Hybrid Particle Swarm Optimization with Quasi-Newton Local Search for Quadrotor Altitude and Attitude Control

This paper presents a novel Hybrid Particle Swarm Optimization with Quasi-Newton method (HPSO-QN) for tuning Proportional-Integral-Derivative (PID) controllers in quadro-tor altitude and attitude stabilization. The proposed HPSO-QN method combines the global search capability of Particle Swarm Optimization (PSO) with the local search strength of the Quasi-Newton (QN) method to improve the optimization of the controller gains while avoiding local minima. Additional penalty conditions are integrated into the algorithm to maintain system stability throughout the optimization process. Comparative results with the standard PSO demonstrate that the proposed HPSO-QN method achieves significant performance improvements in robustness and precision. Statistical analysis of the cost functions confirms that the average is 2.3 times lower and the standard deviation is 30.4 times lower compared to SPSO, emphasizing its effectiveness in quadrotor control systems.