Analysis of Unmanned Surface Vehicles Heading KF-Based PI-(1+PI) Controller Using Improved Spider Wasp Optimizer

This paper proposes a Kalman filter-based cascaded PI-(1+PI) controller, optimized using an Improved Spider Wasp Optimizer (ISWO), to address the challenges of USV heading control in dynamic marine environments. Traditional PID controllers struggle with nonlinearities and noise in USV systems while existing metaheuristic algorithms face limitations in balancing exploration and exploitation. To overcome these issues, the ISWO integrates dynamic adaptive grouping, perturbation dimension-symmetric distance optimization, and nonlinear time-varying weights, enhancing convergence speed and optimization accuracy. A transfer function model of the USV heading system is established using voyage data, with ISWO optimizing its parameters, achieving a 5.67% reduction in mean squared error (MSE) compared to the original Spider Wasp Optimizer and outperforming classical algorithms like Arithmetic Optimization Algorithm (AOA), Crayfish Optimization Algorithm (COA), and Marine Predators Algorithm (MPA). The proposed KF-PI(1+PI) controller incorporates a Kalman filter to suppress noise and a cascaded structure to improve gain and response speed, reducing integrated time absolute error (ITAE) by 84% relative to traditional PID controllers. The hardware-in-the-loop simulation experiments further validate the proposed controller’s robustness. The study demonstrates that ISWO-optimized control systems significantly enhance USV navigation precision and adaptability, offering a viable solution for autonomous marine operations.