susceptible to varying parameters and environmental disturbances. The study of the subject is contributive to study the general nonlinear system control problem and has great significance in academic study. It will benefit the practical engineering applications such as manoeuvring motion control, automatic mooring and unmooring of ships in complicated environment.
A new advanced nonlinear control method, analytic model predictive control, is proposed to study the problem of path following control for underactuated ships. With respect to the nonlinear, underactuated characters, environmental disturbances and uncertain parameters of ship control system, some applicable algorithms are proposed which have the performances of both robustness and adaptiveness on path following control problem for underactuated ships. The main results achieved in this thesis are as follows:
1. Mathematical models of path following system for underactuated ships are established. Modeling plays a key role in controller design. Based on the study of other researchers, this thesis has established some kinds of mathematical models of path following system for underactuated ships, including straight-line and curve path following control models, path following control models with environmental disturbances and uncertain parameters, path following control model based on nonlinear filter. These models are the base of the controller design.
2. With respect to the straight-line path following control system based on first-order nonlinear K-T model for underactuated ships, the path following control algorithms are studied by using state space nonlinear (discrete) generalized predictive control (GPC) and analytic model predictive control methods respectively. In the control algorithm by using (discrete) GPC, environmental disturbance is not considered in the control model. The proposed control algorithm can asymptotically stabilize the underactuated ship onto the desired straight-line reference path, and is robust to the outputs’changes. In the control algorithm by using analytic model predictive control, the straight-line path following system for underactuated ships without environmental disturbance is considered first. By introducing the output-redefinition, the original SIMO system is transformed into an equivalent SISO system. Based on analytic model predictive control technique, the global asymptotically convergent straight-line path following algorithm is proposed. This control algorithm can guarantee the output-redefinition and its compositions all asymptotically converge to zero. The underactuated ship can be driven onto the desired straight-line reference path. And then, the high gain observer technique and the analytic model predictive control are combined, and the straight-line path following control system with environmental disturbance is studied. The proposed path following control algorithm can make the underactuated ship converge to the desired straight-line path in spite of environmental disturbance. Based on the study, the relations and differences of analytic model predictive control, (discrete) GPC, and feedback linearization techniques are systematically summarized.
3. The problem of ill-defined relative degree is studied which occurrs in the curve path following control for underactuated ships. A path following control algorithm for underactuated ships is proposed by using non-switch analytic model predictive control method. This control algorithm is continuous, non-singular, and the system fluctuation is avoided. The problem of ill-defined relative degree is solved. The proposed path following control algorithm can drive the underactuated ship converge to the desired curve path.
4. The curve path following control for underactuated ships is studied in the Serret-Frenet frame. By introducing the output-redefinition, the SIMO system is transformed into an equivalent SISO system which simplifies the controller design. In the Serret-Frenet frame, the curve path following control problem with fixed parameters and without environmental disturbances is studied first. Based on analytic model predictive control, Serret-Frenet frame and output-redefinition techniques, the proposed control algorithm can drive the underactuated ship converge onto the desired path. Path following errors including position error and orientation error all converge to zero; and the corresponding output-redefinition also asymptotically converge to zero. And then, robust path following control algorithms for underactuated ships are proposed with respect to the bounded disturbances induced by wind, waves and disturbance due to uniform current. Analytic model predictive control and nonlinear disturbance observer techniques are used to design the path following controllers. With help of the control algorithms, the underactuated ships can be driven asymptotically onto the desired path in spite of the environmental disturbances. Finally, aiming at the model with uncertain parameters, a joint control algorithm is adopted to get an adaptive path following controller of underactuated ships. The controller is made up of two parts: one is analytic model predictive controller with respect to the nominal system, the other is model reference adaptive identification algorithm with respect to the uncertain parameters. Even if the parameters change abruptly, the proposed controller can stabilize the underactuated ships onto the desired path. The estimation of uncertain parameters and the control input can change smoothly. The system’s fluctuation induced by the parameters’abrupt changes is avoided.
5. With respect to the random disturbances and the parameters’uncertainty of underactuated ships, UKF (Unscented Kalman Filter) is used to estimate the states and uncertain parameters. By combining the analytic model predictive control technique with UKF, the adaptive path following controller is proposed which is robust to random disturbances and uncertain parameters.
Comparing with the previous studies by other researchers, the main innovation points of this thesis can be summarized as follows:
1. Analytic model predictive control method is used for the first time to study the path following control of underactuated ships.
2. With respect to the problem of ill-defined relative degree which occurrs in the curve path following control for underactuated ships, a path following control algorithm for underactuated ships is proposed by using continuous non-switch analytic model predictive control method.
3. The path following control problem is studied in the Serret-Frenet frame. By introducing Serret-Frenet frame and output-redefinition, the original SIMO system is transformed into an equivalent SISO system which simplifies the controller design. By combining analytic model predictive control with nonlinear disturbance observer and model reference adaptive identification, the path following control algorithms are proposed which are robust and adaptive to disturbances and uncertain parameters.
4. The path following control algorithm is proposed by combining analytic model predictive control and UKF, where UKF is used to estimate the states and uncertain parameters. The proposed control algorithm is robust and adaptive to random disturbances and uncertain parameters.
The study of this thesis has demonstrated that the proposed path following control algorithms by using analytic model predictive control and other methods are effective. It provides a new nonlinear control method to study the precise ship motion control technique.
本文采用解析模型预测控制这一先进的非线性控制方法对欠驱动船舶路径跟踪控制问题进行了深入的研究,针对船舶控制系统中存在的非线性、欠驱动特性、外界干扰、模型参数的不确定性,开发了具有鲁棒性能和自适应性能的欠驱动船舶路径跟踪控制器。本文主要完成了以下工作:
1.建立了欠驱动船舶路径跟踪控制系统的数学模型。建模是控制器设计的基础,本文总结了前人的研究成果,建立了多种欠驱动船舶路径跟踪控制系统的数学模型,包括欠驱动船舶直线路径跟踪控制、曲线路径跟踪控制模型,有外界干扰及模型参数不确定的路径跟踪控制模型,基于非线性滤波的路径跟踪控制模型,为后面的控制器设计奠定了基础。
2.针对一阶非线性K-T模型的欠驱动船舶直线路径跟踪控制系统,分别基于状态空间非线性(离散)广义预测控制(Generalized Predictive Control, GPC)和解析模型预测控制方法研究了欠驱动船舶直线路径跟踪控制算法。在基于(离散)GPC的控制算法中,控制模型中未考虑外界干扰的影响,控制算法能够使得欠驱动船舶渐近稳定在设定的直线参考路径上,并对设定的系统的输出变化具有一定的鲁棒性。在基于解析模型预测控制的路径跟踪控制算法中,首先考虑了无外界干扰的欠驱动船舶直线路径跟踪控制的数学模型,通过引入重定义输出变量,使得原单输入多输出控制系统转化为等价的单输入单输出控制系统,基于解析模型预测控制技术提出了具有全局渐近收敛能力的欠驱动船舶直线路径跟踪控制算法,该控制算法能够保证重定义变量及其组成元素渐近收敛到零,使得欠驱动船舶渐近稳定在给定的直线参考路径上。在此基础上,将解析模型预测控制方法与高增益观测器(High Gain Observer, HGO)技术相结合,研究了有外界干扰影响的欠驱动船舶直线路径跟踪控制问题,所提出的路径跟踪控制算法能够使得船舶在外界干扰的影响下最终稳定在给定的直线路径上。并系统的总结了解析模型预测控制与(离散)GPC、反馈线性化方法之间的联系与区别。
3.针对欠驱动船舶曲线路径跟踪控制中存在的不确定相关度问题,提出了一种基于非切换解析模型预测控制方法的欠驱动船舶路径跟踪控制算法。该控制算法是连续的、非奇异的,避免了系统振荡,解决了不确定相关度问题。所提出的路径跟踪控制算法能够使得船舶跟踪并最终稳定在设定的曲线路径上。
4.在Serret-Frenet标架下研究了欠驱动船舶曲线路径跟踪控制算法。将船舶运动模型转化到Serret-Frenet标架下进行研究,并通过引入重定义输出,将单输入多输出控制系统转化为等价的单输入单输出控制系统,简化了控制器设计。在Serret-Frenet标架下,首先,研究了模型参数确定且无外界干扰情况的欠驱动船舶曲线路径跟踪控制问题,基于解析模型预测控制、Serret-Frenet标架以及重定义输出技术提出的控制算法能够使得欠驱动船舶跟踪并稳定在设定的曲线参考路径上,路径跟踪误差(包括位置误差和方位误差)及其对应的重定义输出能够渐近收敛到零。其次,研究了风浪等有界干扰、均匀流干扰影响下的欠驱动船舶路径跟踪控制问题,应用解析模型预测控制与非线性观测器方法设计了欠驱动船舶路径跟踪控制器,提出了对外界干扰具有鲁棒性的路径跟踪算法,使得欠驱动船舶能够抵抗风浪、均匀流的干扰,跟踪并收敛到设定的参考路径。最后,针对模型中参数不确定的情况,采用解析模型预测控制与模型参考自适应辨识联合控制的方法,提出了具有自适应性能的欠驱动船舶路径跟踪控制器,该控制器由两部分组成,即针对对象标称模型设计的解析模型预测控制器和针对对象不确定参数设计的模型参考自适应辨识算法,在模型参数发生变化甚至是“突变”时,该控制器能够保证船舶稳定在设定的参考路径上,不确定参数的估计值以及控制输入平稳变化,避免了系统由于参数“突变”发生剧烈振荡。
5.针对欠驱动船舶路径跟踪控制系统中存在的随机干扰以及模型参数的不确定性,应用UKF(Unscented Kalman Filter)方法对状态和不确定参数进行联合估计,并结合解析模型预测控制技术,设计了对随机干扰和不确定参数具有鲁棒性能的欠驱动船舶自适应路径跟踪控制算法。
同已有的研究相比,本文的创新点在于:
1.首次将解析模型预测控制方法应用于欠驱动船舶路径跟踪控制的研究。
2.针对欠驱动船舶曲线路径跟踪控制中存在的不确定相关度问题,提出了一种基于连续非切换解析模型预测控制方法的欠驱动船舶路径跟踪控制算法。
3.在Serret-Frenet标架下研究了欠驱动船舶路径跟踪控制,并通过引入重定义输出,使得单输入多输出控制系统转化为等价的单输入单输出控制系统,简化了控制器设计;将解析模型预测控制方法与非线性干扰观测器、模型参考自适应辨识方法相结合,提出了对外界干扰、不确定参数具有鲁棒性、自适应性的欠驱动船舶路径跟踪控制算法。
4.将解析模型预测控制方法与UKF相结合,其中UKF用于对控制系统中的状态和参数进行联合估计,所提出的欠驱动船舶路径跟踪控制算法对控制系统中存在的随机干扰和不确定参数具有鲁棒性和自适应性。
通过本文的研究,验证了解析模型预测控制方法用于欠驱动船舶路径跟踪控制器设计的有效性,为研究精确的船舶运动控制提供了一种新的非线性控制方法。
The rapid development of modern shipbuilding and shipping has brought higher and higher requirement of ships’control performances. The conventional course control is far from satisfying the practical requirements. Therefore, the research on more precise ship motion control techniques has received increasing attention in recent years. The problem of path following control for underactuated surface ships is studied in this thesis. For the different control aims, path following controllers are designed to drive the underactuated ships to follow and stabilize onto the desired reference path. The control system is a typical nonlinear control system which has characters such as underactuated, strong nonlinear, susceptible to varying parameters and environmental disturbances. The study of the subject is contributive to study the general nonlinear system control problem and has great significance in academic study. It will benefit the practical engineering applications such as manoeuvring motion control, automatic mooring and unmooring of ships in complicated environment.
