Dual-mode robust MPC for the tracking control of non-holonomoic mobile robots

TL;DR

This paper introduces a dual-mode robust MPC method for non-holonomic mobile robots that enhances tracking accuracy and reduces computational load by combining robust MPC with local nonlinear control, validated through simulations.

Contribution

It proposes a novel dual-mode control framework integrating robust MPC and local nonlinear control for improved tracking of non-holonomic robots under disturbances.

Findings

Enhanced tracking accuracy demonstrated in simulations

Reduced computational burden compared to traditional MPC

Effective disturbance rejection in non-holonomic mobile robots

Abstract

In this paper, a novel dual-mode robust model predictive control (MPC) approach is proposed for solving the tracking control problem of non-holonomoic mobile robots with additive bounded disturbance. To reduce the negative effect of disturbance and drive the state of real system closer to the one of nominal system , a robust reference signal is introduced into the cost function of MPC. In order to reduced the computation burden caused by online optimization of MPC and further improve the tracking accuracy, a dual-mode control strucuture consisting of the robust MPC and the local nonlinear robust control is developed, in which the local nonlinear robust control law is applied within a specified terminal region. Finally, simulation results on the non-holonomic mobile robot are presented to show the validity of the proposed control approach.