Fuzzy-Immune-Regulated Adaptive Degree-of-Stability LQR for a Self-Balancing Robotic Mechanism: Design and HIL Realization

Saleem, Omer; Iqbal, Jamshed

Abstract

This letter formulates a fuzzy-immune adaptive system for the online adjustment of the Degree-of-Stability (DoS) of Linear-Quadratic-Regulator (LQR) procedure to strengthen the disturbance attenuation capacity of a self-balancing mechatronic system. The fuzzy-immune adaptive system uses pre-configured control input-based rules to alter the DoS parameter of LQR for dynamically relocating the closed-loop system's eigenvalues in the complex plane's left half. The corresponding changes in the eigenvalues are conveyed to the Riccati equation, which eventually yields the self-adjusting LQR gains. This arrangement allows for the flexible manipulation of the applied control effort and the response speed as the error conditions change. The efficacies of the self-tuning LQR scheme are verified by performing custom-designed hardware-in-the-loop experiments on the Quanser rotary inverted pendulum system. As compared to the DoS-LQR, the proposed controller improves the pendulum's transient recovery time, overshoots, input demands, and offsets by 32.3%, 50.5%, 33.9%, and 33.3%, respectively, under disturbances. These experimental outcomes verify that the proposed self-tuning LQR law considerably improves the system's disturbance attenuation capability.

Citation

Saleem, O., & Iqbal, J. (2023). Fuzzy-Immune-Regulated Adaptive Degree-of-Stability LQR for a Self-Balancing Robotic Mechanism: Design and HIL Realization. IEEE Robotics and Automation Letters, https://doi.org/10.1109/LRA.2023.3286176

• Journal Article Type: Article
• Acceptance Date: Jun 1, 2023
• Publication Date: Jan 1, 2023
• Deposit Date: Jul 7, 2023
• Publicly Available Date: Jul 14, 2023
• Journal: IEEE Robotics and Automation Letters
• Electronic ISSN: 2377-3766
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• DOI: https://doi.org/10.1109/LRA.2023.3286176
• Public URL: https://hull-repository.worktribe.com/output/4323501