Neural-Network-Based Generalized Predictive Control for Autonomous Underwater Vehicles

Considering the highly nonlinear and time-variant dynamics of autonomous underwater vehicles(AUVs),a generalized predictive control strategy based on neural network is presented.The modified Elman neural network is used as the multi-step predictive model and the neural network learning algorithm is improved.Online learning of the modified Elman network is realized,and the sensitivity formula to solve the neural generalized predictive control law is presented.Experiments are made on the velocity predictive control of AUVs with on-line and off-line neural network learning,and the predictive control results are analyzed contrastively.Experiment results show that the velocity control accuracy of the AUV with on-line learning is more accurate than that of the AUV with off-line learning,and the former has stronger adaptability when the AUV dynamics changes.