[1]YU Lijun,CHEN Jia,LIU Fanming,et al.An improved particle swarm optimization forPID neural network decoupling control[J].CAAI Transactions on Intelligent Systems,2015,10(5):699-704.[doi:10.11992/tis.201406028]
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An improved particle swarm optimization forPID neural network decoupling control

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 10 Number of periods: 2015 5 Page number: 699-704 Column: 学术论文—智能系统 Public date: 2015-10-25
Title:
An improved particle swarm optimization forPID neural network decoupling control
Author(s):
YU Lijun;  CHEN Jia;  LIU Fanming;  WANG Hui
College of Automation, Harbin Engineering University, Harbin 150001, China
Keywords:
particle swarm algorithms; integrate stabilization system; PID neural network; decoupling control; simulation analysis
CLC:
TH186
DOI:
10.11992/tis.201406028
Abstract:
The integrated ship stabilization system has nonlinear, multi-variable and strong coupling characteristics, which may hinder the system from reaching the best control state. An improved particle swarm algorithm is proposed based on the characteristics of particle swarm optimization (PSO) algorithm, which can search the parameter space efficiently, along with its associated PID artificial neuron network that has self-regulation and adaptability. The improved particle swarm algorithm can overcome disadvantages in former particle swarm algorithms such as low precision, the particles tend to fall into extremely small values, and so on. In addition, the improved algorithm can increase the training speed and precision of the PID nerve network, which facilitates parameter optimization. The simulation results show that the improved PSO has certain advantages, it can reduce ship rolling, and can achieve excellent control effects when it is applied to the design of the decoupling control of an integrated stabilization control system.
References:
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