大型冷凝器清洗机器人的智能鲁棒控制方法研究
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摘要
多关节机械手和移动机器人的控制问题一直是控制科学工作者普遍关注的研究领域,同时也取得了许多理论成果,然而大部分工作都集中在传统的机械手或移动机器人的控制上。移动机械手结合了机械手和移动机器人两者的优点,同时具有移动和操作的功能,这种特点使得它优于传统的移动机器人和机械手,不仅具有机械手的操作灵活性,而且具有移动机器人工作空间的广阔性,具有几乎无限大的工作空间。基于移动机械手这种特点,本项目组研究了一种基于移动机械手的智能自动化清洗系统对大型冷凝器进行长期自主在线清洗,合理、高效地实现冷凝器污垢的在线清洗,改善冷凝器传热效果,提高汽轮机组的运行效率。本文重点开展用于大型冷凝器自动化清洗的移动机械手的智能鲁棒控制理论方法研究。全文主要工作包括如下几个方面。
论文首先系统深入地介绍了大型冷凝器的工作原理和主要的清洗方法以及存在的不足;然后阐述了移动机械手的研究进展和机器人鲁棒控制的研究现状。并在此基础上阐述本论文的研究意义。
本文所设计和研究的大型冷凝器自动清洗机器人是一类移动机械手系统,由于该移动机械手是由一个具有非完整性的移动机器人系统和完整约束的机械手组成,因此,论文第二章首先详细地介绍了非完整系统的运动学和动力学建模与特性、多关节机械手的运动学和动力学问题,然后针对移动机械手的整体控制策略和分散控制策略分别建立了相应的系统运动学和动力学模型。
在移动机械手的整体控制策略方面,由于滑模控制方法是一种针对不确定系统的有效非线性反馈控制方法,论文第三章首先根据移动机械手系统的不确定性和外界干扰的有界性设计了滑模控制器,该控制器优点在于滑模控制不需要被控对象精确的数学模型,而只要知道模型中参数的误差范围或变化范围;而且,滑模控制对有界干扰和参数变化具有不敏感性,可以削弱由于负载变化或干扰对系统控制性能的影响。然而,当不确定性和外界干扰的界未知时,滑模控制存在本身固有抖振现象,使得控制器不能得到很好地应用。针对这种情况,论文利用神经网络的非线性逼近能力来辨识移动机械手系统的不确定性和干扰,提出了一种基于神经网络的移动机械手滑模控制,使滑模控制器的抖振大大减少,并利用Lyapunov定理设计了具有神经网络补偿的滑模控制器结构和神经网络的学习算法,从而保证了系统的稳定性、改善了系统的动态性能,实验结果表明基于神经网络的滑模控制方法能够有效地削弱抖振现象,具有很强的抗干扰能力和很好的动态特性。
论文第四章首先针对CMAC中存在的问题,将模糊理论引入CMAC,在划分输入空间和激活联想强度时采用了模糊化的方法,提出了一种比传统CMAC更好的逼近能力的模糊CMAC神经网络模型。利用模糊CMAC神经网络的逼近能力对移动机械手进行建模,并在此基础上,提出了一种具有自适应能力的H_∞控制策略,通过H_∞控制策略减少了外扰和模糊CMAC神经网络重构误差对系统的影响。理论分析证明了该控制器能够将移动机械手系统的外扰影响控制在指定的范围内,且闭环系统的所有信号都是有界的。在仿真实验中,为了验证基于模糊CMAC神经网络的H_∞控制策略的有效性,将其与计算力矩控制方法进行比较,仿真结果表明,在存在外扰的情况下,所提出的控制策略具有比计算力矩控制方法更好的鲁棒性能。
在移动机械手的分散控制策略方面,论文第五章将移动机械臂分成两个子系统,即非完整约束的移动平台子系统和完整约束的机械臂子系统,然后考虑了移动平台的运动学控制器,对两个子系统分别设计Lyapunov函数,将两个子系统之间的耦合看成干扰,之后针对机器人的部分参数未知和全部参数未知分别给出了相应的鲁棒自适应控制器设计,并分别根据Lyapunov稳定性理论证明了整个移动机械手系统的稳定性,且跟踪误差和自适应系数矩阵误差一致终值有界,保证了系统的稳定性、改善了系统的动态性能。实验结果表明,所设计的鲁棒自适应控制器是有效的,且具有较强的鲁棒性和自适应能力。
基于体积、成本等方面的考虑,机器人系统通常不配备速度测量装置,仅通过位置反馈获取速度信息,论文第六章提出了一种模糊自适应非线性鲁棒观测器估计关节速度的方法,利用模糊逻辑来逼近系统参数的不确定性,引入鲁棒项抑制外扰和模糊逻辑逼近过程中的重构误差,采用严格正实Lyapunov设计方法分析观测误差是一致最终有界的;然后在鲁棒观测器的基础上,设计了机器人的模糊自适应输出反馈控制器,模糊系统参数基于Lyapunov稳定性理论自适应调整,整个控制器保证了具有不确定性的机器人系统渐近的跟踪轨迹,且闭环系统的所有信号均有界。仿真实验结果表明了该方法的有效性。
针对冷凝器在线高效清洗要求,论文第七章提出了一种冷凝器在线清洗的新方案,根据冷凝器的水室结构及管束布局,将高压水射流清洗与化学清洗相结合并通过两关节机械臂清洗喷枪来实现清洗。并介绍了清洗系统结构,详细分析了适用于不同发电机组的自动化清洗机器人的机械结构及其控制系统结构。论文最后总结了全文的主要创新性研究成果,并对下一步研究工作进行了展望。
The control problem of multi-joint manipulator and mobile robot has been paid a great attention for a long time, and many theoretical results have been reported. However, most of research works are focused on the control issues of the traditional manipulator or mobile robot systems. A mobile manipulator is a robotic manipulator mounted on a moving base, with the function of mobile and operation, which makes it superior to the traditional characteristics of mobile robots and manipulators. It not only has the manipulator operational flexibility, but also increases the size of the robot workspace, with almost infinite work space. For the purpose of improving the heat transfer performance of condenser and increasing the efficiency of thermal cycle of turbo-generator unit, we investigate intelligent automated cleaning system for large condenser based on a mobile manipulator. This dissertation focuses on the methods of intelligent robust control for cleaning mobile manipulator of large condenser. Main results and contributions of this dissertation are as follows:
