A PSO-based gain optimization of an iterative learning controller for a robotic manipulator under torque uncertainty
11th International Conference on Recent Advances in Air and Space Technologies, Conference Program, RAST 2026, İstanbul, Turkey, 13 - 15 May 2026, (Full Text)
- Publication Type: Conference Paper / Full Text
- Doi Number: 10.1109/rast69551.2026.11672507
- City: İstanbul
- Country: Turkey
- Keywords: Iterative Learning Controller, Particle Swarm Optimization, Robotic Manipulator, Torque Uncertainty
- Yıldız Technical University Affiliated: Yes
Abstract
This study presents the design of an iterative learning controller (ILC) for a robotic arm operating under multiplicative torque uncertainty. The ILC's gain values were determined using particle swarm optimization (PSO), a nature-inspired swarm intelligence algorithm. The algorithm's primary objective is to identify an optimal set of gain parameters that simultaneously minimize the steady-state error, reduce the control effort, and mitigate fluctuations in the control signal. A switching mechanism was incorporated into the controller based on the gain values obtained from the PSO approach to enhance both the transient and steady-state responses. Simulation studies were conducted to evaluate the performance of the proposed control system, and the results suggest a notable enhancement in the system's overall performance.