An Adaptive PID Control Strategy Based on Offline–Online Cooperative Optimization

To address the difficulty of simultaneously achieving fast response, high stability, and strong disturbance rejection in gimbal systems operating under complex conditions, an adaptive control strategy based on offline–online cooperative optimization is proposed. The method is built upon the conventional proportional–integral–derivative (PID) control framework. First, a particle swarm optimization (PSO) algorithm is employed offline to obtain an optimized set of initial control parameters, thereby improving the transient response during the startup phase. Subsequently, a single-neuron adaptive (SNA) mechanism is introduced to adjust the control parameters online according to real-time error information, enhancing the system’s adaptability to environmental variations and external disturbances. Stability analysis demonstrates the convergence of the proposed control scheme. Finally, an experimental gimbal platform is constructed to validate the effectiveness of the method. Experimental results show that, under various disturbance conditions, the proposed strategy effectively reduces angular fluctuation amplitude, shortens the settling time, and maintains smooth control performance. These results indicate that the proposed control strategy exhibits strong robustness and significant engineering applicability.