基于ADACD的新型辨识器及其模型参考自适应控制

将控制依赖自适应评估设计引入到非线性系统的辨识中,以寻求最佳模型.定义一个总评估函数表示系统在所历经时间内的辨识总误差,然后构造一个评估网络来近似逼近这个总评估函数.再构造一个辨识器网络,其输出直接作为评估网络的输入,这样通过最小化评估网络的输出就可以达到寻求最佳模型的目的.辨识器的参数修正原则不再是使当前时刻的辨识误差最小化,而是使评估网络的输出最小化,即使系统在所历经时间内的近似辨识总误差最小化,这样不仅大大加快了收敛速度而且取得了更加精确的辨识效果.在获得对象模型之后,还研究了利用神经网络设计模型参考自适应控制器的方法.仿真结果表明,利用这种新型辨识器设计的模型参考自适应控制器能够保证被...

Novel Identifier Based on ADACD and Model Reference Adaptive Controller

The action-dependent adaptive critic design(ADACD) is introduced into the identification of nonlinear systems to obtain the best model.A total evaluation function is defined to express the total identification error of a system within the time it has experienced,then a critic network is constructed to approximate to this evaluation function.And an identifier network is further constructed taking its output as the critic network input.In such a way the best model can be obtained by minimizing the critic network output.Here the parameter adaptation rule is based on minimizing the critic network output,i.e.,the total identification error of a system within the time it has experienced,instead of minimizing the instant error.As a result,the convergence rate is quickened remarkably with more accurate identifiability.After obtaining the model of the system,the design of the model reference adaptive controller(MRAC) for this model is studied by using neural networks.The simulation results showed that the performance of this novel identifier is excellent,and the MRAC based on it can make sure that a system to be controlled tracks the reference model quickly and stably.