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An autonomous under vehicle (AUV) should have the ability of self-saving and finishing the certain targets when faults occur, which means that an AUV must have the ability of fault-tolerant control. In order to make it possible, one AUV’s fault-tolerant control strategy is made, which is based on the active disturbance rejection control (ADRC). In this paper, the control method in normal and the one in fault are offered respectively. Besides that, one simulation compared with PID control is made. The simulation results show the AUV’s fault-tolerant control strategy based on ADRC can achieve the goal and has better control results to restrain the shock, overshoot and other phenomena caused by disturbance than the strategy based on PID.

This paper addresses the stability analysis on S Plane Control in terms of both position and velocity control. Employing Lyapunov stability theory and T-passivity theory, this paper proves the stability of the position controller based on S Plane Control, and on this ground, the stability analysis of the velocity controller based on S Plane Control is done. Finally, the S Plane Control results obtained from the sea trials are given.

S surface control is a simple and operative motion control algorithm for underwater vehicles, but it has two parameters requiring to be adjusted manually. In order to enhance the adaptability of S surface controller, the research of S surface controller parameter self-tuning methods based on rules and models is carried out. Firstly, combined with fuzzy control, parameter self-tuning method based on fuzzy rules is presented. Then by means of predictive control theory, model-based parameter self-tuning method is proposed. By introducing the nonlinear autoregressive moving average model, the prediction model of underwater vehicles is established using parallel Elman neural network, and the optimal parameters of S surface controller is obtained by constructing quadratic performance index function. The results of simulation experiments show that the response speed of S surface controller with parameter self-tuning modules is improved, and the parameter self-tuning methods is demonstrated feasible and effective.

An autonomous underwater vehicle (AUV) should have the ability of adapting the complexity and unpredictability of the marine environment, which means that the technology of AUV’s fault diagnosis is very significant, especially the part of thrusters. In order to make it possible, one fault diagnosis strategy of AUV’s thrusters is proposed, which is based on the support vector machine (SVM). SVM has many unique advantages in solving small-sample, nonlinear and high dimensional problems. In this paper, different character signal is inputted SVM to train and test it. The simulation results show that the fault diagnosis of AUV’s thrusters based on offline SVM can classify the fault styles successfully, which proves its feasibility and effectiveness. This method offers a new way to solve the fault diagnosis of AUVs.