摘要
针对反馈线性化策略中补偿器参数与实际受控对象参数发生失配的情况,提出一种基于粒子群优化算法求解实际系统参数的方法.以补偿器与受控对象参数完全匹配为参考系统,将施加在实际系统的激励信号同样施加在这个参考系统上,对两者的输出加以比较,进一步构造参数优化问题.决策变量是受控对象中发生变化的参数集,目标函数是两者输出之差的最小累加和,约束条件包括决策变量的上下限、系统的模型以及外部控制器等.采用粒子群优化算法求解这个非线性规划问题,所得决策变量的最优值即为当前实际受控系统参数的真实值.大量仿真计算表明,粒子群优化算法能够求出较为精确的实际系统参数值,并实现补偿器参数与实际受控对象参数之间的重新匹配.
A kind of solving practical system parameter method based on the particle swarm optimization algorithm is presented for the case where there is mismatch between compensator parameters and practical system parameters in feedback linearization. The complete matching between compensator parameters and practical system parameters is treated as reference system, excitation signal is posed onto practical system and reference system simultaneously, and their outputs are put into contrast, then optimization problem can be structured. Decision variables are changeable parameters, the objective function is minimum sum of the difference between their outputs, constraint condition includes the bound of decision variables, system model and external controller and so on. This nonlinear programming problem can be solved by using the PSO method, and the optimal solutions are the practical system parameters real values, a mass of simulation calculations show that the PSO algorithm can calculate more accurate practical system parameters values, and bring the matching again between compensator parameters and practical system parameters.
出处
《沈阳化工大学学报》
CAS
2016年第4期356-361,共6页
Journal of Shenyang University of Chemical Technology
关键词
等效线性化失配
PSO算法
参考系统
参数优化
equivalent linearization mismatch
PSO algorithm
reference system
parameter optimization
