A novel adaptive PD-type iterative learning control of the PMSM servo system with the friction uncertainty in low speeds

Riaz, Saleem; Qi, Rong; Tutsoy, Onder; Iqbal, Jamshed

Abstract

High precision demands in a large number of emerging robotic applications strengthened the role of the modern control laws in the position control of the Permanent Magnet Synchronous Motor (PMSM) servo system. This paper proposes a learning-based adaptive control approach to improve the PMSM position tracking in the presence of the friction uncertainty. In contrast to most of the reported works considering the servos operating at high speeds, this paper focuses on low speeds in which the friction stemmed deteriorations become more obvious. In this paper firstly, a servo model involving the Stribeck friction dynamics is formulated, and the unknown friction parameters are identified by a genetic algorithm from the offline data. Then, a feedforward controller is designed to inject the friction information into the loop and eliminate it before causing performance degradations. Since the friction is a kind of disturbance and leads to uncertainties having time-varying characters, an Adaptive Proportional Derivative (APD) type Iterative Learning Controller (ILC) named as the APD-ILC is designed to mitigate the friction effects. Finally, the proposed control approach is simulated in MATLAB/Simulink environment and it is compared with the conventional Proportional Integral Derivative (PID) controller, Proportional ILC (P-ILC), and Proportional Derivative ILC (PD-ILC) algorithms. The results confirm that the proposed APD-ILC significantly lessens the effects of the friction and thus noticeably improves the control performance in the low speeds of the PMSM.

Citation

Riaz, S., Qi, R., Tutsoy, O., & Iqbal, J. (2023). A novel adaptive PD-type iterative learning control of the PMSM servo system with the friction uncertainty in low speeds. PLoS ONE, 18(1), Article e0279253. https://doi.org/10.1371/journal.pone.0279253

• Journal Article Type: Article
• Acceptance Date: Dec 3, 2022
• Online Publication Date: Jan 18, 2023
• Publication Date: Jan 18, 2023
• Deposit Date: Feb 3, 2023
• Publicly Available Date: Feb 6, 2023
• Journal: PLoS ONE
• Print ISSN: 1932-6203
• Electronic ISSN: 1932-6203
• Publisher: Public Library of Science
• Peer Reviewed: Peer Reviewed
• Volume: 18
• Issue: 1
• Article Number: e0279253
• DOI: https://doi.org/10.1371/journal.pone.0279253
• Public URL: https://hull-repository.worktribe.com/output/4190426