网络控制系统的智能调度及其优化
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摘要
通过资源约束网络实现地域上分布的传感器、执行器及控制器之间的信息交互从而实现被控对象的实时反馈控制的这样一类网络控制系统近来年得到了广泛的关注。通信网络介入传统点对点的控制系统使得这类网络控制系统的分析和设计变得更为复杂。选择合适的控制方法或者采用合适的资源调度策略均能补偿或改善网络的介入对系统性能的影响。
本文从资源调度的角度出发,建立了集成调度与控制协同设计的模型,提出了网络控制系统的稳定性条件和控制器的协同设计方法;对网络的优先级调度提出了智能调度算法;对网络资源和控制性能的优化提出了不同的解决方案。主要研究内容如下:
对于通信约束的网络控制系统,应用通信序列的概念和混合逻辑动态的构架,将其建模成一类集成控制和调度的资源约束离散周期系统。给出了这类系统网络信息的可调度条件和闭环系统渐近稳定的条件,同时也给出了在网络信息的周期调度下控制器的参数化设计方法。
对于一类集成控制和调度的资源约束系统模型,采用线性矩阵不等式来描述离散周期系统的周期通信序列和H_∞控制协同设计的优化问题。提出了一种递增优化序列算法来求解优化的调度策略及其相应渐近稳定及r指数稳定的H_∞控制器。该算法结合线性矩阵不等式凸优化解决了调度与控制的协同设计问题,同时也节省了搜索优化解的计算时间。
在网络带宽受限的情况下,综合考虑了系统响应的误差和误差变化率,设计了一个共享通信网络的模糊反馈调度器。该调度器采用模糊最大优先调度算法对网络消息发送的优先级进行动态调度。对四种不同调度算法在不同随机时延序列下进行了比较,结果表明这种智能调度算法优化了系统的控制性能,并在不确定运行环境中具有更好的适应性。
为了优化控制性能与网络带宽的折衷关系,设计了一个基于模糊逻辑的带宽管理器对控制系统的带宽进行动态管理。利用线性矩阵不等式及资源受限条件来确定带宽动态分配的上下界来保证系统的稳定性。定义了多回路归一化控制质量、归一化带宽需求度性能衡量指标来评价网络控制系统的全局性能。在此评价方法下,对模糊带宽管理方法和采用固定带宽分配方法以及基于线性规划的优化带宽分配方法进行仿真比较。
通过将系统控制性能、网络带宽需求等函数结合专家知识用一系列的约束条件表达成为一个多目标优化问题以达到最大化系统性能并且最小化带宽需求的目标。设计了基于神经网络的资源优化管理器来逼近该输入输出的非线性关系,通过数值优化方法结合专家知识得到一组数据集来训练神经网络的参数后在线对各个控制回路的带宽需求进行动态分配。通过对四种不同的资源分配方法的比较,表明该方法简单有效。
针对工作负载的不可预期变化及控制系统运行环境的不确定性,设计了一个基于最小二乘支持向量机的动态预测反馈调度器。分析了网络带宽的调节机理,给出了反馈调度器的实现方案。通过监测网络资源,在线预测出下一周期的网络可适用利用率,根据预测值采用插值法得到控制回路的下一个采样周期,从而实现系统资源的动态分配。结果表明该反馈调度策略能使系统在可变负载情况下稳定运行,并在控制质量和网络服务质量之间取得了平衡。
A considerable research attention has recently been focused on a class of networked control systems (NCSs) which are typically spatially distributed systems wherein the communication between sensors, actuators, and controllers occurs through a shared resources-limited communication network. However, the insertion of communication network into traditional point-point control system makes the analysis and design of control applications complex.
From resource scheduling perspective in this dissertation, networked control systems integrated control and scheduling codesign are reasonably modeled, and stability results and control laws design methods are also presented by some new techniques. An intelligent scheduling algorithm for the priority of network message is proposed. Some different solutions are also studied for optimization to the network resource and control performance. The main contributions are as follows:
Firstly, by employing the notion of communication sequence and mixed logical dynamical framework, a networked control system subject to communication constraints is modeled to an integrated control and scheduling of discrete-time linear period system with resource constraints. The schedulability conditions and asymptotic stability results of this closed loop system are discussed in detail. The solution of control law is also given under a certain periodic scheduling of network message.
Secondly, for an integrated control and scheduling of resource-constrained system with resource constraints, the optimal solution of discrete-time linear period system for period communication sequence and H-infinity control co-design is depicted by linear matrix inequalities (LMIs). A heuristic search method, namely increasing optimal sequence algorithm, can effectively seek the optimal solution of scheduling scheme and H-infinity controller for its asymptotical stability and r-exponential stability, respectively. The algorithm, in conjunction with the convex optimization of the LMIs, can successfully give a solution to the problem of scheduling and control co-design, and can greatly save the computational cost, simultaneously.
