【摘要】 针对标准粒子群算法(PSO)把惯性权值作为全局参数,很难适应复杂的非线性优化过程的问题,提出了一种基于粒距和动态区间的权值调整策略。根据粒子的粒距大小在动态区间内选取不同的权值,并通过区间的动态变化来控制算法的收敛速度。通过对Rastigrin函数的测试表明,改进算法的收敛速度和收敛精度均有显著提高。最后,将改进算法用于永磁同步电机速度控制器的PI参数优化中。Matlab/Simulink仿真结果表明,该方法具有速度响应快、超调量小的特点,有效地提高了伺服系统的动态性能。更多还原

【Abstract】 Because the same inertia weight is used to update the velocity of particles in the standard particle swarm optimization algorithm(PSO),it cannot adapt to the complex and nonlinear optimization process.Aiming at this problem,the paper put forward a strategy of inertia weight adjustment based on particle spacing and dynamic interval.According to particle spacing,the inertia weight was chosen,and convergence rate of the algorithm were controlled by dynamic change of interval.Optimization tests of Rastigrin functions demonstrate that the NPSO algorithm performs better in convergence speed and accuracy as well as its global search ability.And finally,the NPSO algorithm is applied to PI parameters tuning for speed control of PMSM.Simulation results in Matlab/Simulink show that the optimized PID controller has rapid response and low overshoot,and can effectively improve the dynamic performance for the servo system.更多还原