A new advanced nonlinear control method, analytic model predictive control, is proposed to study the problem of path following control for underactuated ships. With respect to the nonlinear, underactuated characters, environmental disturbances and uncertain parameters of ship control system, some applicable algorithms are proposed which have the performances of both robustness and adaptiveness on path following control problem for underactuated ships. The main results achieved in this thesis are as follows:
1. Mathematical models of path following system for underactuated ships are established. Modeling plays a key role in controller design. Based on the study of other researchers, this thesis has established some kinds of mathematical models of path following system for underactuated ships, including straight-line and curve path following control models, path following control models with environmental disturbances and uncertain parameters, path following control model based on nonlinear filter. These models are the base of the controller design.
2. With respect to the straight-line path following control system based on first-order nonlinear K-T model for underactuated ships, the path following control algorithms are studied by using state space nonlinear (discrete) generalized predictive control (GPC) and analytic model predictive control methods respectively. In the control algorithm by using (discrete) GPC, environmental disturbance is not considered in the control model. The proposed control algorithm can asymptotically stabilize the underactuated ship onto the desired straight-line reference path, and is robust to the outputs’changes. In the control algorithm by using analytic model predictive control, the straight-line path following system for underactuated ships without environmental disturbance is considered first. By introducing the output-redefinition, the original SIMO system is transformed into an equivalent SISO system. Based on analytic model predictive control technique, the global asymptotically convergent straight-line path following algorithm is proposed. This control algorithm can guarantee the output-redefinition and its compositions all asymptotically converge to zero. The underactuated ship can be driven onto the desired straight-line reference path. And then, the high gain observer technique and the analytic model predictive control are combined, and the straight-line path following control system with environmental disturbance is studied. The proposed path following control algorithm can make the underactuated ship converge to the desired straight-line path in spite of environmental disturbance. Based on the study, the relations and differences of analytic model predictive control, (discrete) GPC, and feedback linearization techniques are systematically summarized.
3. The problem of ill-defined relative degree is studied which occurrs in the curve path following control for underactuated ships. A path following control algorithm for underactuated ships is proposed by using non-switch analytic model predictive control method. This control algorithm is continuous, non-singular, and the system fluctuation is avoided. The problem of ill-defined relative degree is solved. The proposed path following control algorithm can drive the underactuated ship converge to the desired curve path.
4. The curve path following control for underactuated ships is studied in the Serret-Frenet frame. By introducing the output-redefinition, the SIMO system is transformed into an equivalent SISO system which simplifies the controller design. In the Serret-Frenet frame, the curve path following control problem with fixed parameters and without environmental disturbances is studied first. Based on analytic model predictive control, Serret-Frenet frame and output-redefinition techniques, the proposed control algorithm can drive the underactuated ship converge onto the desired path. Path following errors including position error and orientation error all converge to zero; and the corresponding output-redefinition also asymptotically converge to zero. And then, robust path following control algorithms for underactuated ships are proposed with respect to the bounded disturbances induced by wind, waves and disturbance due to uniform current. Analytic model predictive control and nonlinear disturbance observer techniques are used to design the path following controllers. With help of the control algorithms, the underactuated ships can be driven asymptotically onto the desired path in spite of the environmental disturbances. Finally, aiming at the model with uncertain parameters, a joint control algorithm is adopted to get an adaptive path following controller of underactuated ships. The controller is made up of two parts: one is analytic model predictive controller with respect to the nominal system, the other is model reference adaptive identification algorithm with respect to the uncertain parameters. Even if the parameters change abruptly, the proposed controller can stabilize the underactuated ships onto the desired path. The estimation of uncertain parameters and the control input can change smoothly. The system’s fluctuation induced by the parameters’abrupt changes is avoided.
5. With respect to the random disturbances and the parameters’uncertainty of underactuated ships, UKF (Unscented Kalman Filter) is used to estimate the states and uncertain parameters. By combining the analytic model predictive control technique with UKF, the adaptive path following controller is proposed which is robust to random disturbances and uncertain parameters.
Comparing with the previous studies by other researchers, the main innovation points of this thesis can be summarized as follows:
1. Analytic model predictive control method is used for the first time to study the path following control of underactuated ships.
2. With respect to the problem of ill-defined relative degree which occurrs in the curve path following control for underactuated ships, a path following control algorithm for underactuated ships is proposed by using continuous non-switch analytic model predictive control method.
3. The path following control problem is studied in the Serret-Frenet frame. By introducing Serret-Frenet frame and output-redefinition, the original SIMO system is transformed into an equivalent SISO system which simplifies the controller design. By combining analytic model predictive control with nonlinear disturbance observer and model reference adaptive identification, the path following control algorithms are proposed which are robust and adaptive to disturbances and uncertain parameters.
4. The path following control algorithm is proposed by combining analytic model predictive control and UKF, where UKF is used to estimate the states and uncertain parameters. The proposed control algorithm is robust and adaptive to random disturbances and uncertain parameters.
The study of this thesis has demonstrated that the proposed path following control algorithms by using analytic model predictive control and other methods are effective. It provides a new nonlinear control method to study the precise ship motion control technique.
引文
[1]邹早建.船舶操纵性研究进展.中国造船工程学会第六届船舶力学学术委员会全体会议. 2006年8月,中国镇江.
[2]贾欣乐,张显库.船舶运动智能控制与H∞鲁棒控制.大连:大连海事大学出版社, 2002.
[3]张显库,贾欣乐.船舶运动控制.北京:国防工业出版社, 2006.
[4] Encarnacao P. and Pascoal A. Combined trajectory tracking and path following: an application to the coordinated control of autonomous marine craft. Proceedings of the 40th IEEE Conference on Decision and Control, Orlando, Florida USA, 2001: 964-969.
[5] Aguiar A.P., Dacic D.B, Hespanha J.P. and Kokotovic P. Path following or reference tracking? An answer relaxing the limits to performance. Proceedings of the 5th IFAC/EURON Symp. Intell. Auton Veh., Lisbon, Portugal, July 2004.
[6] Aguiar A.P., Hespanha J.P. and Kokotovic P. Path following for nonminimum phase systems removes performance limitations. IEEE Transactions on Automatic Control, 2005, Vol. 50, No. 2: 234-239.
[7] Do K.D. and Pan J. State- and output-feedback robust path-following controllers for underactuated ships using Serret-Frenet frame. Ocean Engineering, 2004, Vol. 31: 587-613.
[8] Encarnacao P., Pascoal A. and Arcak M. Path following control of marine vehicles. The 4th Portuguese Conference on Automatic Control: 31-36.
[9] Reyhanoglu M., Van der Schaft A., McClamroch N.H. and Kolmanovsky I. Dynamics and control of a class of underactuated mechanical systems. IEEE Transactions on Automatic Control, 1999, Vol. 44, No. 9: 1663-1671.
[10] Reyhanoglu M., Van der Schaft A., McClamroch N.H. and Kolmanovsky I. Nonlinear control of a class of underactuated systems. Proceedings of the 35th Conference on Decision and Control, Kobe, Japan, 1996: 1682-1687.
[11]韩冰.欠驱动船舶非线性控制研究. [博士学位论文],哈尔滨:哈尔滨工程大学, 2004.
[12]周祥龙,赵景波.欠驱动非线性控制方法综述.工业仪表与自动化装置, 2004, Vol. 5: 10-13.
[13] Reyhanoglu M. Exponential stability of an underactuated autonomous surface vessel. Automatica, 1997, Vol. 33, No. 12: 2249-2254.
[14] Wichlund K.Y., Sordalen O.J. and Egeland O. Control of vehicles with second-order nonholonomic constraints: underactuated vehicles. Proc. European Control Conf., Rome, Italy, 1995: 3086-3091.
[15] Pettersen K.Y. and Egeland O. Robust control of an underactuated surface vessel with thruster dynamics. Proceedings of the American Control Conference, New Mexico, June 1997: 3411-3415.
[16] Pettersen K.Y. and Egeland O. Exponential stabilization of an underactuated surface vessel. Proceedings of the 35th Conference on Decision and Control, Kobe, Japan, 1996: 967-972.
[17]李铁山.船舶直线航迹控制非线性设计方法. [博士学位论文],大连:大连海事大学, 2005.
[18] Holzhuter T. and Schultze S. Operating experience with a high-precision track controller for commercial ships. Control Eng. Practice, 1996, Vol. 4, No. 3: 343-350.
[19] Holzhuter T. LQG approach for the high-precision track control of ship. IEE Proceedings of Control Theory Application, 1997, Vol. 44, No. 2: 121-127.
[20] Cimen T. Development and validation of a mathematical model for control of constrained non-linear oil tanker motion. Mathematical and Computer Modeling of Dynamical Systems, 2008: 1-33.
[21] Cimen T. On-line nonlinear optimal maneuvering control of large tankers in restricted waterways. Proceedings of the 7th IFAC Conference on Maneuvering and Control of Marine Craft, Lisbon, Portugal, 2006.
[22] Sarioz K. and Narli E. Assessment of manoeuvring performance of large tankers in restricted waterways: a real time simulation approach. Ocean Engineering, 2003, Vol. 30: 1535-1551.
[23]彭秀艳,李小军,沈艳,赵希人.大型船舶航迹多变量随机最优控制.船舶工程, 2003, Vol. 25, No. 3: 41-45.
[24]于洋.卡尔曼滤波器在船舶航迹跟踪中的应用.江苏船舶, 2005, Vol. 22, No. 2: 31-39.
[25]张戎军,许汉珍.船舶操纵非线性系统的鲁棒变结构控制.控制与决策, 1994, Vol. 9, No. 5: 360-366.
[26] Zhang R.J., Chen Y.B., Sun Z.Q., Sun F.C. and Xu H.Z. Path control of a surface ship in restricted waters using sliding mode. Proceedings of the 37th IEEE Conference on Decision and Control, Tampa, Florida USA, 1998: 3195-3200.
[27] Zhang R.J., Chen Y.B., Sun Z.Q., Sun F.C. and Xu H.Z. Path control of a surfaceship in restricted waters using sliding mode. IEEE Transactions on Control Systems Technology, 2000, Vol. 8, No. 4: 722-732.
