Experiments of iterative learning control system using particle swarm optimization by new bounded constraints on velocity and positioning. (2014)

• Record Type: Journal Article
• Other Names: Special Editor.
• Is Part Of: Engineering computations. Volume 31(2014)Supplement
• Page Start: 250
• Page End: 266
• Publication Date: 2014
• Subjects: Improved particle swarm optimization -- Iterative learning control -- Linear synchronous motor -- PID control; Computer-aided engineering -- Periodicals; Computer graphics -- Periodicals; 620.00285
• DOI: 10.1108/EC-01-2013-0013
• Languages: English
• ISSNs: 0264-4401

Abstract

Abstract : Purpose – This paper utilizes the improved particle swarm optimization (IPSO) with bounded constraints technique on velocity and positioning for adjusting the gains of a proportional‐integral‐derivative (PID) and iterative learning control (ILC) controllers. The purpose of this paper is to achieve precision motion through bettering control by this technique.Design/methodology/approach – Actual platform positioning must avoid the occurrence of a large control action signal, undesirable overshooting, and preventing out of the maximum position limit. Several in‐house experiments observation, the PSO mechanism is sometimes out of the optimal solution in updating velocity and updating position of particles, the system may become unstable in real‐time applications. The proposed IPSO with new bounded constraints technique shows a great ability to stabilize nonminimum phase and heavily oscillatory systems based on new bounded constraints on velocity and positioning in PSO algorithm is evaluated on one axis of linear synchronous motor with a PC‐based real‐time ILC.Findings – Simulations and experiment results show that the proposed controller can reduce the error significantly after two learning iterations. The developed method using bounded constraints technique provides valuable programming tools to practicing engineers.Originality/value – The proposed IPSO‐ILC‐PID controller overcomes the shortcomings of conventional ILC‐PID controller with fixed gains. Simulation and experimental results show that the proposed IPSO‐ILC‐PID algorithm exhibits great speed convergence and robustness. Experimental results confirm that the proposed IPSO‐ILC‐PID algorithm is effective and achieves better control in real‐time precision positioning. Acknowledgements : The authors would like to express their acknowledgement to the financial support under National Science Council by the project of 100‐2221‐E‐018‐007.