Fuzzy-Augmented Model Reference Adaptive PID Control Law Design for Robust Voltage Regulation in DC–DC Buck Converters

Saleem, Omer; Rasheed Ahmad, Khalid; Iqbal, Jamshed

Abstract

This paper presents a novel fuzzy-augmented model reference adaptive voltage regulation strategy for the DC–DC buck converters to enhance their resilience against random input variations and load-step transients. The ubiquitous proportional-integral-derivative (PID) controller is employed as the baseline scheme, whose gains are tuned offline via a pre-calibrated linear-quadratic optimization scheme. However, owing to the inefficacy of the fixed-gain PID controller against parametric disturbances, it is retrofitted with a model reference adaptive controller that uses Lyapunov gain adaptation law for the online modification of PID gains. The adaptive controller is also augmented with an auxiliary fuzzy self-regulation system that acts as a superior regulator to dynamically update the adaptation rates of the Lyapunov gain adaptation law as a nonlinear function of the system’s classical error and its normalized acceleration. The proposed fuzzy system utilizes the knowledge of the system’s relative rate to execute better self-regulation of the adaptation rates, which in turn, flexibly steers the adaptability and response speed of the controller as the error conditions change. The propositions above are validated by performing tailored hardware experiments on a low-power DC–DC buck converter prototype. The experimental results validate the improved reference tracking and disturbance rejection ability of the proposed control law compared to the fixed PID controller.

Citation

Saleem, O., Rasheed Ahmad, K., & Iqbal, J. (2024). Fuzzy-Augmented Model Reference Adaptive PID Control Law Design for Robust Voltage Regulation in DC–DC Buck Converters. Mathematics, 12(12), Article 1893. https://doi.org/10.3390/math12121893

• Journal Article Type: Article
• Acceptance Date: Jun 15, 2024
• Online Publication Date: Jun 18, 2024
• Publication Date: 2024
• Deposit Date: Jun 18, 2024
• Publicly Available Date: Jun 19, 2024
• Electronic ISSN: 2227-7390
• Publisher: MDPI
• Peer Reviewed: Peer Reviewed
• Volume: 12
• Issue: 12
• Article Number: 1893
• DOI: https://doi.org/10.3390/math12121893
• Public URL: https://hull-repository.worktribe.com/output/4711340