Title :
Neural network modeling and generalized predictive control for an autonomous underwater vehicle
Author :
Xu, Jianan ; Mingjun Zhang
Author_Institution :
Coll. of Mech. & Electr. Eng., Harbin Eng. Univ., Harbin
Abstract :
This paper investigates the application of neural network based generalized predictive motion control to an autonomous underwater vehicle. The modified Elman neural network is used as the multi-step predictive model, the fused identification model is proposed to improve the predictive and control precision. The modified Elman neural network on-line learning improves the control system adaptability to the unpredicted operating environment for autonomous underwater vehicle. Simulations on autonomous underwater vehicle yaw velocity control are included to illustrate the effectiveness of the proposed control scheme.
Keywords :
adaptive control; learning (artificial intelligence); motion control; neurocontrollers; predictive control; remotely operated vehicles; underwater vehicles; velocity control; Elman neural network online learning; autonomous underwater vehicle; control system adaptability; fused identification model; generalized predictive motion control; multistep predictive model; neural network modeling; yaw velocity control; Artificial neural networks; Control systems; Motion control; Neural networks; Nonlinear dynamical systems; Open loop systems; Predictive control; Predictive models; Underwater vehicles; Vehicle dynamics;
Conference_Titel :
Industrial Informatics, 2008. INDIN 2008. 6th IEEE International Conference on
Conference_Location :
Daejeon
Print_ISBN :
978-1-4244-2170-1
Electronic_ISBN :
1935-4576
DOI :
10.1109/INDIN.2008.4618149