[28]匡红波,施小成,田亚杰,付明玉.全垫升气垫船航迹保持的变结构模糊PID控制.控制理论与应用, 2006, Vol. 25 No. 8: 4-6.
[29]马岭,崔维成. NTSM控制的AUV路径跟踪控制研究.中国造船, 2006, Vol. 47, No. 4: 76-82.
[30] Ma L. and Cui W.C. Path following control of a deep-sea manned submersible based upon NTSM. China Ocean Engineering. 2005, Vol. 19, No. 4: 625-636.
[31]马岭,崔维成.基于模糊混合控制的自治水下机器人路径跟踪控制.控制理论与应用, 2006, Vol. 23, No. 3: 341-346.
[32]卜仁祥.欠驱动水面船舶非线性反馈控制研究. [博士学位论文],大连:大连海事大学,2007.
[33]卜仁祥,刘正江,胡江强.基于动态非线性滑动模态的欠驱动船舶直线航迹控制.清华大学学报(自然科学版), 2007, Vol. 47, No. S2: 1880-1883.
[34] Burns R.S. The use of artificial neural networks for the intelligent optimal control of surface ships. IEEE Journal of Oceanic Engineering, 1995, Vol. 2, No. 1: 65-72.
[35] Zhang Y., Hearn G.E. and Sen P. A neural network approach to ship track-keeping control. IEEE Journal of Oceanic Engineering, 1996, Vol. 21, No.4: 513-527.
[36]程启明,刘其明,王志宏,孙干超.船舶航迹保持的在线SIMO神经网络控制器研究.工业仪表与自动化装置, 2000, No. 5: 3-7.
[37]程启明,万德钧,陈熙源.船舶航迹自动舵的神经网络控制方法研究.仪器仪表学报, 1999, Vol. 20, No. 4: 371-375.
[38]陈雪丽,程启明.船舶航迹保持的神经网络控制器研究,河海大学学报, 2001, Vol. 29, No. 5: 70-73.
[39]周岗,周永余,陈永冰.基于模糊理论的舰船航迹控制器.海军工程大学学报, 2000, No.3: 47-56.
[40]周岗,周永余,陈永冰,乔力争,卞鸿巍.舰船航迹控制器中模糊线性化方法的应用.中国惯性技术学报, 2000, Vol. 8, No. 4: 67-71.
[41] Yang Y.S., Zhou C.J. and Ren J.S. Model reference adaptive robust fuzzy control for ship steering autopilot with uncertain nonlinear systems. Applied Soft Computing, 2003, Vol. 3: 305–316.
[42] Yang Y.S. Direct robust adaptive fuzzy control (DRAFC) for uncertain nonlinear systems using small gain theorem. Fuzzy Sets and Systems, 2005, Vol. 151: 79-97.
[43] Velagic J., Vukic Z. and Omerdic E. Adaptive fuzzy ship autopilot for track-keeping. Control Engineering Practice, 2003, Vol. 11: 433-443.
[44] Fossen T.I., Breivik M. and Skjetne R. Line-of-Sight path following of underactuated marine craft. Proceedings of the 6th IFAC Conference on Maneuvering and Control of Marine Craft, Girona, Spain, 2003: 244-249.
[45] Breivik M. and Fossen T.I. Path following of straight lines and circles for marine surface vessels. Proceedings of the 6th IFAC CAMS, Ancona, Italy, 2004: 65-70.
[46] Breivik M. and Fossen T.I. Path following for marine surface vessels. Proceedings of the Oceans’04, Kobe, Japan, 2004: 2282-2289.
[47]孔祥军.船舶操纵运动模拟和智能技术的应用研究. [硕士学位论文],武汉:武汉理工大学, 2004.
[48] Fliess M., Levine J., Martin P. and Rouchon P. Flatness and defect of nonlinear systems: introductory theory and examples. International Journal of Control, 1995, Vol. 61, No. 6: 1327-1361.
[49] Ramirez H.S. and Ibanez C.A. On the control of the hovercraft system. Dynamics and Control, 2000, Vol. 10: 151-163.
[50] Ramirez H.S. and Ibanez C.A. The control of the hovercraft system: a flatness based approach. Proceedings of the 2000 IEEE International Conference on Control Applications, Anchorage, Alaska, USA, 2000: 692-697.
[51]韩冰,赵国良.基于微分平滑的欠驱动船舶航迹控制.哈尔滨工程大学学报,2004, Vol.25, No. 6: 709-713.
[52] Isidori A. Nonlinear control systems [3rd Edition]. New York: Springer-Verlag, 1995.
[53] Indiveri G., Aicardi M. and Casalino G. Nonlinear time-invariant feedback control of an underactuated marine vehicle along a straight course. Proceedings of the 5th IFAC Conference on Maneuvering and Control of Marine Craft, Aalborg, Denmark, 2000: 221-226.
[54] Pettersen K.Y. and Lefeber E. Way-point tracking control of ships. Proceedings of the 40th IEEE Conference on Decision and Control, Orlando, Florida USA, 2001: 940-945.
[55]李铁山,杨盐生,郑云峰.不完全驱动船舶非线性控制.交通运输工程学报, 2003, Vol. 3, No. 4: 39-43.
[56]李铁山,杨盐生,郑云峰.不完全驱动船舶航迹控制输入输出线性化设计.系统工程与电子技术, 2004, Vol. 26, No. 7: 945-948.
[57]周岗,姚琼荟,陈永冰,周永余,李文魁.船舶直线航迹控制系统全局渐近稳定的充分条件.海军工程大学学报, 2006, Vol. 18, No. 3: 52-56.
[58]周岗,姚琼荟,陈永冰,周永余.基于输入输出线性化的船舶全局直线航迹控制,控制理论与应用. 2007, Vol. 24 No. 1: 117-121.
[59]周岗,陈永冰,姚琼荟,周永余,李文魁.一类船舶直线航迹控制系统全局渐近稳定的充分条件及推论.自动化学报, 2007, Vol. 33, No. 11: 1204-1208.
[60]周岗,姚琼荟,陈永冰,周永余,李文魁.不完全驱动船舶直线控制稳定性研究.自动化学报, 2007, Vol. 33, No. 4: 378-384.
[61] Pettersen K.Y. and Nijmeijer H. Global practice stability and tracking for an underactuated ship: a combined averaging and backstepping approach. IFAC System Structure and Control, Jantes, 1998: 59-64.
[62] Pettersen K.Y. and Nijmeijer H. Semi-global practice stabilization and disturbance adaptation for an underactuated ship. Proceedings of the 39th IEEE Conference on Decision and Control, Sydney, Australia, 2000: 2144-2149.
[63] Husa K.E. and Fossen T.I. Backstepping designs for nonlinear way-point tracking of ships. Proceedings of the 4th IFAC Conference on Maneuvering and Control of Marine Craft, 1997.
[64]李铁山,杨盐生.基于耗散理论的不完全驱动船舶直线航迹控制设计.应用科学学报, 2005, Vol. 23, No. 2: 204-208.
[65] Li T.S., Yang Y.S. and Hong B.G. Adaptive robust dissipative designs on straight path control for underactuated ships. Journal of Systems Engineering and Electronics, 2006, Vol. 17, No. 1: 177-181.
[66]李铁山,杨盐生,洪碧光.船舶直线航迹控制的鲁棒自适应非线性设计,大连海事大学学报, 2004, Vol. 30, No. 4: 1-5.
[67]李铁山,杨盐生,杜嘉立,洪碧光.一类不确定非线性系统的鲁棒自适应跟踪控制.哈尔滨工程大学学报, 2005, Vol. 26, No. 2: 152-155.
[68] Li T.S., Yang Y.S., Hong B.G., Ren J.S. and Du J.L. A robust adaptive nonlinear control approach to ship straight-path tracking design. 2005 American Control Conference, 2005, Vol. 6: 4016-4021.
[69]李铁山,杨盐生,洪碧光,秦永祥.船舶航迹控制的鲁棒自适应模糊设计.控制理论与应用, 2007, Vol. 24, No. 3: 445-448.
[70] Aguiar A.P. and Hespanha J.P. Trajectory-tracking and path-following of underactuated autonomous vehicles with parametric modeling uncertainty. IEEE Transactions on Automatic Control, 2007, Vol. 52, No. 8: 1362-1379.
[71] Aguiar A.P. and Hespanha J.P. Logic-based switching control for trajectory-tracking and path-following of underactuated autonomous vehicles with parametric modeling uncertainty. Proceedings of the 2004 American Control Conference, Boston, Massachusetts, 2004: 3004-3010.
[72] Skjetne R. and Fossen T.I. Nonlinear maneuvering and control of ships. Proceedings of OCEANS 2001 MTS/IEEE Conference and Exhibition, 2001: 1808-1815.
[73] Encarnacao P., Pascoal A. and Arcak M. Path following for autonomous marine craft. Proceedings of 5th IFAC Conference on Manoeuvring and Control of Marine Craft, Aalborg, Denmark, 2000: 117-122.
[74] Encarnacao P. and Pascoal A. 3D path following for autonomous underwater vehicle. Proceedings of the 39th IEEE Conference on Decision and Control, Sydney, Australia, December 2000: 2977-2982.
[75] Lapierre L. and Soetanto D. Nonlinear path-following control of an AUV. Ocean Engineering, 2007, Vol. 34: 1734-1744.
[76] Do K.D., Jiang Z.P. and Pan J. Robust global stabilization of underactuated ships on a linear course. Proceedings of the American Control Conference, Anchorage, 2002: 304-309.
[77] Do K.D., Jiang Z.P. and Pan J. Robust adaptive path following of underactuated ships. Proceedings of the 41st IEEE Conference on Decision and Control, Las Vegas, Nevada USA, 2002: 3243-3248.
[78] Do K.D., Jiang Z.P. and Pan, J. Robust adaptive path following of underactuated ships. Automatica, 2004, Vol. 40: 929-944.
[79] Do K.D. and Pan J. Global robust adaptive path following of underactuated ships. Automatica, 2006, Vol. 42: 1713-1722.
[80] Do K.D. and Pan J. Robust path-following of underactuated ships: Theory and experiments on a model ship. Ocean Engineering, 2006, Vol. 33: 1354-1372.
[81] Do K.D., Jiang Z.P. and Pan J. Robust and adaptive path following for underactuated autonomous underwater vehicles. Ocean Engineering, 2004, Vol. 31: 1967-1997.
[82] Bibuli M., Caccia M. and Lapierre L. Path-following algorithms and experiments for an autonomous surface vehicle. Proc. of IFAC Conference on Control Applications in Marine Systems, 2007.
[83] Godhhavn J. M., Fossen T.I. and Berge S.P. Non-linear and adaptive backstepping designs for tracking control of ships. International Journal of Adaptive Control and Signal Processing, 1998, Vol. 12: 649-670.
[84] Pettersen K. Y. and Nijmeijer H. Tracking control of an underactuated surface vessel. Proceedings of the 37th IEEE Conference on Decision and Control, Tampa, Florida USA, 1998: 4561-4566.
