Quantized-States-Based Fuzzy Adaptive Course Tracking Control of an Unmanned Surface Vehicle with Input and State Quantization

Aiming at the course tracking control of unmanned surface vehicle (USV) under the restricted communication bandwidth at sea, this paper focuses on the fuzzy adaptive control scheme with input and state quantization. It alleviates the pressure on signal transmission in limited bandwidth and reduces the actuator actuation frequency while ensuring effective tracking. A fuzzy logic system is suggested for the purpose of approximating uncertainty terms in USV motion model. All state variables and control input signal are assumed to be quantized by the uniform quantizer, respectively. Then, the quantized control input signal is described linearly so that the designed fuzzy quantised controller does not need to anticipate specific information about the quantization parameters. Subsequently, the analysis of the control system without considering state quantization and the bounded nature of the quantization error within the fuzzy adaptive feedback control architecture are demonstrated through the elucidation of several pivotal theoretical lemmas. Based on these lemmas, the stability of the devised system with input and state quantization is established. Three sets of simulation experiments validate the efficacy and viability of the proposed strategy.