A Self-Tuning Variable Universe Fuzzy PID Control Framework with Hybrid BAS-PSO-SA Optimization for Unmanned Surface Vehicles
Huixia Zhang, Zhao Zhao, Yuchen Wei, Yitong Liu, Wenyang Wu
Abstract
In this study, a hybrid heading control framework for unmanned surface vehicles (USVs) is proposed, combining variable domain fuzzy Proportional–Integral–Derivative (VUF-PID) with an improved algorithmic Beetle Antennae Search–Particle Swarm Optimization–Simulated Annealing (BAS-PSO-SA) optimization to address the multi-objective control challenge. Key innovations include a self-tuning VUF mechanism that improves disturbance rejection by 42%, a weighted adaptive optimization strategy that reduces parameter tuning iterations by 37%, and an asymmetric learning factor that balances global exploration and local refinement. Benchmarks using Rastrigin, Griewank, and Sphere functions show superior convergence and 68% stability improvement. Ocean heading simulations of a 7.02 m unmanned surface vehicle (USV) using the Nomoto model show a 91.7% reduction in stabilization time, a 0.9% reduction in overshoot, and a 30% reduction in optimization iterations. The experimental validation under wind and wave disturbances shows that the heading deviation is less than 0.0392°, meeting the IMO MSC.1/Circ.1580 standard, and an 89.5% improvement in energy efficiency. Although the processing time is 12.7% longer compared to the GRO approach, this framework lays a solid foundation for ship autonomy systems, and future enhancements will focus on MPC-based time delay compensation and Field-Programmable Gate Array (FPGA) acceleration.