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研究了一种带有CMAC神经网络的再励学习 (RL)控制方法 ,以解决具有高度非线性的系统控制问题 .研究的重点在于算法的简化以及具有连续输出的函数学习上 .控制策略由两部分构成 :再励学习控制器和固定增益常规控制器 .前者用于学习系统的非线性 ,后者用于稳定系统 .仿真结果表明 ,所提出的控制策略不仅是有效的 ,而且具有很高的控制精度 .
In order to solve the problem of highly nonlinear system control, this paper studies a Re-excitation learning (RL) control method with CMAC neural network.The research focuses on the simplification of algorithms and the function learning with continuous output.The control strategy consists of two It is composed of a re-learning controller and a fixed-gain controller, the former is used to study the nonlinearity of the system and the latter is used to stabilize the system.The simulation results show that the proposed control strategy is not only effective but also has a high control precision .