DHP Adaptive Critic Motion Control of Autonomous Wheeled Mobile Robot

Abstract

Autonomous drive of wheeled mobile robot (WMR) needs implementing velocity and path tracking control subject to complex dynamical constraints. Conventionally, this control design is obtained by analysis and synthesis of the WMR system. This paper presents the dual heuristic programming (DHP) adaptive critic design of the motion control system that enables WMR to achieve the control purpose simply by learning through trial. The design consists of an adaptive critic velocity neuro-control loop and a posture neuro-control loop. The neural weights in the velocity neuro-controller (VNC) are corrected with the DHP adaptive critic method. The designer simply expresses the control objective with a utility function. The VNC learns by sequential optimization to satisfy the control objective. The posture neuro-controller (PNC) approximates the inverse velocity model of WMR so as to map planned positions to desired velocities. Supervised drive of WMR in variant velocities supplies training samples for the PNC and VNC to setup the neural weights. In autonomous drive, the learning mechanism keeps improving the PNC and VNC. The design is evaluated on an experimental WMR. The excellent results make it certain that the DHP adaptive critic motion control design enables WMR to develop the control ability autonomously.