Fuzzy observer based backstepping control for supercavitating vehicle with time-varying state constraints

This study proposed a novel backstepping control algorithm that integrates fuzzy logic observation and anti-saturation compensation to address the challenges of state constraints, uncertainties, and input saturation in supercavitating vehicle longitudinal motion control systems. The research methodology begins with establishing a dynamic model of the supercavitating vehicle and deriving a control-oriented model that accounts for input saturation and uncertainties. To manage system uncertainties, a finite-time convergence observer incorporating fuzzy logic is developed. The control algorithm employs an Adaptive Barrier Lyapunov Function to handle asymmetric time-varying state constraints and distinguishes itself from conventional approaches by incorporating error power information, thereby enhancing control performance. Additionally, a fixed-time convergence anti-saturation compensator is implemented to mitigate input saturation effects, with control stability validated through Lyapunov analysis. The effectiveness of the proposed control system is demonstrated through comprehensive simulation results and subsequent analysis.