PID Neural Network Decoupling Control Based on Hybrid Particle Swarm Optimization and Differential Evolution

For complex systems with high nonlinearity and strong coupling, the decoupling control technology based on proportion integration differentiation (PID) neural network (PIDNN) is used to eliminate the coupling between loops.The connection weights of the PIDNN are easy to fall into local optimum due to the use of the gradient descent learning method.In order to solve this problem, a hybrid particle swarm optimization (PSO) and differential evolution (DE) algorithm (PSO-DE) is proposed for optimizing the connection weights of the PIDNN.The DE algorithm is employed as an acceleration operation to help the swarm to get out of local optima traps in case that the optimal result has not been improved after several iterations.Two multivariable controlled plants with strong coupling between input and output pairs are employed to demonstrate the effectiveness of the proposed method.Simulation results show that the proposed method has better decoupling capabilities and control quality than the previous approaches.