Addie Irawan, Hashim and Mohd Syakirin, Ramli and M. H., Sulaiman and Mohd Iskandar Putra, Azahar (2023) Optimal pneumatic actuator positioning and dynamic stability using prescribed performance control with particle swarm optimization: A simulation study. International Journal of Robotics and Control Systems, 3 (3). pp. 364-379. ISSN 2775-2658. (Published)
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Abstract
This paper introduces an optimal control strategy for pneumatic servo systems (PSS) positioning using Finite-time Prescribed Performance Control (FT-PPC) with Particle Swarm Optimization (PSO). Pneumatic servo systems are widely used in industrial automation, as well as medical and cybernetics systems that involve robotics applications. Precision in pneumatic control is crucial not only for the sake of efficiency but also safety. The primary goal of the proposed control strategy is to optimize the convergence rate and finite time of the prescribed performance function in error transformation of the FT-PPC, as well as the Proportional, Integral and Derivative (PID) controller as the inner-loop controller for this system. The study utilizes a dynamic model of a pneumatic proportional valve with a double-acting cylinder (PPVDC) as the targeted plant and performs simulations with a multi-step input trajectory. This offline tuning method is essential for such nonlinear systems to be safely optimized, avoiding major damage to the real-time fine-tuned works on the controller. The results demonstrate that the proposed control strategy surpasses the performance of FT-PPC with a PID controller alone, significantly improving the system's performance, including suppressing overshoot and oscillation in the responses. Further validation through the actual system of PPVDC using the fine-tuned values of FT-PPC and PID with PSO is a future task and more challenging to come, as hardware constraints may vary with different environments such as temperatures.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Pneumatic actuator; Prescribed performance control; Swarm intelligence; Metaheuristic |
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Faculty/Division: | Faculty of Electrical and Electronic Engineering Technology |
| Depositing User: | Assoc.Prof Addie Irawan |
| Date Deposited: | 12 Sep 2023 07:30 |
| Last Modified: | 12 Sep 2023 07:30 |
| URI: | http://umpir.ump.edu.my/id/eprint/38602 |
| Download Statistic: | View Download Statistics |
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