Optimization speed control of linear induction motors using CSA algorithm and nonlinear disturbance observer

This paper investigates a high-performance robust control for linear induction motor (LIM) drives based on the combination of the Crow Search Algorithm (CSA) and a Nonlinear Disturbance Observer (NDO) within the Field-Oriented Control (FOC) framework. The dynamic behavior of LIMs is strongly affected by longitudinal end-effects, parameter variations, and external disturbances, which degrade the performance of conventional PI-based controllers. To overcome these drawbacks, the CSA metaheuristic is employed to optimally tune the speed PI controller gains by minimizing a transient performance index, ensuring a balanced trade-off between fast response and stability. In addition, a nonlinear disturbance observer is designed to estimate lumped disturbances arising from load variations, model uncertainties, and end-effect nonlinearities, allowing their compensation within the control loop. The proposed CSA–NDO–FOC control structure is developed using the LIM dynamic model expressed in the synchronous (𝑑)–(𝑞) reference frame while explicitly considering the end-effect phenomenon. The effectiveness of the proposed approach is validated through detailed simulations in the MATLAB/Simulink environment and compared with a conventional PI–FOC scheme. The obtained results demonstrate significant performance improvements under sudden load disturbances and parameter variations, including a reduction in overshoot from 9.33% to 4.33%, a decrease in settling time by approximately 37.5%, and an improvement in steady-state accuracy of nearly 76%. These results confirm that the combined CSA-based optimal tuning and NDO-based disturbance compensation provide a robust and effective solution for improving the dynamic performance and disturbance rejection capability of LIM drives in high-precision linear motion applications.