Firstly, the principle of the large condenser, the cleaning method and its shortcomings are introduced. Then, the research progress of the mobile manipulator and robust control of robot are generalized. And, the research significance of this dissertation is presented.
In this dissertation, design and research of the large condenser cleaning robot is a kind of mobile manipulator system, and the mobile manipulator is composed of a nonholonomic mobile robot and a honolomic manipulator. In chapter 2, the kinematics and dynamics model and its characteristics of a nonholonomic system and a multi-joint manipulator are introduced respectively. Then, aiming at the centralized control strategy and decentralized control strategy of mobile manipulator, the corresponding kinematics and dynamics model are founded.
In the centralized control strategy, as sliding mode control is an effective nonlinear feedback control method of uncertain systems, in chapter 3, a sliding controller is designed based on the bound of parameter uncertainties and external disturbances of mobile manipulator system. The advantages of this controller are sliding control does not need precise mathematical model of the controlled object, and that as long as know the change scope of the error of parameters. The control rules of sliding mode control are to change the switching motion constantly, make the state trajectory of the system arrive the sliding mode. In theory, when the state trajectory of the system reaches and slips on the surface, equivalent control can make the state trajectory slide to the sliding surface and keep on it. However, when the uncertainties and external disturbances are unknown, there inherent chattering phenomenon of sliding control can not be made good use of. The existence of chattering may cause instability of the system, and restrain the application of the sliding mode control in a certain extent. For counteracting the defects of sliding mode control, a sliding mode control system for mobile manipulator based on neural network is designed. The neural network is used to compensate the uncertainties and external disturbances. Based on Lyapunov theorem, the structure of sliding mode controller and the learning algorithm of the neural network are designed. So the stability of the system is guaranteed, and the dynamic performance of the system is improved. The simulation results show that the sliding mode control method based on neural network can weaken chattering phenomenon effectively, and has an excellent dynamic characteristics.
