Title: Quasi min-max model predictive control for Hammerstein system over networks with packet losses

Authors: Na Liu; Xiaoming Tang; Shuang Yang

Addresses: Key Laboratory of Industrial Internet of Things and Networked Control, Ministry of Education, Chongqing University of Posts and Telecommunications, Chongqing 400065, China ' Key Laboratory of Industrial Internet of Things and Networked Control, Ministry of Education, Chongqing University of Posts and Telecommunications, Chongqing 400065, China ' Key Laboratory of Industrial Internet of Things and Networked Control, Ministry of Education, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

Abstract: In this paper, a constrained model predictive control (MPC) approach is presented for the networked control systems (NCSs) containing Hammerstein nonlinearity and bounded packet losses. The Hammerstein nonlinearity is partially removed by establishing its pseudo-inverse, and the remaining weaker nonlinearity is tackled by the polytopic description. The model of NCS is constructed from the standpoint of robust control, which transforms the stabilisation of the control systems with packet losses into the stabilisation of a set of subsystems. The constrained networked MPC approach is given by parameterising the infinite horizon control moves into a free control move followed by a state feedback law which the input and state constraints are explicitly considered. Compared with the networked MPC without free control move, the presented approach improves the control performance of the closed-loop system, which is verified by a comparison simulation example.

Keywords: MPC; model predictive control; NCSs; networked control systems; Hammerstein model; nonlinearity; constrained control system; bounded packet losses; optimisation; polytopic description; robust control; state feedback.

DOI: 10.1504/IJSCIP.2017.089815

International Journal of System Control and Information Processing, 2017 Vol.2 No.2, pp.165 - 179

Received: 20 Oct 2017
Accepted: 25 Oct 2017

Published online: 12 Feb 2018 *

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