基于合作粒子群算法的PID神经网络非线性控制系统

PID神经元网络 (PIDNN)模型为一种新型的神经网络模型,兼有PID与神经网络的共同优点,应用于复杂的控制系统.取得优良控制性能,但其后向传播算法 (BP)限制了该模型的应用范围.为实现对非线性多变量系统的有效控制,扩展神经网络的应有范围,本文采用PIDNN神经网络设计了多变量PIDNN神经网络 (MPIDNN)控制器,并用本文作者提出的合作粒子群算法 (CPSO)取代了传统BP后向传播算法,通过比较MPIDNN_CPSO、MPIDNNCRPSO、MPIDNN_PSO和MPIDNN_BP4种控制器的控制性能,仿真结果表明,基于CPSO算法的MPIDNN控制器实现了对非线性多变量不对称系统的有效控制.与传统的BP算法相比,CPSO算法提高了控制系统的稳定性、精确性与鲁棒性.

Nonlinear control system of PID neural network based on cooperated particle swarm optimization(PSO)

The PID neural network (PIDNN) model is a novel neural network model with the advantages of PID and artificial neuron network. This model has been used for complex control systems to achieve desirable control performances. However, the conventional backward-propagation (BP)algorithm restrains the model's wide applications to control field. To control the nonlinear MIMO system efficiently and to extend the application range of PIDNN, we develop the MIMO PID neural network (MPIDNN) controller based on PIDNN, and propose the cooperated PSO (CPSO) algorithm to take the place of BP algorithm. Simulation results of the MPIDNN controllers based on BP, PSO and CRPSO algorithms indicate that CPSO-based MPIDNN controller is more effective than the other three in controlling the MIMO systems. The CPSO algorithm makes MPIDNN controller better in performances than BP algorithm in accuracy, stability and robustness.