A neural network-based self-tuning PID controller of an autonomous underwater vehicle

Taking into account the complex interferences in underwater environment, this paper presents a neural network-based self-tuning PID controller for a spherical AUV. The control system consists of neural network identifier and neural network controller, and the weights of neural networks are trained by using Davidon least square method. The proposed controller is characterized with a strong anti-interference ability and a fast convergence rate. For its simple structure, the controller can be easily realized in hardware. The linear velocity of the spherical AUV can be controlled to precisely track any desired trajectory in vehicle-fixed coordinate system. The effectiveness of the controller is verified by simulation results.