Adaptive Neural Network Predictive Control Based on PSO Algorithm

摘要：

A neural network-based model predictive control scheme is proposed for nonlinear systems. In this scheme an adaptive diagonal recurrent neural network (DRNN) is used for modeling of nonlinear processes. A recursive estimation algorithm using the extended Kalman filter (EKF) is proposed to calculate Jacobian matrix in the model adaptation so that the algorithm is simple and converges fast. Particle swarm optimization (PSO) is adopted to obtain optimal future control inputs over a prediction horizon, which overcomes effectively the shortcoming of descent-based nonlinear programming method on the initial condition sensitivity. A case study of biochemical fermentation process shows that the performance of the proposed control scheme is better than that of PI controller.

关键词： Model predictive control (MPC) Diagonal recurrent neural network (DRNN) Particle swarm optimization (PSO)

作者: Chengli Su Yun Wu

作者单位: School of Information and Control Engineering, Liaoning Shihua University, Fushun China 113001

会议类型: 国际会议

会议名称: 2009年中国控制与决策会议(2009 Chinese Control and Decision Conference)

会议地点: 广西桂林

会议语种:英文

页码: 5829-5833

在线出版日期: 2009-06-17（万方平台首次上网日期，不代表论文的发表时间）