Bio-Inspired Fuzzy Sliding Mode Based Cruise Control of USV with Model Uncertainty

Inherent uncertainty in model parameters has positioned Unmanned Surface Vessels (USVs) as a focal point of global research. The sliding mode control (SM C), known for its robust capabilities, is considered a superior strategy in addressing the aforementioned challenges which unfortunately caused pronounced chattering effects in system states and control signals. To mitigate this problem, a Predictive Fuzzy Sliding Mode Controller (PFSMC) in a dual-loop structure is proposed in this work, incorporating biomimetic adaptive parameter adjustment characteristics based on environmental conditions. In this proposed approach, Model Predictive Control (MPC) is utilized for the outer-loop for position tracking. By generating smooth virtual velocity based on a relatively accurate kinematic model and predictive insights, a SMC method empowered by fuzzy logic is employed. This method dynamically adjusts the sliding surface and reaching law parameters according to real-time error feedback. Finally, the effectiveness of the proposed PFSMC is validated through simulation experiments in an USV cruising task.