quotation:		[Copy]
		Dewei LI,Yugeng XI,Yuanyuan ZOU.[en_title][J].Control Theory and Technology,2013,11(2):230~236.[Copy]

This Paper:Browse 1581 Download 79	码上扫一扫！

DeweiLI,YugengXI,YuanyuanZOU
0 Fontlarge +\|Default\|Small
(Department of Automation, and Key Laboratory of System Control and Information Processing, Ministry of Education, Shanghai Jiao Tong University;Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology)

摘要:

关键词:

DOI：

Received:February 28, 2012Revised:May 29, 2012

基金项目:This work was supported by the National Science Foundation of China (Nos. 60934007, 61074060, 61004062), the China Postdoctoral Science Foundation Special Support(No. 201003272), the Shanghai ‘Chen Guang’ Program (No. 10CG30), and the Research and innovation project of Shanghai Education Commission (No. 11CXY08).

Model predictive control for constrained uncertain piecewise linear systems

Dewei LI,Yugeng XI,Yuanyuan ZOU

(Department of Automation, and Key Laboratory of System Control and Information Processing, Ministry of Education, Shanghai Jiao Tong University;Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology)

Abstract:

For constrained piecewise linear (PWL) systems, the possible existing model uncertainty will bring the difficulties to the design approaches of model predictive control (MPC) based on mixed integer programming (MIP). This paper combines the robust method and hybrid method to design the MPC for PWL systems with structured uncertainty. For the proposed approach, as the system model is known at current time, a free control move is optimized to be the current control input. Meanwhile, the MPC controller uses a sequence of feedback control laws as the future control actions, where each feedback control law in the sequence corresponds to each partitions and the arbitrary switching technique is adopted to tackle all the possible switching. Furthermore, to reduce the online computational burden of MPC, the segmented design procedure is suggested by utilizing the characteristics of the proposed approach. Then, an offline design algorithm is proposed, and the reserved degree of freedom can be online used to optimize the control input with lower computational burden.

Key words: Model predictive control Constrained piecewise linear system Structured uncertainty Segmented design procedure