System Identification and Control using Adaptive Particle Swarm Optimization
This paper presents a methodology for finding optimal system parameters and optimal control parameters using a novel adaptive particle swarm optimization (APSO) algorithm. In the proposed APSO, every particle dynamically adjusts inertia weight according to feedback taken from particles' best memories. The main advantages of the proposed APSO are to achieve faster convergence speed and better solution accuracy with minimum incremental computational burden. In the beginning we attempt to utilize the proposed algorithm to identify the unknown system parameters the structure of which is assumed to be known previously. Next, according to the identified system, PID gains are optimally found by also using the proposed algorithm. Two simulated examples are finally given to demonstrate the effectiveness of the proposed algorithm. The comparison to PSO with linearly decreasing inertia weight (LDW-PSO) and genetic algorithm (GA) exhibits the APSO-based system's superiority.
A. Alfi and H. Modares, "System Identification and Control using Adaptive Particle Swarm Optimization," Applied Mathematical Modelling, vol. 35, no. 3, pp. 1210-1221, Elsevier, Mar 2011.
The definitive version is available at https://doi.org/10.1016/j.apm.2010.08.008
Electrical and Computer Engineering
Keywords and Phrases
Adaptive Particle Swarm Optimizations; Computational Burden; Faster Convergence; Inertia Weight; Optimal Controls; Particle Swarm; PID Controllers; PID Gains; Solution Accuracy; System Identifications; Adaptive Control Systems; Control System Analysis; Controllers; Electric Control Equipment; Genetic Algorithms; Particle Swarm Optimization (PSO); Proportional Control Systems; Two Term Control Systems; Parameter Estimation; Genetic Algorithm; Particle Swarm Optimization; PID Controller
International Standard Serial Number (ISSN)
Article - Journal
© 2011 Elsevier, All rights reserved.
01 Mar 2011