基于PSO算法的自适应降阶系统辨识方法研究

System Identification of Adaptive Reduced Order Based on PSO Algorithm

针对实际流程工业中系统的模型阶次无法确定问题,提出一种基于粒子群优化算法的自适应降阶辨识方法。该方法将高阶传递函数作为系统初始模型,使用粒子群优化算法辨识被控对象,在满足降阶条件下给系统模型降低阶次实现被控对象参数估计。最后给出MATLAB仿真案例,仿真研究结果佐证了这种自适应降阶方法的有效性并达到较好辨识精度,在流程工业领域具有一定的实用价值。

Aiming at solving the difficulty of model order uncertainty in industrial systems,an adaptive reduced order method is proposed based on the particle swarm optimization algorithm （PSO）.It regards the higher order transfer function as the initial model system to identify the controlled object with the help of PSO.Under the condition of reducing the order, system model order is reduced to realize the parameter estimation of the controlled object.In the end,this paper offers sim-ulation cases on MATLAB.