This paper considers a quasi-min-max model predictive control（MPC）problem for constrained linear parameter var...
ZHAO Min 1,LI Shaoyuan 2 1.Department of Control Science and Engineering,University of Shanghai for Science and Technology,2.Department of Automation,Shanghai Jiao Tong University,Shanghai 200240,P.R.China
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.
This paper is concerned with the H∞ filtering problems for both continuous- and discrete-time Markov jumping linear systems （MJLS） with non-accessible mode information. A new design method is proposed, which greatly reduces the overdesign introduced in the derivation process. The desired filters can be obtained from the solution of convex optimization problems in terms of linear matrix inequalities （LMIs）, which can be solved via efficient interior-point algorithms. Numerical examples are provided to illustrate the advantages of the proposed approach.
