A Discrete-Time Least-Squares Adaptive State Tracking Control Scheme with A Mobile-Robot System Study

TL;DR

This paper introduces a novel discrete-time adaptive control scheme using least-squares for mobile robots, addressing a long-standing problem with proven stability, optimality, and collision avoidance in simulation.

Contribution

It develops a new least-squares based adaptive control method for discrete-time systems, applicable to mobile robots, with stability and collision avoidance mechanisms.

Findings

Successful state tracking in simulation

Ensured collision avoidance

Validated stability and optimality

Abstract

This paper develops an adaptive state tracking control scheme for discrete-time systems, using the least-squares algorithm, as the new solution to the long-standing discrete-time adaptive state tracking control problem to which the Lyapunov method (well-developed for the continuous-time adaptive state tracking problem) is not applicable. The new adaptive state tracking scheme is based on a recently-developed new discrete-time error model which has been used for gradient algorithm based state tracking control schemes, and uses the least-squares algorithm for parameter adaptation. The new least-squares algorithm is derived to minimize an accumulative estimation error, to ensure certain optimality for parameter estimation. The system stability and output tracking properties are studied. Technical results are presented in terms of plant-model matching, error model, adaptive law, optimality formulation, and stability and tracking analysis. The developed adaptive control scheme is applied to a discrete-time multiple mobile robot system to meet an adaptive state tracking objective. In addition, a collision avoidance mechanism is proposed to prevent collisions in the whole tracking process. Simulation results are presented, which verify the desired system state tracking properties under the developed least-squares algorithm based adaptive control scheme.