Adaptive Model Predictive Control of Wheeled Mobile Robots

TL;DR

This paper presents an adaptive model predictive control algorithm for guiding two-wheeled mobile robots with unknown inertia, utilizing recursive least squares for parameter updates, demonstrated through numerical simulations.

Contribution

It introduces an adaptive MPC framework that estimates unknown inertia parameters in real-time for improved robot control.

Findings

Effective guidance of mobile robots demonstrated in simulations.

Adaptive parameter estimation improves control accuracy.

Framework applicable to nonholonomic robot models.

Abstract

In this paper, a control algorithm for guiding a two wheeled mobile robot with unknown inertia to a desired point and orientation using an Adaptive Model Predictive Control (AMPC) framework is presented. The two wheeled mobile robot is modeled as a knife edge or a skate with nonholonomic kinematic constraints and the dynamical equations are derived using the Lagrangian approach. The inputs at every time instant are obtained from Model Predictive Control (MPC) with a set of nominal parameters which are updated using a recursive least squares algorithm. The efficacy of the algorithm is demonstrated through numerical simulations at the end of the paper.