This paper investigates the problem of receding horizon state estimation for networked control systems （NCSs） with random network-induced delays less than one sample period, which are formulated as multirate control systems. Based on a batch of recent past slow rate measurements in a finite horizon window, the initial state estimation in this window is solved by minimizing a receding-horizon objective function, and then the fast rate state estimations are calculated by the prediction of dynamic equation to compensate for the network-induced time delays. Furthermore, convergence results and unbiasedness properties are analyzed. An upper bound of estimation error is presented under the assumption of bounded disturbances acting on the system and measurement equations. A simulation example shows the effectiveness of the proposed method.
A min-max model predictive control strategy is proposed for a class of constrained nonlinear system whose trajectories can be embedded within those of a bank of linear parameter varying （LPV） models. The embedding LPV models can yield much better approximation of the nonlinear system dynamics than a single LTV model. For each LPV model, a parameter-dependent Lyapunov function is introduced to obtain poly-quadratically stable control law and to guarantee the feasibility and stability of the origi- nal nonlinear system. This approach can greatly reduce computational burden in traditional nonlinear predictive control strategy. Finally a simulation example illustrating the strategy is presented.
＜正＞Data-based modeling of unknown distributed parameter systems（DPSs） is very challenging due to their infinit...
QI Chenkun~1,LI Han-Xiong~2,ZHANG Xian-Xia~3,ZHAO Xianchao~1,LI Shaoyuan~4,GAO Feng~1 1.School of Mechanical Engineering,Shanghai Jiao Tong University,Shanghai 200240,P.R.China 2.Department of Manufacturing Engineering & Engineering Management,City University of Hong Kong,Hong Kong 3.Shanghai Key Laboratory of Power Station Automation Technology,School of Mechatronics and Automation,Shanghai University, Shanghai 200072,P.R.China 4.Department of Automation,Shanghai Jiao Tong University,Shanghai 200240,P.R.China
In this paper, a low-dimensional multiple-input and multiple-output （MIMO） model predictive control （MPC） configuration is presented for partial differential equation （PDE） unknown spatially-distributed systems （SDSs）. First, the dimension reduction with principal component analysis （PCA） is used to transform the high-dimensional spatio-temporal data into a low-dimensional time domain. The MPC strategy is proposed based on the online correction low-dimensional models, where the state of the system at a previous time is used to correct the output of low-dimensional models. Sufficient conditions for closed-loop stability are presented and proven. Simulations demonstrate the accuracy and efficiency of the proposed methodologies.
