Robust Model Predictive Control of Networked Control Systems under Input Constraints and Packet Dropouts

Deyin Yao,Qing Lu,Yiyong Sun,Hamid Reza Karimi

doi:10.1155/2014/478567

Deyin Yao, Qing Lu + Show 2 more

Open Access

https://doi.org/10.1155/2014/478567

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Abstract

This paper deals with the problem of robust model predictive control (RMPC) for a class of linear time-varying systems with constraints and data losses. We take the polytopic uncertainties into account to describe the uncertain systems. First, we design a robust state observer by using the linear matrix inequality (LMI) constraints so that the original system state can be tracked. Second, the MPC gain is calculated by minimizing the upper bound of infinite horizon robust performance objective in terms of linear matrix inequality conditions. The method of robust MPC and state observer design is illustrated by a numerical example.

Highlights

Model predictive control (MPC) [1, 2] is an important method to handle control problems with systems having input, state, and output constraints [3–7]
The MPC controller gain and dynamic output controller are represented in Sections 5 and 6, respectively
We assume that the linear discrete-time system (1) has input constraints [35], which satisfies at each instant k ≥ 0, as follows:

Summary

Introduction

Model predictive control (MPC) [1, 2] is an important method to handle control problems with systems having input, state, and output constraints [3–7]. The robust MPC controller should be developed by considering packet dropouts and constraints. We describe an industrial process system as the linear time-varying system with packet dropouts from the MPC controller and dynamic output controller to the plant. The main merit of this paper is the following one: a robust MPC controller is developed for a control system with uncertainties, saturations, and packet dropouts under time-varying probabilities. The MPC controller gain and dynamic output controller are represented in Sections 5 and 6, respectively. E{x} and E{x | y} represent the expectation of event x and the expectation of x conditional on y, respectively

Problem Formulation

Infinite Horizon Robust Performance Objective Analysis

State Observer Design

MPC Controller Design

Dynamic Output Controller

Simulation Results

Conclusion