TR2022-125

Self-tuning Optimal Torque Control for Servomotor Drives Via Adaptive Dynamic Programming


    •  Wang, Y., "Self-tuning Optimal Torque Control for Servomotor Drives Via Adaptive Dynamic Programming", IEEE International Conference on Systems, Man, and Cybernetics (SMC), October 2022.
      BibTeX TR2022-125 PDF
      • @inproceedings{Wang2022oct,
      • author = {Wang, Yebin},
      • title = {Self-tuning Optimal Torque Control for Servomotor Drives Via Adaptive Dynamic Programming},
      • booktitle = {IEEE International Conference on Systems, Man, and Cybernetics (SMC)},
      • year = 2022,
      • month = oct,
      • url = {https://www.merl.com/publications/TR2022-125}
      • }
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  • Research Areas:

    Control, Machine Learning, Optimization

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.