This paper studies the problem of the mixed H/Hrobust model predictive control(RMPC) for a class of linear sys...
HUANG He,LI Dewei~*,XI Yugeng Department of Automation,Shanghai Jiao Tong University, Key Laboratory of System Control and Information Processing,Ministry of Education of China, Shanghai,200240,P.R.China.
针对具有外界扰动的线性定常(Linear time invariant,LTI)系统,本文研究了其鲁棒预测控制器(Robust model predictive control,RMPC)的设计方法.设计采用了混合的H2/H∞控制方法以有效地兼顾系统的抗干扰能力和闭环控制性能.同时,为了降低设计的保守性,设计利用闭环多步控制策略以扩大控制器的可行范围,改善系统控制性能.进而,为了便于实际实施,提出该RMPC的简化设计,通过将大部分在线计算量离线完成以降低鲁棒预测控制器的在线计算量.
High performance computer is often required by model predictive control(MPC) systems due to the heavy online computation burden.To extend MPC to more application cases with low-cost computation facilities, the implementation of MPC controller on field programmable gate array(FPGA) system is studied.For the dynamic matrix control(DMC) algorithm,the main design idea and the implemental strategy of DMC controller are introduced based on a FPGA’s embedded system.The performance tests show that both the computation efficiency and the accuracy of the proposed controller can be satisfied due to the parallel computing capability of FPGA.
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.
