基于迭代学习的开关磁阻电动机最优控制研究

以转矩脉动最小化为目标,提出了一种开关磁阻电动机（SRM）最优控制器的设计方法.基于迭代学习控制（ILC）的基本原理,阐述了单输入单输出（SISO）非线性系统中ILC的收敛性和稳定性的一般性结论.建立了以非线性磁链模型为基础的SRM数学模型.所设计的最优控制器由基于转矩分配函数（TSF）的转矩控制器和电流控制器构成,转矩控制器根据期望转矩经过“学习”得出期望电流曲线,电流控制器经过“学习”使实际输出电流逼近期望电流曲线,从而实现转矩控制.仿真结果表明,在恒转矩负载下,转矩脉动减小,且具有较好的收敛性与稳定性,达到了转矩脉动最小的优化目的.

Optimum control for switched reluctance motor using iterative learning

A method for designing switched reluctance motor(SRM) optimum controller was established aimed at ripple-free torque control.The theory of iterative learning control(ILC) was used to ensure the astringency and stability of ILC in single-in-single-out(SISO) non-linear system.A mathematical model for SRM was established on the basis of non-linear flux-linkage characteristics.The designed optimum controller was consisted of torque controller and current controller.The torque controller designed using torque share function(TSF) generated the desired current profile according to the desired torque profile via "study";and the current controller made the actual current approach the desired current profile as accurately as possible via "study",which gave rise to ripple-free torque control.The results of computer simulation prove that the steady state error of torque is diminished under the condition of constant torque load,and good astringency and stability performance is shown.