未建模误差补偿Elman网络直接广义预测控制

Direct GPC based on elman neural network with dynamic modeling error compensation

针对一类非线性系统,提出了Elman网络直接广义预测控制算法。先将非线性系统等价转换成线性系统,然后对未建模动态误差进行估计,最后利用Elman网络进行预测控制器设计,并根据跟踪误差对控制器参数中的未知向量进行自适应调整,理论证明了误差收敛到原点一个小邻域内。该方法不需要求解Diophantine方程和矩阵求逆,只需要辨识一个参数θ(k),因此减少了在线计算量,提高了实时性。仿真结果验证了该方法的有效性。

Direct Generalized Predictive Cnntrol（GPC） hased on Ehnan neural network for a class of nonlinear system with unknown parameters is presented to overcome the high load of computing of traditional GPC as online recursion of Diophantine equations and matrix inversion.In this method,the nonlinear system is substituted with a time varying linear system,then the dynamic modeling error is estimated,and the controller parameter vector θ is adjusted adaptively,finally an Elman neural network is used to approximate the function of control increment.It ix proved that the proposed method can make the tracking error converge to a little neighborhood of the origin.Simulation resuhs demonstrate the effectiveness of this proposed method.