Sliding Mode Controller Design for Robotic Manipulator Based on the Hybrid Pigeon‐Inspired Optimization

ABSTRACT To address the challenge of finding the optimal parameter combination for proportional–integral sliding mode (PISM) controllers in robotic manipulators, this article proposes a novel hybrid pigeon‐inspired optimization (HPIO) algorithm for systematic parameter tuning. The proposed algorithm enhances the conventional PIO framework by incorporating a predation‐inspired random perturbation mechanism and a dynamic population screening strategy, thereby balancing global exploration and local exploitation capabilities. An ablation study validates the individual contributions of the two improvement phases and confirms their synergistic effect on convergence speed and optimization accuracy. Then the HPIO algorithm is employed to optimize the proportional gain , integral gain , and robust gain of the PISM controller, where influences the tracking error response, improves steady‐state accuracy, and enhances robustness to disturbances. Comparative experiments show that HPIO improves the best and mean fitness values relative to PIO and provides a favorable trade‐off between optimization quality and offline computational cost. The proposed method also achieves improved tracking performance under the tested disturbance conditions.