Quantized state feedback adaptive trajectory tracking control of Unmanned Surface Vehicle with state and input quantization

The main topic of this paper focuses on the trajectory tracking control of Unmanned Surface Vehicle with signal quantization effects. Neural network observers are used for observation in order to assess quantized states, system uncertainty, and external disturbances . The acquired observation results are then used in the development of the adaptive sliding mode controller . In order to address input quantization issues, linear analytic models are introduced, which eliminate the need for prior quantization parameter information while building control laws. The Nussbaum function is integrated to address the problem of unknown control direction. The stability of both the closed-loop system and all closed-loop signals is then demonstrated through the application of various lemmas and theorems. The suggested control strategy is validated using simulation studies, demonstrating its efficacy.