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Neural network adaptive control for underwater robotic systems

Neural networks are currently finding practical applications, ranging from ‘soft’ regulatory control in consumer products to accurate modelling of non-linear systems. This paper describes the application of neural networks to the control of a remotely operated underwater vehicle, as an example of a system containing severe nonlinearities. Neural networks are been used in a closed-loop to approximate the nonlinear vehicle dynamics. No prior off-line training phase and no explicit knowledge of the structure of the vehicle are required, and the proposed scheme exploits the advantages of both neural network control and adaptive control. A control law and a stable on-line adaptive law are derived using the Lyapunov theory, and the convergence of the tracking error to zero and the bounded-ness of signals are guaranteed. In this paper, a neural network architecture based on radial basis functions has been used to evaluate the performance of the adaptive controller via computer simulation.