Research on Dual-Closed-Loop Speed Control of DC Motor Based on Multi-Level Integral Separation and Genetic Algorithm Optimization

In the low-speed operation of the DC motor dual closed-loop speed control system, The PI controller of the speed loop is prone to experiencing integral saturation due to the persistent presence of large errors., which leads to increasing overshoot and prolonged adjustment time, seriously restricting the system performance. To address this issue, this paper proposes an improved speed loop controller. This controller adopts a multi-level integral separation strategy, managing the integral action in stages based on the error magnitude; and innovatively utilizes the genetic algorithm (GA), taking the comprehensive control index (overshoot + adjustment time) as the fitness function, to automatically optimize the multi-level separation thresholds offline. Experiments were conducted on the Matlab/Simulink platform for the 583.3 rpm step start-up condition. The results show that compared with the traditional PI control, single-level integral separation PI control, and multi-level integral separation PI control with manually set thresholds, the proposed GA-optimized multi-level integral separation method can effectively suppress integral saturation, significantly reduce overshoot (nearly zero) and shorten the adjustment time (by approximately 18%). This method effectively solves the low-speed integral saturation problem, significantly improves the dynamic performance and robustness of the system, and provides an automated optimization solution for high-performance low-speed control of DC motors.