[85] Pettersen K. Y. and Nijmeijer H. Underactuated ship tracking control: Theory and experiments. Int. J. Control, 2001, Vol. 74, No. 14: 1435-1446
[86] Aicardi M., Casalino G., Indiveri G., Aguiar A., Encarnacao P. and Pascoal A. A planar path following controller for underactuated marine vehicles. Proc. of MED’s 2001 9th IEEE Mediterranean Conference on Control and Automation, Dubrovnik, Croatia, 2001: 1-6.
[87]胡耀华,贾欣乐.船舶运动的预测控制.大连海事大学学报, 1998, Vol. 24, No. 1: 5-9.
[88]胡耀华,贾欣乐.广义预测控制应用于船舶航向和航迹保持.中国造船, 1998, No.1: 36-41.
[89]胡耀华,贾欣乐.具有约束条件的船舶运动预测控制.控制理论与应用, 2000, Vol. 17, No. 4: 542-547.
[90]贾欣乐,杨盐生.船舶运动数学模型-机理建模与辨识建模.大连:大连海事大学出版社, 1999.
[91]李殿璞.船舶运动与建模.哈尔滨:哈尔滨工程大学出版社, 2005.
[92] Fossen T.I. Guidance and Control of Ocean Vehicles. New York: Wiley, 1994.
[93]叶光.基于VV&A的船舶运动控制系统仿真的研究. [博士学位论文],大连:大连海事大学, 2007.
[94] K?llstr?m C.G. Identification and adaptive control applied to ship steering. Publication No. 93 of SSPA, Sweden, 1982.
[95] Abkowitz M.A. Measurement of hydrodynamics from ship manoeuvring trails by system identification. Trans. of SNAME, 1980, Vol. 88.
[96] Zhou W.W. and Blanke M. Identification of a class of non-linear state-space models using PRE techniques. Proceedings of 25th IEEE CDC. Greece, 1986.
[97] Abkowitz M.A. Lectures on ship hydrodynamics, steering and maneuverability. Hydro- and Aerodynamics Laboratory. Report No. Hy-5. Denmark, 1964.
[98] Norrbin N.H. Theory and observation on the use of a mathematical model for ship manoeuvring in deep and confined waters. Publication No. 68 of SSPA. Sweden, 1970.
[99] Inoue S. Hirano M. Kijima K. and Takashima J. A practical calculation method of ship manoeuvring motion. International Shipbuilding Progress, 1981, Vol. 28, No. 325.
[100] ?str?m K.J. and K?llstr?m C.G. Identification of ship steering dynamics. Automatica, 1976, Vol. 12, No. 9.
[101]席裕庚.预测控制.北京:国防工业出版社,1993.
[102]舒迪前.预测控制系统及其应用.北京:机械工业出版社, 1996.
[103]诸静.智能预测控制及其应用.杭州:浙江大学出版社, 2002.
[104]王伟.广义预测控制理论及其应用.北京:科学出版社, 1998.
[105] Rawling J.B. Tutorial overview of model predictive control. IEEE Control Systems Magazine, 2000, Vol. 20: 38-52.
[106]席裕庚,耿晓军,陈虹.预测控制性能研究的新进展,控制理论与应用, 2000, Vol. 17, No. 4: 469-475.
[107]陈虹,刘志远,解小华.非线性模型预测控制的现状与问题.控制与决策, 2001, Vol. 16, No. 4: 385-391.
[108] Richalet J., Rault A. and Papon J. Model predictive heuristic control: applications to industrial processes. Automatica, 1978, Vol. 14, No. 5:413-428.
[109] Rouhani R. and Mehra R.K. Model algorithmic control (MAC): basic theoretical properties. Automatica, 1982, Vol. 18, No.4: 401-414.
[110] Culter C.R. and Ramaker B.L. Dynamic matrix control-a computer control algorithm. Proceedings of the 1980 Joint Automatic Control Conference, San Francisco, 1980.
[111] Clarke D.W., Monhtadl C. and Tuffs P.S. Generalized predictive control. Automatica, 1987, Vol. 23, No. 2: 137-162.
[112] Garcia C.E. and Morari M. Internal model control: A unifying review and some new results. Industrial and Engineering Chemistry: Process Design and Development, 1982, Vol. 21, No. 2: 308-323.
[113] Brosilow C.B. and Joseph B. Inferential Control of Processes. AICHE Journal, 1978, Vol. 24, No. 3:485-509.
[114] Kwon W.H. and Byun D.G. Receding horizon tracking control as a predictive control and its stability properties. International Journal of Control, 1989, Vol. 50, No.5: 1807-1824.
[115] Kuntze H.B., Jacubasch A. Richalet J. and Arber C., On the predictive functional control of an elastic industrial robot. Proc. 25th CDC. Athens, Greece, 1986: 1877-1881.
[116]张智焕.复杂系统预测控制算法及其应用研究. [博士学位论文],浙江:浙江大学, 2002.
[117]张日东.非线性预测控制及应用研究. [博士学位论文],浙江:浙江大学, 2007.
[118]王元慧.模型预测控制在动力定位系统中的应用. [硕士学位论文],哈尔滨:哈尔滨工程大学, 2006.
[119]陈栋.基于模块化思想的CGPC控制软件的开发和应用. [硕士学位论文],北京:北京化工大学, 2008.
[120]魏环.连续时间广义预测控制及其实现方法研究. [博士学位论文],北京:北京化工大学, 2008.
[121] Chen, W.H., Balance D.J. and Gawthrop P.J., Optimal control of nonlinear systems: a predictive control approach. Automatica, 2003, Vol. 39: 633-641.
[122] Demircioglu H. and Gawthrop P.J. Continuous-time generalized predictive control. Automatica, 1991, Vol. 27, No.1: 55-74.
[123] Demircioglu H. and Karasu E. Generalized predictive control: a practical application and comparison of discrete and continuous time versions. IEEE Control Systems Magazine, 2000, Vol. 20, No. 5: 36-47.
[124] Demircioglu H. and Gawthrop P.J. Multivariable continuous-time generalized predictive control. Automatica, 1992, Vol. 28, No. 4: 697-713.
[125] Demircioglu H. and Clarke Q.W. CGPC with guaranteed stability properties. Control Theory and Applications, IEE Proceedings Part D, 1992, Vol. 139, No. 4: 371-380.
[126] Demircioglu H. and Yavuzyilmaz C. Constrained predictive control in continuous time. IEEE Control Systems Magazine, 2002, Vol. 22: 57-67.
[127] Gawthrop P.J., Demircioglu H. and Siller-Alcala I. Multivariable continuous -time generalized predictive control: a state-space approach to linear and nonlinear system. IEE Proc. Control Theory Appl., 1998, Vol. 145, No. 3: 241-250.
[128] Lu P. Optimal predictive control of continuous nonlinear systems. International Journal of Control, 1995, Vol. 62, No. 3: 633-649.
[129] Singh S.N., Steinberg M. and DiGirolamo R.D. Nonlinear predictive control of feedback linearizable systems and flight control system design. Journal of Guidance, Control, and Dynamics, 1995, Vol. 18, No. 5: 1023-1028.
[130] Soroush M. and Soroush H.M. Input-output linearizing nonlinear model predictive control. International Journal of Control, 1997, Vol. 68, No.6: 1449-1473.
[131]吕剑虹.连续时间域内具有比例积分作用的广义预测控制.控制理论与应用, 1993, Vol. 10, No. 4: 361-366.
[132]吕剑虹,陈来九.一种多变量连续时间预测控制方法.自动化学报, 1995, Vol. 21, No. 2: 203-208.
[133] Lv J.H., Xu Z.G. and Chen L.J. Continuous-time generalized predictive control with pole-placement. Journal of Southeast University, 1994, Vol. 10, No. 1:81-88.
[134] Kefi L., Chrifi L., Mahieddine S.M., Pinchon D. and Castelain J.M. Multivariable CGPC based internal model control: application to induction motor control. 2004 IEEE International Conference on Industrial Technology, Tunisia, Hammamet, 2004: 444-448.
[135]任强,陈增强,孙明玮,袁著祉.连续时间广义预测控制在纵向飞行控制中仿真.系统仿真学报,2006, Vol. 18, No. 2: 814-816.
[136] Wang Z.H., Chen Z.Q. and Yuan Z.H. Robustly stable control of continuous-time generalized predictive control combined with QFT based on GIMC structure. Acta Automatic Sinica, 2007, Vol. 33, No. 6: 649-651.
[137]杨平,翁思义.连续时间广义预测控制用于电厂锅炉汽压控制的仿真研究.中国电机工程学报, 1993, Vol. 13, No. 1: 50-55.
[138]杨平,翁思义.单整定参数的简化CGPC控制器.上海电力学院学报, 1994, Vol. 10, No. 2: 28-35.
[139]杨平.基于仿增量型的连续时间广义预测控制.上海电力学院学报,1994, Vol. 10, No. 2: 6-11.
[140]杨平,翁思义.一种前馈CGPC控制器.上海电力学院学报, 1994, Vol. 10, No. 4: 1-7.
[141]杨平,翁思义.状态估计器在前馈CGPC中的应用.上海电力学院学报, 1994, Vol. 10, No. 2: 36-41.
[142]周建锁,刘志远,裴润.机器人轨迹跟踪连续预测控制.系统工程与电子技术, 2000, Vol. 22, No. 11: 39-41.
[143]周建锁,叶青,刘志远,裴润.机器人非线性连续预测控制.哈尔滨工业大学学报, 2000, Vol. 32, No. 3: 108-114.
[144]周建锁,刘志远,裴润.一种非线性连续预测变结构控制方案.电机与控制学报, 2000, Vol. 4, No. 4: 227-229.
[145]周建锁,刘志远,裴润.机器人轨迹跟踪非线性预测变结构控制.机器人, 1999, Vol. 21: 76-80.
[146]周建锁,刘志远,裴润.约束非线性系统的滑模预测控制方法.控制与决策学报, 2001, Vol. 16, No. 2:207-210.
[147]周建锁,刘志远,裴润.带终端滑模约束的非线性模型预测控制方法.控制与决策, 2001, Vol. 16, No. 4:473-479.
[148]李明,陈宗海,张海涛,秦廷.连续时间广义预测控制算法的应用研究.测控技术, 2004, Vol. 23, No. 7: 61-63.
[149]刘晓华,刘芳.无穷时域的连续时间广义预测控制.控制与决策, 2008, Vol.23, No. 3: 337-340.
[150]刘晓华,韩春艳.连续时间不确定系统的鲁棒预测控制.系统工程与电子技术,2008, Vol. 30, No. 2: 312-315.
[151]刘芳,刘晓华,许国纯.带有模型不确定性的连续时间广义预测控制.鲁东大学学报, 2008, Vol. 24, No. 1: 22-26.
[152]刘芳.稳定的连续时间广义预测控制. [硕士学位论文],鲁东大学, 2007.
