Genetic Algorithm-Optimized Nonlinear Active Disturbance Rejection Control for Stability Enhancement in Linear Motors

This study presents a Nonlinear Active Disturbance Rejection Control (NLADRC) strategy, optimized by genetic algorithms, to address the stability degradation of linear motors under external load disturbances. The proposed approach demonstrates its effectiveness by enhancing the motor's resistance to disturbances through real-time estimating and compensating uncertainties. By employing a nonlinear mechanism, the strategy improves the accuracy in estimating disturbances, ensuring that the control system maintains stability and responsiveness even in the presence of load variations and parameter fluctuations. To overcome the complexity of parameter tuning in NLADRC, a genetic algorithm is introduced to iteratively optimize the adjustable parameters, thereby improving the overall control performance of the system. Furthermore, the accuracy of the state observer is enhanced by incorporating a Savitzky-Golay filter and an Expanded State Observer (ESO), which further boosts the system's disturbance rejection capability. The results demonstrate that the proposed strategy accelerates the system's response speed and significantly enhances its anti-disturbance capability, effectively resolving the stability issues of the linear motor under complex operating conditions.