Abstract

The problem of robust tube-based model predictive control (MPC) is considered for a class of discrete-time constrained linear systems with time-delayed states and additional disturbances. This paper presents an active robust control scheme that can simultaneously cope with disturbances and time-delay states using active approaches, rather than inherent system robustness. The proposed robust controller design methodology is implemented by constructing a minimal robust control-invariant set of nominal linear systems with time-delayed states neglecting additional disturbances. Based on the time-delay-dependent robust MPC approach for the nominal time-delay system, a novel active robust tube-based MPC algorithm considering time-delayed states is proposed. Furthermore, a delay-dependent sufficient condition for the tube-based controller is derived. Based on the implementation of the delay-dependent robust tube-based controller, the novel active control method is much less conservative than previous approaches. Numerical simulation results demonstrate the effectiveness of the proposed method.

Highlights

  • Model predictive control (MPC), which is known as receding horizon control, has received significant attention in the control engineering field

  • PRELIMINARY RESULTS The proposed controller for constrained linear systems with time delays is based on the robust MPC algorithm for regulation proposed in [4]

  • In this paper, we proposed a novel delay-dependent active robust tube-based MPC algorithm for a class of linear constrained systems with time-delayed states and bounded additional disturbances

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Summary

Introduction

Model predictive control (MPC), which is known as receding horizon control, has received significant attention in the control engineering field. It has become a standard control strategy in industrial fields for implementing constrained, multivariable control in current process industries. Significant progress has been made toward ideal MPC with optimal theoretical properties, such as guaranteed stability and robustness [7], [8]. Time delays, such as computation time, are typically ignored in idealized models.

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