Learning and Adaptation-Based Position-Tracking Controller for Rover Vehicle Applications Considering Actuator Dynamics

Abstract

This article suggests an intelligent position-tracking control algorithm for rover vehicles considering actuator (dc motor) dynamics. The parameter and load uncertainties in the vehicle and dc motor dynamics are explicitly handled by modifying the original open-loop system dynamics. The proposed controller forms the conventional multiloop structure including disturbance observers for each loop. The features of this article fall into the following three parts: first, the learning part from the real-time feedback gain mechanism (named the self-tuner) in the closed-form for outer loop; second, the adaptation part from the online wheel radius estimation securing the outer-loop control accuracy; and third, the parameter-independent angular acceleration observer-based pole-zero cancellation dc motor speed controller without current feedback considering the inner- and outer-loop vehicle control algorithms. Experimental evidence is also provided to demonstrate the practical merits of the proposed technique with the use of the TETRIX, myRIO-1900, and LabVIEW.