In Chapter 4, aim at the existing problems in cerabellar model articulation controller (CMAC), by fuzzifying the space division method of cerabellar model articulation controller, a fuzzy CMAC neural network is proposed. Then, using fuzzy CMAC neural network approaches the model of mobile manipulator, and on this basis, an H_∞controller with adaptive mechanism is proposed. The effects of external disturbances and the reconstruction error of fuzzy CMAC neural network are reduced by using the H_∞control strategy. Theoretical analysis shows that the controller can restrict the effects in a designated area, and all signals in closed-loop system are bounded. Finally, in order to validate the effectiveness of the H_∞control strategy based on fuzzy CMAC neural network, a set of experiences which is compared with computed torque control method is drawn. The simulation results show that the proposed control strategy is better than computed torque control method under external disturbances, has better robust performance.
In decentralized control strategy, mobile manipulator is divided into two subsystems in Chapter 5, that is, nonholonomic mobile platform subsystems and holonomic manipulator subsystem. Then, considering the kinematics controller of the mobile platform, Lyapunov function of the two subsystems are designed. The couple between the two subsystems is regarded as disturbances, and aim at some unknown parameters and all unknown parameters, corresponding robust adaptive controller are given respectively. According to the Lyapunov stability theory, the overall mobile manipulator system is stable, and the tracking error and adaptive coefficient error is uniformly ultimately bounded (UUB). The stability of the system is ensured and the dynamic performance is improved. Finally, simulation results show that the robust adaptive controller is effective, and has good robust and adaptive capacity.
Considering the size, cost and so on, robot system is usually not equipped with speed measuring devices, and obtains speed information only through position feedback. In Chapter 6, a fuzzy adaptive nonlinear robust observer is proposed. The parameters of system uncertainties are approximated by fuzzy logic, and the robust term restrains external disturbances and reconstruction error of fuzzy logic. The observing errors are proven to be UUB by strictly positive real Lyapunov design. Then, on the basis of the designed observer, a fuzzy adaptive output feedback controller is designed. Parameters of fuzzy system are tuned adaptively based on Lyapunov stability theory, the controller can guarantee uncertainty robot tracking desired trajectory stability, and all signals in the closed-loop system are bounded. Finally simulation results show that the method is effective.
Aim at the requirements of condenser online efficient cleaning, a new online cleaning project for condenser is presented in chapter 7. According to the condenser structure and layout, high pressure water jet cleaning and chemical cleaning are combined, and cleaning project is realized by two-joint robot arm. The cleaning system structure is introduced, and the architectures of three generations of cleaning robot are analyzed in detail.
