为了抑制集总干扰(外部扰动及模型失配、变量间耦合导致的内部扰动)对系统的影响,以往的方法通常采用反馈+前馈补偿的控制方式,不能保证系统控制输出最优.为此,本文提出了一种基于扰动观测器的多变量非最小状态空间预测控制方法(disturb...为了抑制集总干扰(外部扰动及模型失配、变量间耦合导致的内部扰动)对系统的影响,以往的方法通常采用反馈+前馈补偿的控制方式,不能保证系统控制输出最优.为此,本文提出了一种基于扰动观测器的多变量非最小状态空间预测控制方法(disturbance observer-based multivariable non-minimum state space predictive control, D-MNMSSPC).本方法首先通过扰动观测器(disturbance observer, DOB)估计集总干扰,然后将扰动的估计值及输出变量同时引入到状态变量中形成复合多变量非最小状态空间(multivariable non-minimum state space, MNMSS)预测模型,该模型可以避免状态观测器的设计,减小了设计负担,并且可以保证扰动直接参与预测控制滚动优化,从而获得系统的最优控制性能,仿真结果验证了D-MNMSSPC方法的有效性.展开更多
By using the stochastic martingale theory, convergence properties of stochastic gradient （SG） identification algorithms are studied under weak conditions. The analysis indicates that the parameter estimates by the S...By using the stochastic martingale theory, convergence properties of stochastic gradient （SG） identification algorithms are studied under weak conditions. The analysis indicates that the parameter estimates by the SG algorithms consistently converge to the true parameters, as long as the information vector is persistently exciting （i.e., the data product moment matrix has a bounded condition number） and that the process noises are zero mean and uncorrelated. These results remove the strict assumptions, made in existing references, that the noise variances and high-order moments exist, and the processes are stationary and ergodic and the strong persis- tent excitation condition holds. This contribution greatly relaxes the convergence conditions of stochastic gradient algorithms. The simulation results with bounded and unbounded noise variances confirm the convergence conclusions proposed.展开更多
A constrained decoupling (generalized predictive control) GPC algorithm is proposed for MIMO (malti-input multi-output) system. This algorithm takes account of all constraints of inputs and their increments. By solvin...A constrained decoupling (generalized predictive control) GPC algorithm is proposed for MIMO (malti-input multi-output) system. This algorithm takes account of all constraints of inputs and their increments. By solving matrix equations, the multi-step predictive decoupling controllers are realized. This algorithm need not solve Diophantine functions, and weakens the cross-coupling of the variables. At last the simulation results demon- strate the effectiveness of this proposed strategy.展开更多
文摘为了抑制集总干扰(外部扰动及模型失配、变量间耦合导致的内部扰动)对系统的影响,以往的方法通常采用反馈+前馈补偿的控制方式,不能保证系统控制输出最优.为此,本文提出了一种基于扰动观测器的多变量非最小状态空间预测控制方法(disturbance observer-based multivariable non-minimum state space predictive control, D-MNMSSPC).本方法首先通过扰动观测器(disturbance observer, DOB)估计集总干扰,然后将扰动的估计值及输出变量同时引入到状态变量中形成复合多变量非最小状态空间(multivariable non-minimum state space, MNMSS)预测模型,该模型可以避免状态观测器的设计,减小了设计负担,并且可以保证扰动直接参与预测控制滚动优化,从而获得系统的最优控制性能,仿真结果验证了D-MNMSSPC方法的有效性.
基金Supported by the National Natural Science Foundation of China (Grant Nos. 60574051 and 60674092) the Natural Science Foundation of Jiangsu Province, China (Grant No. BK2007017) and by Program for Innovative Research Team of Jiangnan University
文摘By using the stochastic martingale theory, convergence properties of stochastic gradient （SG） identification algorithms are studied under weak conditions. The analysis indicates that the parameter estimates by the SG algorithms consistently converge to the true parameters, as long as the information vector is persistently exciting （i.e., the data product moment matrix has a bounded condition number） and that the process noises are zero mean and uncorrelated. These results remove the strict assumptions, made in existing references, that the noise variances and high-order moments exist, and the processes are stationary and ergodic and the strong persis- tent excitation condition holds. This contribution greatly relaxes the convergence conditions of stochastic gradient algorithms. The simulation results with bounded and unbounded noise variances confirm the convergence conclusions proposed.
基金Supported by the National Natural Science Foundation of China (No.60374037, No.60574036), the Program for New Century Excellent Talents in University of China (NCET), and the Specialized Research Fund for the Doctoral Program of Higher Edu-cation of China (No.20050055013).
文摘A constrained decoupling (generalized predictive control) GPC algorithm is proposed for MIMO (malti-input multi-output) system. This algorithm takes account of all constraints of inputs and their increments. By solving matrix equations, the multi-step predictive decoupling controllers are realized. This algorithm need not solve Diophantine functions, and weakens the cross-coupling of the variables. At last the simulation results demon- strate the effectiveness of this proposed strategy.
