Abstract:

Data-driven methods for learning optimal control policies such as adaptive dynamic programming have garnered widespread attention. A strong contrast to full-fledged theoretical research is the scarcity of demonstrated successes in industrial applications. This paper extends an established data-driven solution for a class of adaptive optimal linear output regulation problem to achieve self-tuning torque control of servomotor drives, and thus enables online adaptation to unknown motor resistance, inductance, and permanent magnet flux. We make contributions by tackling three practical issues/challenges: 1) tailor the baseline algorithm to reduce computation burden; 2) demonstrate the necessity of perturbing reference in order to learn feedforward gain matrix; 3) generalize the algorithm to the case where F matrix in the output equation is unknown. Simulation demonstrates that the deployment of adaptive dynamic programming lands at optimal torque tracking policies.