Research on PID self-tuning control based on recursive least squares parameter identification for the fast steering mirror-based optoelectronic tracking system

In the optoelectronic tracking system based on fast steering mirror (FSM), the traditional PID algorithm cannot achieve optimal control performance under parameter perturbations; intelligent algorithms, represented by machine learning, often fail to meet fast response requirements. To address this issue, this paper proposes a control strategy based on Least Squares Method with Discounted Measurement system parameter identification self-tuning PID control (RDM-STC-PID). This algorithm identifies the parameters of the FSM system online with input and output signals, allowing the controller to achieve self-tuning. The Least Squares Method with Discounted Measurement identification algorithm utilizes weighting factors and forgetting factors to suppress noise and adapt to system changes. The weighting factor enhances sensitivity to new data, while the forgetting factor reduces the influence of old data. Subsequently, the identified system parameter is used to precisely design the self-tuning controller parameters through an optimal pole-zero configuration method. Creating FSM system with quantitative parameter variability is challenging. Therefore, this paper verifies the approach through hardware-in-the-loop and physical experiments. The experimental results demonstrate that the system with the proposed method exhibits significantly improved robustness both before and after parameter disturbances, compared to traditional PID controllers and self-tuning control with conventional identification methods. Specifically, the step response achieves a stable regulation time of 40 ms, an overshoot of 2.25, and a reduction in integral squared error to 1.524, reflecting a marked improvement in performance.