基于粒子群算法的PID神经网络解耦控制

PID Neural Network Decoupling Control Based on Particle Swarm Optimization

基于粒子群的优化算法具有对整个参数空间进行高效并行搜索的特点以及PID神经网络的自调节和自适应特性,设计了具有PID结构的多变量自适应神经网络控制器。该算法采用粒子群算法优化PID神经网络初始权值,并将优化后的最优初始权值控制非线性耦合系统。系统仿真结果表明,粒子群优化后的PID神经网络控制器具有逼近控制目标更快、响应时间较短的显著优点。该控制策略可在大范围内克服系统的非线性和强耦合问题,具有一定的理论研究价值和工程实用价值。

The automatic control of such a system is a research focus in the process control area. A multivariable adaptive PID Artificial Neural Network （ANN） controller was introduced, which was based on the characteristics of Particle Swarm Optimization （PSO） algo- rithm searching the parameter space concurrently and efficiently, and the self-regulation and adaptability of PID artificial neuron net- works. Utilize the PSO to optimize the initial weight value of PID neural network, successfully achieve the control strategy of a nonlinear coupling system using the improved PID neural network with those obtained from the original PID neural network. The new control strate- gy could overcome nonlinear and strong coupling features of the system in a wide range and is expected to have certain theoretical and en- gineering application value.