[153]刘浩,刘晓华,张喜英.非最小相位连续时间系统广义预测加权控制算法.鲁东大学学报, 2007, Vol. 23, No. 2: 114-116.
[154]刘福才,王娟,石淼,高秀伟.混沌系统的非线性连续预测变结构控制与同步.物理学报, 2002, Vol. 51, No.12: 2707-2712.
[155]刘兵,冯纯伯.基于双重准则的二自由度预测控制—连续情况.自动化学报, 1998, Vol. 24, No. 6: 721-726.
[156]刘兵,冯纯伯,徐刚.预测控制系统的LQG设计:连续情况.东南大学学报,1997, Vol. 27, No. 3: 118-120.
[157]刘兵,冯纯伯.多指标的连续预测控制系统设计.控制理论与应用, 1999, Vol. 16, No. 1: 117-119.
[158]刘兵,徐立鸿,冯纯伯.多变量连续预测控制的动态补偿.控制理论与应用, 1997, Vol. 14, No. 4: 570-573.
[159] Zhang L.Q. and Huang B. Robust model predictive control of singular systems. IEEE Trans. on Automat Contr., 2004, Vol. 49, No. 6:1000-1006.
[160]郭健.一类非线性系统广义预测控制. [博士学位论文],南京:南京理工大学, 2002.
[161] Kouvaritakis B., Cannon M. and Rossiter J.A. Recent developments in generalized predictive control for continuous-time systems. International Journal of Control, 1999, Vol.72, No. 2: 164-173.
[162] Deng M.C., Inoue A., Yanou A., et al. Continuous-time anti-windup generalized predictive control of non-minimum phase processes with input constraints. Proceedings of the 42nd IEEE Conference on Decision and Control, Manui, Hawaii, USA, 2003: 4457-4461.
[163]袁天保,刘新建,秦子增.混合加权连续预测控制研究.电机与控制学报, 2005, Vol. 9, No. 6: 593-599.
[164]袁天保,刘新建,秦子增.自旋弹道导弹连续预测控制研究.弹箭与制导学报, 2005, Vol. 25, No. 3: 474-477.
[165]陈栋,魏环,李全善,赵众,潘立登.模块化思想在CGPC控制软件中的应用.北京化工大学学报,2008, Vol. 35, No. 5: 94-97.
[166]潘立登,魏环.一类时滞多变量连续系统的广义预测控制.江苏大学学报, 2008, Vol. 29, No. 4: 326-329.
[167]席裕庚,耿晓军.连续非线性系统预测控制的次优性分析.自动化学报, 1999, Vol. 25, No. 5: 673-676.
[168] Chen W.H., Balance D.J. and O’Reilly J. Model predictive control of nonlinear systems: computational burden and stability. IEE Proceedings of Control Theory and Applications, 2000, Vol. 147, No. 4: 387-394.
[169] Chen W.H., Balance D.J. and Gawthrop P.J. Nonlinear generalized predictive control and optimal dynamic inversion control. Proceedings of 14th IFAC World Congress, Beijing, China, 1999: 415-420.
[170] Chen W.H. Analytic predictive controllers for nonlinear systems with ill-defined relative degree. IEE Proceedings of Control Theory and Applications, 2001, Vol. 148, No. 1: 9-16.
[171] Chen W.H., Balance D.J., Gawthrop P.J. Gribble J.J. and O’Reilly J. Nonlinear PID predictive controller. IEE Proceedings of Control Theory and Applications, 1999, Vol. 146, No.6: 603-611.
[172] Chen W.H. Predictive control of general nonlinear systems using approximation. IEE Proceedings of Control Theory and Applications, 2004, Vol. 151, No. 2: 137-144.
[173] Mrabet M., Fnaiech F. and Al-Haddad K. Nonlinear predictive adaptive controllers for nonlinear systems. IEEE ISIE 2004, Ajaccio, France, 2004: 453-458
[174] Mrabet M., Fnaiech F. and Al-Haddad K. Nonlinear predictive adaptive controllers for general nonlinear systems. ICTA 2005, Thessaloniki, Greece, 2005: 12-17.
[175] Mrabet M., Fnaiech F. and Al-Haddad K. Convergence analysis of a nonlinear predictive adaptive controllers. IEEE ISCCSP 2004, Hammamet, Tunisia, 2004: 123-126.
[176] Mrabet M. Adaptive predictive controllers for nonlinear systems with ill-defined relative degree. IEEE ICIT 2004, Hammamet Tunisia, 2004: 21-26.
[177] Zhuo X.S. and Zhou H.C. Boiler-turbine control system design using continuous-time nonlinear model predictive control. Journal of Chongqing University, 2008, Vol. 7, No. 2: 113-118.
[178] Dutka A.S., Ordys A.W. and Grimble A.J. Non-linear predictive control of 2 dof helicopter model. Proceedings of the 42nd IEEE Conference on Decision andControl, Maui, Hawaii USA, December 2003: 3954-3959.
[179] Said S.H. and M’Sahli F. Output feedback nonlinear GPC using high gain observer. 3rd International Symposium on Computational Intelligence and Intelligent Informatics, Agadir, Morocco, 2007: 125-129.
[180]张国银,杨智,谭洪舟.一类非线性系统切换解析模型预测控制方法.自动化学报, 2008, Vol. 34, No. 9: 1147-1156.
[181] Hauser J., Sastry S. and Kokotovic P. Nonlinear control via approximate input-output linearization: the ball and beam example. IEEE Transactions On Automatic Control, 1992, Vol. 37, No. 3: 392-398.
[182] Chen W.H. and Balance D.J. On a switch control scheme for nonlinear system with ill-defined relative degree. Systems and Control Letters, 2002, Vol. 47, No. 2: 159-166.
[183] Do K.D. and Pan J. Underactuated ship global tracking under relaxed conditions. IEEE Transactions on Automatic Control, 2002, Vol. 47, No. 9: 1529-1536.
[184]陈维桓.微分几何初步.北京大学出版社, 1990.
[185]刑喆.基于Unscented变换的Kalman滤波算法在非线性系统中的研究与仿真. [硕士学位论文],天津:天津大学, 2005.
[186]江宝安.基于UKF滤波的单目标跟踪算法研究. [硕士学位论文],长沙:国防科学技术大学, 2003.
[187]李涛.非线性滤波方法在导航系统中的应用研究. [博士学位论文],长沙:国防科学技术大学, 2003.
[188] Julier S.J. and Uhlmann J.K. A new extension of the Kalman filter to nonlinear systems. Proceedings of AeroSense: The 11st International Symposium on Aerospace/Defence Sensing, Simulation and Controls, 1997.
[189] Julier S.J., Uhlmann J.K. and Durrant-Whyte H.F. A new method for the nonlinear transformation of means and covariances in filters and estimator. IEEE Transactions on Automatic Control, 2000, Vol. 45, No. 3: 477-482.
[190] Julier S.J. and Uhlmann J.K. Reduced sigma point filters for the propagation of means and covariances through nonlinear transformations. Proceedings of the American Control Conference, Anchorage, 2002: 887-892.
[191] Julier S.J. The scaled unscented transformation. Proceedings of the American Control Conference, Anchorage, 2002: 4555-4559.
[192] Julier S.J. and Uhlmann J.K. Unscented filtering and nonlinear estimation. Proceedings of the IEEE, 2004, Vol. 92, No. 3: 401-422.
[193] Wan E.A. and Van der Merwe R. The unscented Kalman filter for nonlinearestimation. Proceedings of Symposium 2000 on Adaptive Systems for Signal Processing, Communications, and Control, 2000: 153-158.
[194] Van der Merwe R. and Wan E.A. The square-root unscented Kalman filter for state and parameter-estimation. IEEE International Conference on Acoustics, Speech, and Signal Processing ICASSP, 2001, Vol. 6: 3461–3464.
[195] Peng Y., Han J.D. and Song Q. Tracking control of underactuated surface ships: using unscented Kalman filter to estimate the uncertain parameters. Proceedings of the 2007 IEEE International Conference on Mechatronics and Automation, Harbin, China, 2007: 1884-1889.
[196] Peng Y., Zhou B. and Han J.D. Hardware design and UKF-based tracking control design of unmanned trimaran surface vehicle. Proceedings of the 2007 IEEE International Conference on Robotics and Biomimetics, Sanya, China, 2007: 1618-1623.
[197]宋崎,韩建达.基于UKF的移动机器人主动建模及模型自适应控制方法.机器人, Vol. 27, No. 3, 2005: 226-230.
[198]韩曾晋.自适应控制.北京:清华大学出版社, 1995.
[2]贾欣乐,张显库.船舶运动智能控制与H∞鲁棒控制.大连:大连海事大学出版社, 2002.
[3]张显库,贾欣乐.船舶运动控制.北京:国防工业出版社, 2006.
[4] Encarnacao P. and Pascoal A. Combined trajectory tracking and path following: an application to the coordinated control of autonomous marine craft. Proceedings of the 40th IEEE Conference on Decision and Control, Orlando, Florida USA, 2001: 964-969.
[5] Aguiar A.P., Dacic D.B, Hespanha J.P. and Kokotovic P. Path following or reference tracking? An answer relaxing the limits to performance. Proceedings of the 5th IFAC/EURON Symp. Intell. Auton Veh., Lisbon, Portugal, July 2004.
[6] Aguiar A.P., Hespanha J.P. and Kokotovic P. Path following for nonminimum phase systems removes performance limitations. IEEE Transactions on Automatic Control, 2005, Vol. 50, No. 2: 234-239.
[7] Do K.D. and Pan J. State- and output-feedback robust path-following controllers for underactuated ships using Serret-Frenet frame. Ocean Engineering, 2004, Vol. 31: 587-613.
[8] Encarnacao P., Pascoal A. and Arcak M. Path following control of marine vehicles. The 4th Portuguese Conference on Automatic Control: 31-36.
[9] Reyhanoglu M., Van der Schaft A., McClamroch N.H. and Kolmanovsky I. Dynamics and control of a class of underactuated mechanical systems. IEEE Transactions on Automatic Control, 1999, Vol. 44, No. 9: 1663-1671.
[10] Reyhanoglu M., Van der Schaft A., McClamroch N.H. and Kolmanovsky I. Nonlinear control of a class of underactuated systems. Proceedings of the 35th Conference on Decision and Control, Kobe, Japan, 1996: 1682-1687.
[11]韩冰.欠驱动船舶非线性控制研究. [博士学位论文],哈尔滨:哈尔滨工程大学, 2004.
[12]周祥龙,赵景波.欠驱动非线性控制方法综述.工业仪表与自动化装置, 2004, Vol. 5: 10-13.
[13] Reyhanoglu M. Exponential stability of an underactuated autonomous surface vessel. Automatica, 1997, Vol. 33, No. 12: 2249-2254.
[14] Wichlund K.Y., Sordalen O.J. and Egeland O. Control of vehicles with second-order nonholonomic constraints: underactuated vehicles. Proc. European Control Conf., Rome, Italy, 1995: 3086-3091.
