In this paper, an output-feedback fuzzy adaptive dynamic surface controller (FADSC) based on fuzzy adaptive extended state observer (FAESO) is proposed for autonomous underwater vehicle (AUV) systems in the presence of external disturbances, parameter uncertainties, measurement noises and actuator faults. The fuzzy logic system is incorporated into both the observers and controllers to improve the adaptability of the entire system. The dynamics of the AUV system is established first, considering the external disturbances and parameter uncertainties. Based on the dynamic models, the ESO, combined with a fuzzy logic system tuning the observer bandwidth, is developed to not only adaptively estimate both system states and the lumped disturbances for the controller, but also reduce the impact of measurement noises. Then, the DSC, together with fuzzy logic system tuning the time constant of the low-pass filter, is designed using estimations from the FAESO for the AUV system. The asymptotic stability of the entire system is analyzed through Lyapunov’s direct method in the time domain. Comparative simulations are implemented to verify the effectiveness and advantages of the proposed method compared with other observers and controllers considering external disturbances, parameter uncertainties and measurement noises and even the actuator faults that are not considered in the design process. The results show that the proposed method outperforms others in terms of tracking accuracy, robustness and energy consumption.

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The motion-control precision of a shallow-sea underwater robot is seriously affected by external disturbances such as wind, waves and ocean currents. Due to the lack of a specialized disturbance-sensor system, the disturbance force and torque cannot be sensed effectively. Inspired by bionics, an artificial lateral-line system for estimating external disturbances of an underwater robot is presented in this paper. In the system, the pressure of water is first collected through the pressure-sensor array. Then, the pressure data is processed by a series of algorithms, and the disturbance force and torque are observed from the data. Both multiple linear regression and the artificial neural network method are used to fit the mathematical models of the disturbances. Finally, the system is validated experimentally to be effective and practical. The underwater robot senses the disturbance force and torque from the water indirectly through the artificial lateral-line system, which will improve the accuracy of motion control.

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