Improved Manta Ray Foraging Optimization for PID Control Parameter Tuning in Artillery Stabilization Systems

In this paper, an Improved Manta Ray Foraging Optimization (IMRFO) algorithm is proposed to address the challenge of parameter tuning in traditional PID controllers for artillery stabilization systems. The proposed algorithm introduces chaotic mapping to optimize the initial population, enhancing the global search capability; additionally, a sigmoid function and Lévy flight-based dynamic adjustment strategy regulate the selection factor and step size, improving both convergence speed and optimization accuracy. Comparative experiments using five benchmark test functions demonstrate that the IMRFO algorithm outperforms five commonly used heuristic algorithms in four cases. The proposed algorithm is validated through co-simulation and physical platform experiments. Experimental results show that the proposed approach significantly improves control accuracy and response speed, offering an effective solution for optimizing complex nonlinear control systems. By introducing heuristic optimization algorithms for self-tuning artillery stabilization system parameters, this work provides a new approach to enhancing the intelligence and adaptability of modern artillery control.