Adaptive neural network predictive control based on PSO algorithm

Authors:
Chengli Su;Yun Wu
Affiliations:
School of Information and Control Engineering, Liaoning Shihua University, Fushun, China;School of Information and Control Engineering, Liaoning Shihua University, Fushun, China
Venue:
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Year:
2009

Citing 4
Cited 0

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Recent approaches to global optimization problems through Particle Swarm Optimization

Natural Computing: an international journal
Neural-network predictive control for nonlinear dynamic systems with time-delay

IEEE Transactions on Neural Networks

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Abstract

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.