[15] Pettersen K.Y. and Egeland O. Robust control of an underactuated surface vessel with thruster dynamics. Proceedings of the American Control Conference, New Mexico, June 1997: 3411-3415.
[16] Pettersen K.Y. and Egeland O. Exponential stabilization of an underactuated surface vessel. Proceedings of the 35th Conference on Decision and Control, Kobe, Japan, 1996: 967-972.
[17]李铁山.船舶直线航迹控制非线性设计方法. [博士学位论文],大连:大连海事大学, 2005.
[18] Holzhuter T. and Schultze S. Operating experience with a high-precision track controller for commercial ships. Control Eng. Practice, 1996, Vol. 4, No. 3: 343-350.
[19] Holzhuter T. LQG approach for the high-precision track control of ship. IEE Proceedings of Control Theory Application, 1997, Vol. 44, No. 2: 121-127.
[20] Cimen T. Development and validation of a mathematical model for control of constrained non-linear oil tanker motion. Mathematical and Computer Modeling of Dynamical Systems, 2008: 1-33.
[21] Cimen T. On-line nonlinear optimal maneuvering control of large tankers in restricted waterways. Proceedings of the 7th IFAC Conference on Maneuvering and Control of Marine Craft, Lisbon, Portugal, 2006.
[22] Sarioz K. and Narli E. Assessment of manoeuvring performance of large tankers in restricted waterways: a real time simulation approach. Ocean Engineering, 2003, Vol. 30: 1535-1551.
[23]彭秀艳,李小军,沈艳,赵希人.大型船舶航迹多变量随机最优控制.船舶工程, 2003, Vol. 25, No. 3: 41-45.
[24]于洋.卡尔曼滤波器在船舶航迹跟踪中的应用.江苏船舶, 2005, Vol. 22, No. 2: 31-39.
[25]张戎军,许汉珍.船舶操纵非线性系统的鲁棒变结构控制.控制与决策, 1994, Vol. 9, No. 5: 360-366.
[26] Zhang R.J., Chen Y.B., Sun Z.Q., Sun F.C. and Xu H.Z. Path control of a surface ship in restricted waters using sliding mode. Proceedings of the 37th IEEE Conference on Decision and Control, Tampa, Florida USA, 1998: 3195-3200.
[27] Zhang R.J., Chen Y.B., Sun Z.Q., Sun F.C. and Xu H.Z. Path control of a surfaceship in restricted waters using sliding mode. IEEE Transactions on Control Systems Technology, 2000, Vol. 8, No. 4: 722-732.
[28]匡红波,施小成,田亚杰,付明玉.全垫升气垫船航迹保持的变结构模糊PID控制.控制理论与应用, 2006, Vol. 25 No. 8: 4-6.
[29]马岭,崔维成. NTSM控制的AUV路径跟踪控制研究.中国造船, 2006, Vol. 47, No. 4: 76-82.
[30] Ma L. and Cui W.C. Path following control of a deep-sea manned submersible based upon NTSM. China Ocean Engineering. 2005, Vol. 19, No. 4: 625-636.
[31]马岭,崔维成.基于模糊混合控制的自治水下机器人路径跟踪控制.控制理论与应用, 2006, Vol. 23, No. 3: 341-346.
[32]卜仁祥.欠驱动水面船舶非线性反馈控制研究. [博士学位论文],大连:大连海事大学,2007.
[33]卜仁祥,刘正江,胡江强.基于动态非线性滑动模态的欠驱动船舶直线航迹控制.清华大学学报(自然科学版), 2007, Vol. 47, No. S2: 1880-1883.
[34] Burns R.S. The use of artificial neural networks for the intelligent optimal control of surface ships. IEEE Journal of Oceanic Engineering, 1995, Vol. 2, No. 1: 65-72.
[35] Zhang Y., Hearn G.E. and Sen P. A neural network approach to ship track-keeping control. IEEE Journal of Oceanic Engineering, 1996, Vol. 21, No.4: 513-527.
[36]程启明,刘其明,王志宏,孙干超.船舶航迹保持的在线SIMO神经网络控制器研究.工业仪表与自动化装置, 2000, No. 5: 3-7.
[37]程启明,万德钧,陈熙源.船舶航迹自动舵的神经网络控制方法研究.仪器仪表学报, 1999, Vol. 20, No. 4: 371-375.
[38]陈雪丽,程启明.船舶航迹保持的神经网络控制器研究,河海大学学报, 2001, Vol. 29, No. 5: 70-73.
[39]周岗,周永余,陈永冰.基于模糊理论的舰船航迹控制器.海军工程大学学报, 2000, No.3: 47-56.
[40]周岗,周永余,陈永冰,乔力争,卞鸿巍.舰船航迹控制器中模糊线性化方法的应用.中国惯性技术学报, 2000, Vol. 8, No. 4: 67-71.
[41] Yang Y.S., Zhou C.J. and Ren J.S. Model reference adaptive robust fuzzy control for ship steering autopilot with uncertain nonlinear systems. Applied Soft Computing, 2003, Vol. 3: 305–316.
[42] Yang Y.S. Direct robust adaptive fuzzy control (DRAFC) for uncertain nonlinear systems using small gain theorem. Fuzzy Sets and Systems, 2005, Vol. 151: 79-97.
[43] Velagic J., Vukic Z. and Omerdic E. Adaptive fuzzy ship autopilot for track-keeping. Control Engineering Practice, 2003, Vol. 11: 433-443.
[44] Fossen T.I., Breivik M. and Skjetne R. Line-of-Sight path following of underactuated marine craft. Proceedings of the 6th IFAC Conference on Maneuvering and Control of Marine Craft, Girona, Spain, 2003: 244-249.
[45] Breivik M. and Fossen T.I. Path following of straight lines and circles for marine surface vessels. Proceedings of the 6th IFAC CAMS, Ancona, Italy, 2004: 65-70.
[46] Breivik M. and Fossen T.I. Path following for marine surface vessels. Proceedings of the Oceans’04, Kobe, Japan, 2004: 2282-2289.
[47]孔祥军.船舶操纵运动模拟和智能技术的应用研究. [硕士学位论文],武汉:武汉理工大学, 2004.
[48] Fliess M., Levine J., Martin P. and Rouchon P. Flatness and defect of nonlinear systems: introductory theory and examples. International Journal of Control, 1995, Vol. 61, No. 6: 1327-1361.
[49] Ramirez H.S. and Ibanez C.A. On the control of the hovercraft system. Dynamics and Control, 2000, Vol. 10: 151-163.
[50] Ramirez H.S. and Ibanez C.A. The control of the hovercraft system: a flatness based approach. Proceedings of the 2000 IEEE International Conference on Control Applications, Anchorage, Alaska, USA, 2000: 692-697.
[51]韩冰,赵国良.基于微分平滑的欠驱动船舶航迹控制.哈尔滨工程大学学报,2004, Vol.25, No. 6: 709-713.
[52] Isidori A. Nonlinear control systems [3rd Edition]. New York: Springer-Verlag, 1995.
[53] Indiveri G., Aicardi M. and Casalino G. Nonlinear time-invariant feedback control of an underactuated marine vehicle along a straight course. Proceedings of the 5th IFAC Conference on Maneuvering and Control of Marine Craft, Aalborg, Denmark, 2000: 221-226.
[54] Pettersen K.Y. and Lefeber E. Way-point tracking control of ships. Proceedings of the 40th IEEE Conference on Decision and Control, Orlando, Florida USA, 2001: 940-945.
[55]李铁山,杨盐生,郑云峰.不完全驱动船舶非线性控制.交通运输工程学报, 2003, Vol. 3, No. 4: 39-43.
[56]李铁山,杨盐生,郑云峰.不完全驱动船舶航迹控制输入输出线性化设计.系统工程与电子技术, 2004, Vol. 26, No. 7: 945-948.
[57]周岗,姚琼荟,陈永冰,周永余,李文魁.船舶直线航迹控制系统全局渐近稳定的充分条件.海军工程大学学报, 2006, Vol. 18, No. 3: 52-56.
[58]周岗,姚琼荟,陈永冰,周永余.基于输入输出线性化的船舶全局直线航迹控制,控制理论与应用. 2007, Vol. 24 No. 1: 117-121.
[59]周岗,陈永冰,姚琼荟,周永余,李文魁.一类船舶直线航迹控制系统全局渐近稳定的充分条件及推论.自动化学报, 2007, Vol. 33, No. 11: 1204-1208.
[60]周岗,姚琼荟,陈永冰,周永余,李文魁.不完全驱动船舶直线控制稳定性研究.自动化学报, 2007, Vol. 33, No. 4: 378-384.
[61] Pettersen K.Y. and Nijmeijer H. Global practice stability and tracking for an underactuated ship: a combined averaging and backstepping approach. IFAC System Structure and Control, Jantes, 1998: 59-64.
[62] Pettersen K.Y. and Nijmeijer H. Semi-global practice stabilization and disturbance adaptation for an underactuated ship. Proceedings of the 39th IEEE Conference on Decision and Control, Sydney, Australia, 2000: 2144-2149.
[63] Husa K.E. and Fossen T.I. Backstepping designs for nonlinear way-point tracking of ships. Proceedings of the 4th IFAC Conference on Maneuvering and Control of Marine Craft, 1997.
[64]李铁山,杨盐生.基于耗散理论的不完全驱动船舶直线航迹控制设计.应用科学学报, 2005, Vol. 23, No. 2: 204-208.
[65] Li T.S., Yang Y.S. and Hong B.G. Adaptive robust dissipative designs on straight path control for underactuated ships. Journal of Systems Engineering and Electronics, 2006, Vol. 17, No. 1: 177-181.
[66]李铁山,杨盐生,洪碧光.船舶直线航迹控制的鲁棒自适应非线性设计,大连海事大学学报, 2004, Vol. 30, No. 4: 1-5.
[67]李铁山,杨盐生,杜嘉立,洪碧光.一类不确定非线性系统的鲁棒自适应跟踪控制.哈尔滨工程大学学报, 2005, Vol. 26, No. 2: 152-155.
[68] Li T.S., Yang Y.S., Hong B.G., Ren J.S. and Du J.L. A robust adaptive nonlinear control approach to ship straight-path tracking design. 2005 American Control Conference, 2005, Vol. 6: 4016-4021.
[69]李铁山,杨盐生,洪碧光,秦永祥.船舶航迹控制的鲁棒自适应模糊设计.控制理论与应用, 2007, Vol. 24, No. 3: 445-448.
[70] Aguiar A.P. and Hespanha J.P. Trajectory-tracking and path-following of underactuated autonomous vehicles with parametric modeling uncertainty. IEEE Transactions on Automatic Control, 2007, Vol. 52, No. 8: 1362-1379.
[71] Aguiar A.P. and Hespanha J.P. Logic-based switching control for trajectory-tracking and path-following of underactuated autonomous vehicles with parametric modeling uncertainty. Proceedings of the 2004 American Control Conference, Boston, Massachusetts, 2004: 3004-3010.
