For a class of linear discrete-time systems that is subject to randomly occurred networked packet loss in industrial cyber physical systems, a novel robust model predictive control method with active compensation mech...For a class of linear discrete-time systems that is subject to randomly occurred networked packet loss in industrial cyber physical systems, a novel robust model predictive control method with active compensation mechanism was proposed. The probability distribution of packet loss is described as the Bernoulli distributed white sequences. By using the Lyapunov stability theory, the existing sufficient conditions of the controller are derived from solving a group of linear matrix inequalities. Moreover, dropout-rate with uncertainty and unknown dropout-rate are also considered, which can greatly reduce the conservativeness of the controller. The designed robust model predictive control method not only efficiently eliminates the negative effects of the networked data loss in industrial cyber physical systems but also ensures the stability of closed-loop system. Two examples were provided to illustrate the superiority and effectiveness of the proposed method.展开更多
Robustly stable multi-step-ahead model predictive control (MPC) based on parallel support vector machines (SVMs) with linear kernel was proposed. First, an analytical solution of optimal control laws of parallel SVMs ...Robustly stable multi-step-ahead model predictive control (MPC) based on parallel support vector machines (SVMs) with linear kernel was proposed. First, an analytical solution of optimal control laws of parallel SVMs based MPC was derived, and then the necessary and sufficient stability condition for MPC closed loop was given according to SVM model, and finally a method of judging the discrepancy between SVM model and the actual plant was presented, and consequently the constraint sets, which can guarantee that the stability condition is still robust for model/plant mismatch within some given bounds, were obtained by applying small-gain theorem. Simulation experiments show the proposed stability condition and robust constraint sets can provide a convenient way of adjusting controller parameters to ensure a closed-loop with larger stable margin.展开更多
Model predictive controllers(MPC)with the two-loop scheme are successful approaches practically and can be classified into two main categories,tube-based MPC and MPCbased reference governors(RG).In this paper,an enhan...Model predictive controllers(MPC)with the two-loop scheme are successful approaches practically and can be classified into two main categories,tube-based MPC and MPCbased reference governors(RG).In this paper,an enhanced twoloop MPC design is proposed for a pre-stabilized system with the bounded uncertainty subject to the input and state constraints.The proposed method offers less conservatism than the tube-based MPC methods by enlarging the restricted input constraint.Contrary to the MPC-based RGs,the investigated method improves tracking performance of the pre-stabilized system while satisfying the constraints.Additionally,the robust global asymptotic stability of the closed-loop system is guaranteed in a novel procedure with terminal constraint relaxation.Simulation of the proposed method on a servo system shows its effectiveness in comparison to the others.展开更多
随着新能源大规模接入电网,为应对新能源随机性和波动性给互联系统负荷频率控制(Load Frequency Control,LFC)带来的不确定问题,实现新能源电力系统多约束条件下的优化运行,建立了含风电机组的LFC多胞模型,以减少模型参数不确定对控制...随着新能源大规模接入电网,为应对新能源随机性和波动性给互联系统负荷频率控制(Load Frequency Control,LFC)带来的不确定问题,实现新能源电力系统多约束条件下的优化运行,建立了含风电机组的LFC多胞模型,以减少模型参数不确定对控制系统的影响。设计了基于原对偶神经网络(Primal-Dual Neural Network,PDNN)的Tube鲁棒模型预测控制(Tube-Robust Model Predictive Control,Tube-RMPC)策略。将标称模型预测控制器与辅助反馈控制器结合,通过PDNN实时求解标称模型预测控制器以保证为LFC系统产生最优状态轨迹。设计辅助反馈控制器抵消外部干扰,使实际系统的状态维持在以标称轨迹为中心的Tube内。最后,对含风电的三区域负荷频率控制系统进行仿真研究,结果表明所提出的Tube-RMPC控制策略,不仅能够有效提高控制精度,还能增强系统鲁棒性,提高实时优化效率。展开更多
基金Project(61673199)supported by the National Natural Science Foundation of ChinaProject(ICT1800400)supported by the Open Research Project of the State Key Laboratory of Industrial Control Technology,Zhejiang University,China
文摘For a class of linear discrete-time systems that is subject to randomly occurred networked packet loss in industrial cyber physical systems, a novel robust model predictive control method with active compensation mechanism was proposed. The probability distribution of packet loss is described as the Bernoulli distributed white sequences. By using the Lyapunov stability theory, the existing sufficient conditions of the controller are derived from solving a group of linear matrix inequalities. Moreover, dropout-rate with uncertainty and unknown dropout-rate are also considered, which can greatly reduce the conservativeness of the controller. The designed robust model predictive control method not only efficiently eliminates the negative effects of the networked data loss in industrial cyber physical systems but also ensures the stability of closed-loop system. Two examples were provided to illustrate the superiority and effectiveness of the proposed method.
基金Project(2002CB312200) supported by the National Key Fundamental Research and Development Program of China project(60574019) supported by the National Natural Science Foundation of China
文摘Robustly stable multi-step-ahead model predictive control (MPC) based on parallel support vector machines (SVMs) with linear kernel was proposed. First, an analytical solution of optimal control laws of parallel SVMs based MPC was derived, and then the necessary and sufficient stability condition for MPC closed loop was given according to SVM model, and finally a method of judging the discrepancy between SVM model and the actual plant was presented, and consequently the constraint sets, which can guarantee that the stability condition is still robust for model/plant mismatch within some given bounds, were obtained by applying small-gain theorem. Simulation experiments show the proposed stability condition and robust constraint sets can provide a convenient way of adjusting controller parameters to ensure a closed-loop with larger stable margin.
基金Supported by National Natural Science Foundation of China （60504026, 60674041） and National High Technology Research and Development Program of China （863 Program）（2006AA04Z173）.
文摘Model predictive controllers(MPC)with the two-loop scheme are successful approaches practically and can be classified into two main categories,tube-based MPC and MPCbased reference governors(RG).In this paper,an enhanced twoloop MPC design is proposed for a pre-stabilized system with the bounded uncertainty subject to the input and state constraints.The proposed method offers less conservatism than the tube-based MPC methods by enlarging the restricted input constraint.Contrary to the MPC-based RGs,the investigated method improves tracking performance of the pre-stabilized system while satisfying the constraints.Additionally,the robust global asymptotic stability of the closed-loop system is guaranteed in a novel procedure with terminal constraint relaxation.Simulation of the proposed method on a servo system shows its effectiveness in comparison to the others.
文摘随着新能源大规模接入电网,为应对新能源随机性和波动性给互联系统负荷频率控制(Load Frequency Control,LFC)带来的不确定问题,实现新能源电力系统多约束条件下的优化运行,建立了含风电机组的LFC多胞模型,以减少模型参数不确定对控制系统的影响。设计了基于原对偶神经网络(Primal-Dual Neural Network,PDNN)的Tube鲁棒模型预测控制(Tube-Robust Model Predictive Control,Tube-RMPC)策略。将标称模型预测控制器与辅助反馈控制器结合,通过PDNN实时求解标称模型预测控制器以保证为LFC系统产生最优状态轨迹。设计辅助反馈控制器抵消外部干扰,使实际系统的状态维持在以标称轨迹为中心的Tube内。最后,对含风电的三区域负荷频率控制系统进行仿真研究,结果表明所提出的Tube-RMPC控制策略,不仅能够有效提高控制精度,还能增强系统鲁棒性,提高实时优化效率。