Finally, the main innovations of the thesis are summarized, and the fields for further research are expected.
论文首先系统深入地介绍了大型冷凝器的工作原理和主要的清洗方法以及存在的不足;然后阐述了移动机械手的研究进展和机器人鲁棒控制的研究现状。并在此基础上阐述本论文的研究意义。
本文所设计和研究的大型冷凝器自动清洗机器人是一类移动机械手系统,由于该移动机械手是由一个具有非完整性的移动机器人系统和完整约束的机械手组成,因此,论文第二章首先详细地介绍了非完整系统的运动学和动力学建模与特性、多关节机械手的运动学和动力学问题,然后针对移动机械手的整体控制策略和分散控制策略分别建立了相应的系统运动学和动力学模型。
在移动机械手的整体控制策略方面,由于滑模控制方法是一种针对不确定系统的有效非线性反馈控制方法,论文第三章首先根据移动机械手系统的不确定性和外界干扰的有界性设计了滑模控制器,该控制器优点在于滑模控制不需要被控对象精确的数学模型,而只要知道模型中参数的误差范围或变化范围;而且,滑模控制对有界干扰和参数变化具有不敏感性,可以削弱由于负载变化或干扰对系统控制性能的影响。然而,当不确定性和外界干扰的界未知时,滑模控制存在本身固有抖振现象,使得控制器不能得到很好地应用。针对这种情况,论文利用神经网络的非线性逼近能力来辨识移动机械手系统的不确定性和干扰,提出了一种基于神经网络的移动机械手滑模控制,使滑模控制器的抖振大大减少,并利用Lyapunov定理设计了具有神经网络补偿的滑模控制器结构和神经网络的学习算法,从而保证了系统的稳定性、改善了系统的动态性能,实验结果表明基于神经网络的滑模控制方法能够有效地削弱抖振现象,具有很强的抗干扰能力和很好的动态特性。
论文第四章首先针对CMAC中存在的问题,将模糊理论引入CMAC,在划分输入空间和激活联想强度时采用了模糊化的方法,提出了一种比传统CMAC更好的逼近能力的模糊CMAC神经网络模型。利用模糊CMAC神经网络的逼近能力对移动机械手进行建模,并在此基础上,提出了一种具有自适应能力的H_∞控制策略,通过H_∞控制策略减少了外扰和模糊CMAC神经网络重构误差对系统的影响。理论分析证明了该控制器能够将移动机械手系统的外扰影响控制在指定的范围内,且闭环系统的所有信号都是有界的。在仿真实验中,为了验证基于模糊CMAC神经网络的H_∞控制策略的有效性,将其与计算力矩控制方法进行比较,仿真结果表明,在存在外扰的情况下,所提出的控制策略具有比计算力矩控制方法更好的鲁棒性能。
在移动机械手的分散控制策略方面,论文第五章将移动机械臂分成两个子系统,即非完整约束的移动平台子系统和完整约束的机械臂子系统,然后考虑了移动平台的运动学控制器,对两个子系统分别设计Lyapunov函数,将两个子系统之间的耦合看成干扰,之后针对机器人的部分参数未知和全部参数未知分别给出了相应的鲁棒自适应控制器设计,并分别根据Lyapunov稳定性理论证明了整个移动机械手系统的稳定性,且跟踪误差和自适应系数矩阵误差一致终值有界,保证了系统的稳定性、改善了系统的动态性能。实验结果表明,所设计的鲁棒自适应控制器是有效的,且具有较强的鲁棒性和自适应能力。
基于体积、成本等方面的考虑,机器人系统通常不配备速度测量装置,仅通过位置反馈获取速度信息,论文第六章提出了一种模糊自适应非线性鲁棒观测器估计关节速度的方法,利用模糊逻辑来逼近系统参数的不确定性,引入鲁棒项抑制外扰和模糊逻辑逼近过程中的重构误差,采用严格正实Lyapunov设计方法分析观测误差是一致最终有界的;然后在鲁棒观测器的基础上,设计了机器人的模糊自适应输出反馈控制器,模糊系统参数基于Lyapunov稳定性理论自适应调整,整个控制器保证了具有不确定性的机器人系统渐近的跟踪轨迹,且闭环系统的所有信号均有界。仿真实验结果表明了该方法的有效性。
针对冷凝器在线高效清洗要求,论文第七章提出了一种冷凝器在线清洗的新方案,根据冷凝器的水室结构及管束布局,将高压水射流清洗与化学清洗相结合并通过两关节机械臂清洗喷枪来实现清洗。并介绍了清洗系统结构,详细分析了适用于不同发电机组的自动化清洗机器人的机械结构及其控制系统结构。论文最后总结了全文的主要创新性研究成果,并对下一步研究工作进行了展望。
The control problem of multi-joint manipulator and mobile robot has been paid a great attention for a long time, and many theoretical results have been reported. However, most of research works are focused on the control issues of the traditional manipulator or mobile robot systems. A mobile manipulator is a robotic manipulator mounted on a moving base, with the function of mobile and operation, which makes it superior to the traditional characteristics of mobile robots and manipulators. It not only has the manipulator operational flexibility, but also increases the size of the robot workspace, with almost infinite work space. For the purpose of improving the heat transfer performance of condenser and increasing the efficiency of thermal cycle of turbo-generator unit, we investigate intelligent automated cleaning system for large condenser based on a mobile manipulator. This dissertation focuses on the methods of intelligent robust control for cleaning mobile manipulator of large condenser. Main results and contributions of this dissertation are as follows:
Firstly, the principle of the large condenser, the cleaning method and its shortcomings are introduced. Then, the research progress of the mobile manipulator and robust control of robot are generalized. And, the research significance of this dissertation is presented.
