System identification and control using adaptive particle swarm optimization

被引:139
作者
Alfi, Alireza [1 ]
Modares, Hamidreza [2 ]
机构
[1] Shahrood Univ Technol, Fac Elect & Robot Engn, Shahrood 3619995161, Iran
[2] Ferdowsi Univ Mashhad, Dept Elect Engn, Mashhad 917751111, Iran
关键词
Particle swarm optimization; Parameter estimation; PID controller; Genetic algorithm; NEURAL-NETWORK; PID CONTROLLER; DESIGN;
D O I
10.1016/j.apm.2010.08.008
中图分类号
T [工业技术];
学科分类号
120111 [工业工程];
摘要
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. (C) 2010 Elsevier Inc. All rights reserved.
引用
收藏
页码:1210 / 1221
页数:12
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