Adaptive NN Fault-Tolerant Control for Underactuated MSVs with Input Quantization

• Published in: Chinese Automation Congress (Online) pp. 8689 - 8694
• Main Authors: Zhang, Nanyan, Lai, Xiao
• Format: Conference Proceeding
• Language: English
• Published: IEEE 17.11.2023
• Subjects: Actuator fault; Actuators; Adaptation models; Artificial neural networks; input quantization; neural network; Quantization (signal); Simulation; Trajectory tracking; Upper bound
• ISSN: 2688-0938
• DOI: 10.1109/CAC59555.2023.10451065

In this work, we have studied the problem of trajectory tracking control for underactuated marine surface vehicles (MSV s) subject to uncertain model parameters, unknown external disturbances, actuator undesirable faults, and input signal quantization. To effectively handle input signal quantization, a hysteresis quantizer is employed. The uncertain dynamics of the MSVs are approximated by using neural networks (NN). By resorting the adaptive technique to estimate an upper bound for the compound disturbances are lumped by time-varying external disturbances, actuator faults, and NN approximation errors. By introducing a continuous smooth function, the trajectory tracking control law was developed within backstepping frameworks. The theoretical deduce and simulation results confirm the effectiveness of the developed control strategy.