Thirdly, considering the error and error difference of system response, a fuzzy feedback scheduler that shares communication network is designed with the bandwidth constraint. A scheduling algorithm of the scheduler, namely fuzzy maximum first (FMF), can schedule dynamically the priority of network message. Four different scheduling algorithms with different stochastic delay series are compared, respectively. The results of simulation highlight that the proposed scheduling algorithm can optimize the performance of control loop and has more flexible than other algorithms in uncertain running conditions.
Fourthly, in order to optimize an unavoidable tradeoff between the control performance and the bandwidth consumption, an intelligent control approach to manage bandwidth dynamically, namely fuzzy bandwidth management (FBM), is proposed based on fuzzy logic control technique. The lower and upper bound of the assignable bandwidth, which can guarantee the system's stability, are evaluated in terms of LMIs and the resource constraints, respectively. In addition, the normalizable criterions of quality of control (QoC) and requirement of bandwidth (RoB) are also defined, which can estimate the performance of the whole networked control systems. The proposed approach, traditional fixed bandwidth allocation and optimal bandwidth allocation based on linear programming are also compared under these criterions.
Fifthly, considering control performance and network resource, a multi-objective programming with a set of constraints combining expert knowledge expressed as rules is presented in order to maximize control performance and minimize bandwidth consumption. An optimal resource manager based on neural network (NN) as good and robust nonlinear function approximator is employed to provide the optimal solution. The optimal algorithm combining expert knowledge as connotative constraints expressed as rules is used as a teacher to label the data samples for the NN well-trained. Consequently, the NN is employed to allocate requirement of bandwidth for each control loop at runtime. The simulation results show that the proposed optimal strategy is a simple and effective approach in comparison with other three different resource allocation strategies.
Finally, due to the variation of the workload and unpredictable open environment, a feedback scheduler based on least squares support vector machines (LSSVM) is designed in order to guarantee the stability of the system. The mechanism and its applications are discussed in detail. The feedback scheduler monitors the network resources, predicates the available utilization for the next period, and adopts interpolated method to calculate the next sampling period from predicative value. Consequently, the system's resources are dynamically allocated by this feedback scheduling mechanism. The results of simulation indicate that the feedback scheduling strategy can guarantee the stability of the system under flexible workload. These results also prove that the proposed strategy is an effective tradeoff method between quality of control and quality of service.
本文从资源调度的角度出发,建立了集成调度与控制协同设计的模型,提出了网络控制系统的稳定性条件和控制器的协同设计方法;对网络的优先级调度提出了智能调度算法;对网络资源和控制性能的优化提出了不同的解决方案。主要研究内容如下:
对于通信约束的网络控制系统,应用通信序列的概念和混合逻辑动态的构架,将其建模成一类集成控制和调度的资源约束离散周期系统。给出了这类系统网络信息的可调度条件和闭环系统渐近稳定的条件,同时也给出了在网络信息的周期调度下控制器的参数化设计方法。
对于一类集成控制和调度的资源约束系统模型,采用线性矩阵不等式来描述离散周期系统的周期通信序列和H_∞控制协同设计的优化问题。提出了一种递增优化序列算法来求解优化的调度策略及其相应渐近稳定及r指数稳定的H_∞控制器。该算法结合线性矩阵不等式凸优化解决了调度与控制的协同设计问题,同时也节省了搜索优化解的计算时间。
在网络带宽受限的情况下,综合考虑了系统响应的误差和误差变化率,设计了一个共享通信网络的模糊反馈调度器。该调度器采用模糊最大优先调度算法对网络消息发送的优先级进行动态调度。对四种不同调度算法在不同随机时延序列下进行了比较,结果表明这种智能调度算法优化了系统的控制性能,并在不确定运行环境中具有更好的适应性。
为了优化控制性能与网络带宽的折衷关系,设计了一个基于模糊逻辑的带宽管理器对控制系统的带宽进行动态管理。利用线性矩阵不等式及资源受限条件来确定带宽动态分配的上下界来保证系统的稳定性。定义了多回路归一化控制质量、归一化带宽需求度性能衡量指标来评价网络控制系统的全局性能。在此评价方法下,对模糊带宽管理方法和采用固定带宽分配方法以及基于线性规划的优化带宽分配方法进行仿真比较。
通过将系统控制性能、网络带宽需求等函数结合专家知识用一系列的约束条件表达成为一个多目标优化问题以达到最大化系统性能并且最小化带宽需求的目标。设计了基于神经网络的资源优化管理器来逼近该输入输出的非线性关系,通过数值优化方法结合专家知识得到一组数据集来训练神经网络的参数后在线对各个控制回路的带宽需求进行动态分配。通过对四种不同的资源分配方法的比较,表明该方法简单有效。
针对工作负载的不可预期变化及控制系统运行环境的不确定性,设计了一个基于最小二乘支持向量机的动态预测反馈调度器。分析了网络带宽的调节机理,给出了反馈调度器的实现方案。通过监测网络资源,在线预测出下一周期的网络可适用利用率,根据预测值采用插值法得到控制回路的下一个采样周期,从而实现系统资源的动态分配。结果表明该反馈调度策略能使系统在可变负载情况下稳定运行,并在控制质量和网络服务质量之间取得了平衡。
A considerable research attention has recently been focused on a class of networked control systems (NCSs) which are typically spatially distributed systems wherein the communication between sensors, actuators, and controllers occurs through a shared resources-limited communication network. However, the insertion of communication network into traditional point-point control system makes the analysis and design of control applications complex.
