感应电机的神经网络自适应L2鲁棒控制

针对感应电机控制中存在的参数不确定性,基于反步法(backstepping)设计了感应电机的神经网络自适应L2鲁棒控制器,提出了控制器和一个转子磁链观测器联用,考虑了磁链估计误差.控制器用径向基函数神经网络(RBFNN)补偿定、转子电阻,及负载转矩和磁链估计误差的不确定性.根据HJI(hamilton-jaccobi-issacs)不等式证明了该控制系统的鲁棒性和稳定性,避免了直接解HJI不等式.仿真结果表明,提出的控制方法对于所考虑的不确定性是鲁棒的,对转速和转子磁链参考信号跟踪精确度高,不必假设所有的状态变量可测量,适用于高性能的感应电机控制系统.

Neural network adaptive L2 robust control of induction motors

To deal with the parameter uncertainties of induction motor in its control system,a neural network adaptive L2 robust control method is proposed based on backstepping,and the proposed controllers are combined with a rotor flux observer and the rotor flux estimation error is considered.The uncertainties of stator and rotor resistances and load torque are compensated by using radial basis function neural networks(RBFNNs).The control system is proved to be robustness and stable by using HJI(hamilton-jaccobi-issacs) inequality without saluting it directly.The simulation results indicate that the proposed control method is robust to the considered uncertainties,able to trace the speed and rotor flux reference signals accurately,and do not need all the state variables are detectable,so it satisfies the requirement of high performance induction motor control system.