To address the challenge of manually tuning optimal parameters for the linear active disturbance rejection control(LADRC)system of a permanent magnet linear synchronous motor(PMLSM)

an improved particle swarm optimization(PSO)based LADRC strategy was proposed. Specifically

the crossover and mutation mechanisms of the genetic algorithm(GA)are integrated into the PSO framework to prevent premature convergence. And the inertia weights of chaotic mapping and adaptive dynamically adjusted learning factors were used to balance the global and local search ability. Benchmark function tests demonstrate the superior performance of the proposed algorithm compared to existing PSO-based parameter tuning methods. Simulation and experimental verification of the LADRC strategy demonstrate that tuning parameters with the improved PSO algorithm significantly enhances the PMSLM's dynamic response and anti-interference capability. Specifically

it achieves a shorter response time during no-load startup compared to the bandwidth method

standard PSO

and improve particle swarm optimization-genetic algorithm(IPSO-GA)hybrid

and reduces the position deviation under load disturbance by 0.99 mm(28.0%)

0.38 mm(13.0%)

and 0.15 mm(5.5%)against these methods

respectively

confirming the validity of this research.