From resource scheduling perspective in this dissertation, networked control systems integrated control and scheduling codesign are reasonably modeled, and stability results and control laws design methods are also presented by some new techniques. An intelligent scheduling algorithm for the priority of network message is proposed. Some different solutions are also studied for optimization to the network resource and control performance. The main contributions are as follows:
Firstly, by employing the notion of communication sequence and mixed logical dynamical framework, a networked control system subject to communication constraints is modeled to an integrated control and scheduling of discrete-time linear period system with resource constraints. The schedulability conditions and asymptotic stability results of this closed loop system are discussed in detail. The solution of control law is also given under a certain periodic scheduling of network message.
Secondly, for an integrated control and scheduling of resource-constrained system with resource constraints, the optimal solution of discrete-time linear period system for period communication sequence and H-infinity control co-design is depicted by linear matrix inequalities (LMIs). A heuristic search method, namely increasing optimal sequence algorithm, can effectively seek the optimal solution of scheduling scheme and H-infinity controller for its asymptotical stability and r-exponential stability, respectively. The algorithm, in conjunction with the convex optimization of the LMIs, can successfully give a solution to the problem of scheduling and control co-design, and can greatly save the computational cost, simultaneously.
Thirdly, considering the error and error difference of system response, a fuzzy feedback scheduler that shares communication network is designed with the bandwidth constraint. A scheduling algorithm of the scheduler, namely fuzzy maximum first (FMF), can schedule dynamically the priority of network message. Four different scheduling algorithms with different stochastic delay series are compared, respectively. The results of simulation highlight that the proposed scheduling algorithm can optimize the performance of control loop and has more flexible than other algorithms in uncertain running conditions.
Fourthly, in order to optimize an unavoidable tradeoff between the control performance and the bandwidth consumption, an intelligent control approach to manage bandwidth dynamically, namely fuzzy bandwidth management (FBM), is proposed based on fuzzy logic control technique. The lower and upper bound of the assignable bandwidth, which can guarantee the system's stability, are evaluated in terms of LMIs and the resource constraints, respectively. In addition, the normalizable criterions of quality of control (QoC) and requirement of bandwidth (RoB) are also defined, which can estimate the performance of the whole networked control systems. The proposed approach, traditional fixed bandwidth allocation and optimal bandwidth allocation based on linear programming are also compared under these criterions.
Fifthly, considering control performance and network resource, a multi-objective programming with a set of constraints combining expert knowledge expressed as rules is presented in order to maximize control performance and minimize bandwidth consumption. An optimal resource manager based on neural network (NN) as good and robust nonlinear function approximator is employed to provide the optimal solution. The optimal algorithm combining expert knowledge as connotative constraints expressed as rules is used as a teacher to label the data samples for the NN well-trained. Consequently, the NN is employed to allocate requirement of bandwidth for each control loop at runtime. The simulation results show that the proposed optimal strategy is a simple and effective approach in comparison with other three different resource allocation strategies.
Finally, due to the variation of the workload and unpredictable open environment, a feedback scheduler based on least squares support vector machines (LSSVM) is designed in order to guarantee the stability of the system. The mechanism and its applications are discussed in detail. The feedback scheduler monitors the network resources, predicates the available utilization for the next period, and adopts interpolated method to calculate the next sampling period from predicative value. Consequently, the system's resources are dynamically allocated by this feedback scheduling mechanism. The results of simulation indicate that the feedback scheduling strategy can guarantee the stability of the system under flexible workload. These results also prove that the proposed strategy is an effective tradeoff method between quality of control and quality of service.
引文
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