In this dissertation, design and research of the large condenser cleaning robot is a kind of mobile manipulator system, and the mobile manipulator is composed of a nonholonomic mobile robot and a honolomic manipulator. In chapter 2, the kinematics and dynamics model and its characteristics of a nonholonomic system and a multi-joint manipulator are introduced respectively. Then, aiming at the centralized control strategy and decentralized control strategy of mobile manipulator, the corresponding kinematics and dynamics model are founded.
In the centralized control strategy, as sliding mode control is an effective nonlinear feedback control method of uncertain systems, in chapter 3, a sliding controller is designed based on the bound of parameter uncertainties and external disturbances of mobile manipulator system. The advantages of this controller are sliding control does not need precise mathematical model of the controlled object, and that as long as know the change scope of the error of parameters. The control rules of sliding mode control are to change the switching motion constantly, make the state trajectory of the system arrive the sliding mode. In theory, when the state trajectory of the system reaches and slips on the surface, equivalent control can make the state trajectory slide to the sliding surface and keep on it. However, when the uncertainties and external disturbances are unknown, there inherent chattering phenomenon of sliding control can not be made good use of. The existence of chattering may cause instability of the system, and restrain the application of the sliding mode control in a certain extent. For counteracting the defects of sliding mode control, a sliding mode control system for mobile manipulator based on neural network is designed. The neural network is used to compensate the uncertainties and external disturbances. Based on Lyapunov theorem, the structure of sliding mode controller and the learning algorithm of the neural network are designed. So the stability of the system is guaranteed, and the dynamic performance of the system is improved. The simulation results show that the sliding mode control method based on neural network can weaken chattering phenomenon effectively, and has an excellent dynamic characteristics.
In Chapter 4, aim at the existing problems in cerabellar model articulation controller (CMAC), by fuzzifying the space division method of cerabellar model articulation controller, a fuzzy CMAC neural network is proposed. Then, using fuzzy CMAC neural network approaches the model of mobile manipulator, and on this basis, an H_∞controller with adaptive mechanism is proposed. The effects of external disturbances and the reconstruction error of fuzzy CMAC neural network are reduced by using the H_∞control strategy. Theoretical analysis shows that the controller can restrict the effects in a designated area, and all signals in closed-loop system are bounded. Finally, in order to validate the effectiveness of the H_∞control strategy based on fuzzy CMAC neural network, a set of experiences which is compared with computed torque control method is drawn. The simulation results show that the proposed control strategy is better than computed torque control method under external disturbances, has better robust performance.
In decentralized control strategy, mobile manipulator is divided into two subsystems in Chapter 5, that is, nonholonomic mobile platform subsystems and holonomic manipulator subsystem. Then, considering the kinematics controller of the mobile platform, Lyapunov function of the two subsystems are designed. The couple between the two subsystems is regarded as disturbances, and aim at some unknown parameters and all unknown parameters, corresponding robust adaptive controller are given respectively. According to the Lyapunov stability theory, the overall mobile manipulator system is stable, and the tracking error and adaptive coefficient error is uniformly ultimately bounded (UUB). The stability of the system is ensured and the dynamic performance is improved. Finally, simulation results show that the robust adaptive controller is effective, and has good robust and adaptive capacity.
Considering the size, cost and so on, robot system is usually not equipped with speed measuring devices, and obtains speed information only through position feedback. In Chapter 6, a fuzzy adaptive nonlinear robust observer is proposed. The parameters of system uncertainties are approximated by fuzzy logic, and the robust term restrains external disturbances and reconstruction error of fuzzy logic. The observing errors are proven to be UUB by strictly positive real Lyapunov design. Then, on the basis of the designed observer, a fuzzy adaptive output feedback controller is designed. Parameters of fuzzy system are tuned adaptively based on Lyapunov stability theory, the controller can guarantee uncertainty robot tracking desired trajectory stability, and all signals in the closed-loop system are bounded. Finally simulation results show that the method is effective.
Aim at the requirements of condenser online efficient cleaning, a new online cleaning project for condenser is presented in chapter 7. According to the condenser structure and layout, high pressure water jet cleaning and chemical cleaning are combined, and cleaning project is realized by two-joint robot arm. The cleaning system structure is introduced, and the architectures of three generations of cleaning robot are analyzed in detail.
Finally, the main innovations of the thesis are summarized, and the fields for further research are expected.
引文
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