[72] Skjetne R. and Fossen T.I. Nonlinear maneuvering and control of ships. Proceedings of OCEANS 2001 MTS/IEEE Conference and Exhibition, 2001: 1808-1815.
[73] Encarnacao P., Pascoal A. and Arcak M. Path following for autonomous marine craft. Proceedings of 5th IFAC Conference on Manoeuvring and Control of Marine Craft, Aalborg, Denmark, 2000: 117-122.
[74] Encarnacao P. and Pascoal A. 3D path following for autonomous underwater vehicle. Proceedings of the 39th IEEE Conference on Decision and Control, Sydney, Australia, December 2000: 2977-2982.
[75] Lapierre L. and Soetanto D. Nonlinear path-following control of an AUV. Ocean Engineering, 2007, Vol. 34: 1734-1744.
[76] Do K.D., Jiang Z.P. and Pan J. Robust global stabilization of underactuated ships on a linear course. Proceedings of the American Control Conference, Anchorage, 2002: 304-309.
[77] Do K.D., Jiang Z.P. and Pan J. Robust adaptive path following of underactuated ships. Proceedings of the 41st IEEE Conference on Decision and Control, Las Vegas, Nevada USA, 2002: 3243-3248.
[78] Do K.D., Jiang Z.P. and Pan, J. Robust adaptive path following of underactuated ships. Automatica, 2004, Vol. 40: 929-944.
[79] Do K.D. and Pan J. Global robust adaptive path following of underactuated ships. Automatica, 2006, Vol. 42: 1713-1722.
[80] Do K.D. and Pan J. Robust path-following of underactuated ships: Theory and experiments on a model ship. Ocean Engineering, 2006, Vol. 33: 1354-1372.
[81] Do K.D., Jiang Z.P. and Pan J. Robust and adaptive path following for underactuated autonomous underwater vehicles. Ocean Engineering, 2004, Vol. 31: 1967-1997.
[82] Bibuli M., Caccia M. and Lapierre L. Path-following algorithms and experiments for an autonomous surface vehicle. Proc. of IFAC Conference on Control Applications in Marine Systems, 2007.
[83] Godhhavn J. M., Fossen T.I. and Berge S.P. Non-linear and adaptive backstepping designs for tracking control of ships. International Journal of Adaptive Control and Signal Processing, 1998, Vol. 12: 649-670.
[84] Pettersen K. Y. and Nijmeijer H. Tracking control of an underactuated surface vessel. Proceedings of the 37th IEEE Conference on Decision and Control, Tampa, Florida USA, 1998: 4561-4566.
[85] Pettersen K. Y. and Nijmeijer H. Underactuated ship tracking control: Theory and experiments. Int. J. Control, 2001, Vol. 74, No. 14: 1435-1446
[86] Aicardi M., Casalino G., Indiveri G., Aguiar A., Encarnacao P. and Pascoal A. A planar path following controller for underactuated marine vehicles. Proc. of MED’s 2001 9th IEEE Mediterranean Conference on Control and Automation, Dubrovnik, Croatia, 2001: 1-6.
[87]胡耀华,贾欣乐.船舶运动的预测控制.大连海事大学学报, 1998, Vol. 24, No. 1: 5-9.
[88]胡耀华,贾欣乐.广义预测控制应用于船舶航向和航迹保持.中国造船, 1998, No.1: 36-41.
[89]胡耀华,贾欣乐.具有约束条件的船舶运动预测控制.控制理论与应用, 2000, Vol. 17, No. 4: 542-547.
[90]贾欣乐,杨盐生.船舶运动数学模型-机理建模与辨识建模.大连:大连海事大学出版社, 1999.
[91]李殿璞.船舶运动与建模.哈尔滨:哈尔滨工程大学出版社, 2005.
[92] Fossen T.I. Guidance and Control of Ocean Vehicles. New York: Wiley, 1994.
[93]叶光.基于VV&A的船舶运动控制系统仿真的研究. [博士学位论文],大连:大连海事大学, 2007.
[94] K?llstr?m C.G. Identification and adaptive control applied to ship steering. Publication No. 93 of SSPA, Sweden, 1982.
[95] Abkowitz M.A. Measurement of hydrodynamics from ship manoeuvring trails by system identification. Trans. of SNAME, 1980, Vol. 88.
[96] Zhou W.W. and Blanke M. Identification of a class of non-linear state-space models using PRE techniques. Proceedings of 25th IEEE CDC. Greece, 1986.
[97] Abkowitz M.A. Lectures on ship hydrodynamics, steering and maneuverability. Hydro- and Aerodynamics Laboratory. Report No. Hy-5. Denmark, 1964.
[98] Norrbin N.H. Theory and observation on the use of a mathematical model for ship manoeuvring in deep and confined waters. Publication No. 68 of SSPA. Sweden, 1970.
[99] Inoue S. Hirano M. Kijima K. and Takashima J. A practical calculation method of ship manoeuvring motion. International Shipbuilding Progress, 1981, Vol. 28, No. 325.
[100] ?str?m K.J. and K?llstr?m C.G. Identification of ship steering dynamics. Automatica, 1976, Vol. 12, No. 9.
[101]席裕庚.预测控制.北京:国防工业出版社,1993.
[102]舒迪前.预测控制系统及其应用.北京:机械工业出版社, 1996.
[103]诸静.智能预测控制及其应用.杭州:浙江大学出版社, 2002.
[104]王伟.广义预测控制理论及其应用.北京:科学出版社, 1998.
[105] Rawling J.B. Tutorial overview of model predictive control. IEEE Control Systems Magazine, 2000, Vol. 20: 38-52.
[106]席裕庚,耿晓军,陈虹.预测控制性能研究的新进展,控制理论与应用, 2000, Vol. 17, No. 4: 469-475.
[107]陈虹,刘志远,解小华.非线性模型预测控制的现状与问题.控制与决策, 2001, Vol. 16, No. 4: 385-391.
[108] Richalet J., Rault A. and Papon J. Model predictive heuristic control: applications to industrial processes. Automatica, 1978, Vol. 14, No. 5:413-428.
[109] Rouhani R. and Mehra R.K. Model algorithmic control (MAC): basic theoretical properties. Automatica, 1982, Vol. 18, No.4: 401-414.
[110] Culter C.R. and Ramaker B.L. Dynamic matrix control-a computer control algorithm. Proceedings of the 1980 Joint Automatic Control Conference, San Francisco, 1980.
[111] Clarke D.W., Monhtadl C. and Tuffs P.S. Generalized predictive control. Automatica, 1987, Vol. 23, No. 2: 137-162.
[112] Garcia C.E. and Morari M. Internal model control: A unifying review and some new results. Industrial and Engineering Chemistry: Process Design and Development, 1982, Vol. 21, No. 2: 308-323.
[113] Brosilow C.B. and Joseph B. Inferential Control of Processes. AICHE Journal, 1978, Vol. 24, No. 3:485-509.
[114] Kwon W.H. and Byun D.G. Receding horizon tracking control as a predictive control and its stability properties. International Journal of Control, 1989, Vol. 50, No.5: 1807-1824.
[115] Kuntze H.B., Jacubasch A. Richalet J. and Arber C., On the predictive functional control of an elastic industrial robot. Proc. 25th CDC. Athens, Greece, 1986: 1877-1881.
[116]张智焕.复杂系统预测控制算法及其应用研究. [博士学位论文],浙江:浙江大学, 2002.
[117]张日东.非线性预测控制及应用研究. [博士学位论文],浙江:浙江大学, 2007.
[118]王元慧.模型预测控制在动力定位系统中的应用. [硕士学位论文],哈尔滨:哈尔滨工程大学, 2006.
[119]陈栋.基于模块化思想的CGPC控制软件的开发和应用. [硕士学位论文],北京:北京化工大学, 2008.
[120]魏环.连续时间广义预测控制及其实现方法研究. [博士学位论文],北京:北京化工大学, 2008.
[121] Chen, W.H., Balance D.J. and Gawthrop P.J., Optimal control of nonlinear systems: a predictive control approach. Automatica, 2003, Vol. 39: 633-641.
[122] Demircioglu H. and Gawthrop P.J. Continuous-time generalized predictive control. Automatica, 1991, Vol. 27, No.1: 55-74.
[123] Demircioglu H. and Karasu E. Generalized predictive control: a practical application and comparison of discrete and continuous time versions. IEEE Control Systems Magazine, 2000, Vol. 20, No. 5: 36-47.
[124] Demircioglu H. and Gawthrop P.J. Multivariable continuous-time generalized predictive control. Automatica, 1992, Vol. 28, No. 4: 697-713.
[125] Demircioglu H. and Clarke Q.W. CGPC with guaranteed stability properties. Control Theory and Applications, IEE Proceedings Part D, 1992, Vol. 139, No. 4: 371-380.
[126] Demircioglu H. and Yavuzyilmaz C. Constrained predictive control in continuous time. IEEE Control Systems Magazine, 2002, Vol. 22: 57-67.
[127] Gawthrop P.J., Demircioglu H. and Siller-Alcala I. Multivariable continuous -time generalized predictive control: a state-space approach to linear and nonlinear system. IEE Proc. Control Theory Appl., 1998, Vol. 145, No. 3: 241-250.
[128] Lu P. Optimal predictive control of continuous nonlinear systems. International Journal of Control, 1995, Vol. 62, No. 3: 633-649.
[129] Singh S.N., Steinberg M. and DiGirolamo R.D. Nonlinear predictive control of feedback linearizable systems and flight control system design. Journal of Guidance, Control, and Dynamics, 1995, Vol. 18, No. 5: 1023-1028.
[130] Soroush M. and Soroush H.M. Input-output linearizing nonlinear model predictive control. International Journal of Control, 1997, Vol. 68, No.6: 1449-1473.
[131]吕剑虹.连续时间域内具有比例积分作用的广义预测控制.控制理论与应用, 1993, Vol. 10, No. 4: 361-366.
[132]吕剑虹,陈来九.一种多变量连续时间预测控制方法.自动化学报, 1995, Vol. 21, No. 2: 203-208.
[133] Lv J.H., Xu Z.G. and Chen L.J. Continuous-time generalized predictive control with pole-placement. Journal of Southeast University, 1994, Vol. 10, No. 1:81-88.
[134] Kefi L., Chrifi L., Mahieddine S.M., Pinchon D. and Castelain J.M. Multivariable CGPC based internal model control: application to induction motor control. 2004 IEEE International Conference on Industrial Technology, Tunisia, Hammamet, 2004: 444-448.
[135]任强,陈增强,孙明玮,袁著祉.连续时间广义预测控制在纵向飞行控制中仿真.系统仿真学报,2006, Vol. 18, No. 2: 814-816.
[136] Wang Z.H., Chen Z.Q. and Yuan Z.H. Robustly stable control of continuous-time generalized predictive control combined with QFT based on GIMC structure. Acta Automatic Sinica, 2007, Vol. 33, No. 6: 649-651.
