Sliding mode control of quadrotor UAVs using improved opposition-based Newton-Raphson-based optimizer

This paper introduces a novel metaheuristic optimization algorithm, the Opposition-Based Newton-Raphson-Based Optimizer (OBNRBO), with application to quadrotor UAVs control. The proposed method enhances the conventional Newton-Raphson-Based Optimizer (NRBO) by integrating Opposition-Based Learning (OBL), thereby strengthening global exploration, reducing the risk of premature convergence, and accelerating convergence speed while preserving high solution accuracy. This strategy improves population diversity and search efficiency, resulting in improved optimization reliability and robustness. The performance of OBNRBO is rigorously evaluated using benchmark optimization functions and compared with the standard NRBO and several state-of-the-art algorithms. The results demonstrate that OBNRBO consistently achieves higher accuracy, faster convergence rates, and improved statistical stability. To demonstrate its practical effectiveness, OBNRBO is applied for tuning Sliding Mode Controllers (SMCs) for quadrotor UAV position and attitude control. Quadrotor systems present significant control challenges due to nonlinear dynamics, coupling effects, and disturbances, making them a suitable test platform. The proposed OBNRBO-based tuning approach improves time-domain performance indices by approximately 10–20% for PID controllers and 5–30% for SMC controllers compared with standard NRBO tuning. The OBNRBO-optimized SMC achieves the best overall performance across different trajectories and evaluation criteria, confirming the algorithm’s effectiveness in complex nonlinear control applications.