[137]杨平,翁思义.连续时间广义预测控制用于电厂锅炉汽压控制的仿真研究.中国电机工程学报, 1993, Vol. 13, No. 1: 50-55.
[138]杨平,翁思义.单整定参数的简化CGPC控制器.上海电力学院学报, 1994, Vol. 10, No. 2: 28-35.
[139]杨平.基于仿增量型的连续时间广义预测控制.上海电力学院学报,1994, Vol. 10, No. 2: 6-11.
[140]杨平,翁思义.一种前馈CGPC控制器.上海电力学院学报, 1994, Vol. 10, No. 4: 1-7.
[141]杨平,翁思义.状态估计器在前馈CGPC中的应用.上海电力学院学报, 1994, Vol. 10, No. 2: 36-41.
[142]周建锁,刘志远,裴润.机器人轨迹跟踪连续预测控制.系统工程与电子技术, 2000, Vol. 22, No. 11: 39-41.
[143]周建锁,叶青,刘志远,裴润.机器人非线性连续预测控制.哈尔滨工业大学学报, 2000, Vol. 32, No. 3: 108-114.
[144]周建锁,刘志远,裴润.一种非线性连续预测变结构控制方案.电机与控制学报, 2000, Vol. 4, No. 4: 227-229.
[145]周建锁,刘志远,裴润.机器人轨迹跟踪非线性预测变结构控制.机器人, 1999, Vol. 21: 76-80.
[146]周建锁,刘志远,裴润.约束非线性系统的滑模预测控制方法.控制与决策学报, 2001, Vol. 16, No. 2:207-210.
[147]周建锁,刘志远,裴润.带终端滑模约束的非线性模型预测控制方法.控制与决策, 2001, Vol. 16, No. 4:473-479.
[148]李明,陈宗海,张海涛,秦廷.连续时间广义预测控制算法的应用研究.测控技术, 2004, Vol. 23, No. 7: 61-63.
[149]刘晓华,刘芳.无穷时域的连续时间广义预测控制.控制与决策, 2008, Vol.23, No. 3: 337-340.
[150]刘晓华,韩春艳.连续时间不确定系统的鲁棒预测控制.系统工程与电子技术,2008, Vol. 30, No. 2: 312-315.
[151]刘芳,刘晓华,许国纯.带有模型不确定性的连续时间广义预测控制.鲁东大学学报, 2008, Vol. 24, No. 1: 22-26.
[152]刘芳.稳定的连续时间广义预测控制. [硕士学位论文],鲁东大学, 2007.
[153]刘浩,刘晓华,张喜英.非最小相位连续时间系统广义预测加权控制算法.鲁东大学学报, 2007, Vol. 23, No. 2: 114-116.
[154]刘福才,王娟,石淼,高秀伟.混沌系统的非线性连续预测变结构控制与同步.物理学报, 2002, Vol. 51, No.12: 2707-2712.
[155]刘兵,冯纯伯.基于双重准则的二自由度预测控制—连续情况.自动化学报, 1998, Vol. 24, No. 6: 721-726.
[156]刘兵,冯纯伯,徐刚.预测控制系统的LQG设计:连续情况.东南大学学报,1997, Vol. 27, No. 3: 118-120.
[157]刘兵,冯纯伯.多指标的连续预测控制系统设计.控制理论与应用, 1999, Vol. 16, No. 1: 117-119.
[158]刘兵,徐立鸿,冯纯伯.多变量连续预测控制的动态补偿.控制理论与应用, 1997, Vol. 14, No. 4: 570-573.
[159] Zhang L.Q. and Huang B. Robust model predictive control of singular systems. IEEE Trans. on Automat Contr., 2004, Vol. 49, No. 6:1000-1006.
[160]郭健.一类非线性系统广义预测控制. [博士学位论文],南京:南京理工大学, 2002.
[161] Kouvaritakis B., Cannon M. and Rossiter J.A. Recent developments in generalized predictive control for continuous-time systems. International Journal of Control, 1999, Vol.72, No. 2: 164-173.
[162] Deng M.C., Inoue A., Yanou A., et al. Continuous-time anti-windup generalized predictive control of non-minimum phase processes with input constraints. Proceedings of the 42nd IEEE Conference on Decision and Control, Manui, Hawaii, USA, 2003: 4457-4461.
[163]袁天保,刘新建,秦子增.混合加权连续预测控制研究.电机与控制学报, 2005, Vol. 9, No. 6: 593-599.
[164]袁天保,刘新建,秦子增.自旋弹道导弹连续预测控制研究.弹箭与制导学报, 2005, Vol. 25, No. 3: 474-477.
[165]陈栋,魏环,李全善,赵众,潘立登.模块化思想在CGPC控制软件中的应用.北京化工大学学报,2008, Vol. 35, No. 5: 94-97.
[166]潘立登,魏环.一类时滞多变量连续系统的广义预测控制.江苏大学学报, 2008, Vol. 29, No. 4: 326-329.
[167]席裕庚,耿晓军.连续非线性系统预测控制的次优性分析.自动化学报, 1999, Vol. 25, No. 5: 673-676.
[168] Chen W.H., Balance D.J. and O’Reilly J. Model predictive control of nonlinear systems: computational burden and stability. IEE Proceedings of Control Theory and Applications, 2000, Vol. 147, No. 4: 387-394.
[169] Chen W.H., Balance D.J. and Gawthrop P.J. Nonlinear generalized predictive control and optimal dynamic inversion control. Proceedings of 14th IFAC World Congress, Beijing, China, 1999: 415-420.
[170] Chen W.H. Analytic predictive controllers for nonlinear systems with ill-defined relative degree. IEE Proceedings of Control Theory and Applications, 2001, Vol. 148, No. 1: 9-16.
[171] Chen W.H., Balance D.J., Gawthrop P.J. Gribble J.J. and O’Reilly J. Nonlinear PID predictive controller. IEE Proceedings of Control Theory and Applications, 1999, Vol. 146, No.6: 603-611.
[172] Chen W.H. Predictive control of general nonlinear systems using approximation. IEE Proceedings of Control Theory and Applications, 2004, Vol. 151, No. 2: 137-144.
[173] Mrabet M., Fnaiech F. and Al-Haddad K. Nonlinear predictive adaptive controllers for nonlinear systems. IEEE ISIE 2004, Ajaccio, France, 2004: 453-458
[174] Mrabet M., Fnaiech F. and Al-Haddad K. Nonlinear predictive adaptive controllers for general nonlinear systems. ICTA 2005, Thessaloniki, Greece, 2005: 12-17.
[175] Mrabet M., Fnaiech F. and Al-Haddad K. Convergence analysis of a nonlinear predictive adaptive controllers. IEEE ISCCSP 2004, Hammamet, Tunisia, 2004: 123-126.
[176] Mrabet M. Adaptive predictive controllers for nonlinear systems with ill-defined relative degree. IEEE ICIT 2004, Hammamet Tunisia, 2004: 21-26.
[177] Zhuo X.S. and Zhou H.C. Boiler-turbine control system design using continuous-time nonlinear model predictive control. Journal of Chongqing University, 2008, Vol. 7, No. 2: 113-118.
[178] Dutka A.S., Ordys A.W. and Grimble A.J. Non-linear predictive control of 2 dof helicopter model. Proceedings of the 42nd IEEE Conference on Decision andControl, Maui, Hawaii USA, December 2003: 3954-3959.
[179] Said S.H. and M’Sahli F. Output feedback nonlinear GPC using high gain observer. 3rd International Symposium on Computational Intelligence and Intelligent Informatics, Agadir, Morocco, 2007: 125-129.
[180]张国银,杨智,谭洪舟.一类非线性系统切换解析模型预测控制方法.自动化学报, 2008, Vol. 34, No. 9: 1147-1156.
[181] Hauser J., Sastry S. and Kokotovic P. Nonlinear control via approximate input-output linearization: the ball and beam example. IEEE Transactions On Automatic Control, 1992, Vol. 37, No. 3: 392-398.
[182] Chen W.H. and Balance D.J. On a switch control scheme for nonlinear system with ill-defined relative degree. Systems and Control Letters, 2002, Vol. 47, No. 2: 159-166.
[183] Do K.D. and Pan J. Underactuated ship global tracking under relaxed conditions. IEEE Transactions on Automatic Control, 2002, Vol. 47, No. 9: 1529-1536.
[184]陈维桓.微分几何初步.北京大学出版社, 1990.
[185]刑喆.基于Unscented变换的Kalman滤波算法在非线性系统中的研究与仿真. [硕士学位论文],天津:天津大学, 2005.
[186]江宝安.基于UKF滤波的单目标跟踪算法研究. [硕士学位论文],长沙:国防科学技术大学, 2003.
[187]李涛.非线性滤波方法在导航系统中的应用研究. [博士学位论文],长沙:国防科学技术大学, 2003.
[188] Julier S.J. and Uhlmann J.K. A new extension of the Kalman filter to nonlinear systems. Proceedings of AeroSense: The 11st International Symposium on Aerospace/Defence Sensing, Simulation and Controls, 1997.
[189] Julier S.J., Uhlmann J.K. and Durrant-Whyte H.F. A new method for the nonlinear transformation of means and covariances in filters and estimator. IEEE Transactions on Automatic Control, 2000, Vol. 45, No. 3: 477-482.
[190] Julier S.J. and Uhlmann J.K. Reduced sigma point filters for the propagation of means and covariances through nonlinear transformations. Proceedings of the American Control Conference, Anchorage, 2002: 887-892.
[191] Julier S.J. The scaled unscented transformation. Proceedings of the American Control Conference, Anchorage, 2002: 4555-4559.
[192] Julier S.J. and Uhlmann J.K. Unscented filtering and nonlinear estimation. Proceedings of the IEEE, 2004, Vol. 92, No. 3: 401-422.
[193] Wan E.A. and Van der Merwe R. The unscented Kalman filter for nonlinearestimation. Proceedings of Symposium 2000 on Adaptive Systems for Signal Processing, Communications, and Control, 2000: 153-158.
[194] Van der Merwe R. and Wan E.A. The square-root unscented Kalman filter for state and parameter-estimation. IEEE International Conference on Acoustics, Speech, and Signal Processing ICASSP, 2001, Vol. 6: 3461–3464.
[195] Peng Y., Han J.D. and Song Q. Tracking control of underactuated surface ships: using unscented Kalman filter to estimate the uncertain parameters. Proceedings of the 2007 IEEE International Conference on Mechatronics and Automation, Harbin, China, 2007: 1884-1889.
[196] Peng Y., Zhou B. and Han J.D. Hardware design and UKF-based tracking control design of unmanned trimaran surface vehicle. Proceedings of the 2007 IEEE International Conference on Robotics and Biomimetics, Sanya, China, 2007: 1618-1623.
[197]宋崎,韩建达.基于UKF的移动机器人主动建模及模型自适应控制方法.机器人, Vol. 27, No. 3, 2005: 226-230.
[198]韩曾晋.自适应控制.北京:清华大学出版社, 1995.