IEEE Trans Neural Netw Learn Syst. 2015 Apr;26(4):851-65. doi: 10.1109/TNNLS.2015.2399020. Epub 2015 Feb 26.
In industrial process control, there may be multiple performance objectives, depending on salient features of the input-output data. Aiming at this situation, this paper proposes multiple actor-critic structures to obtain the optimal control via input-output data for unknown nonlinear systems. The shunting inhibitory artificial neural network (SIANN) is used to classify the input-output data into one of several categories. Different performance measure functions may be defined for disparate categories. The approximate dynamic programming algorithm, which contains model module, critic network, and action network, is used to establish the optimal control in each category. A recurrent neural network (RNN) model is used to reconstruct the unknown system dynamics using input-output data. NNs are used to approximate the critic and action networks, respectively. It is proven that the model error and the closed unknown system are uniformly ultimately bounded. Simulation results demonstrate the performance of the proposed optimal control scheme for the unknown nonlinear system.
在工业过程控制中，可能存在多个性能目标，具体取决于输入-输出数据的显著特征。针对这种情况，本文提出了多个演员-评论家结构，通过输入-输出数据为未知非线性系统获得最优控制。分流抑制人工神经网络（SIANN）用于将输入-输出数据分类到几个类别之一。不同的性能度量函数可以为不同的类别定义。近似动态规划算法，包含模型模块、批评者网络和动作网络，用于在每个类别中建立最优控制。递归神经网络（RNN）模型用于使用输入-输出数据来重建未知系统动态。神经网络分别用于近似批评家和动作网络。证明了模型误差和封闭的未知系统是一致最终有界的。仿真结果验证了所提出的未知非线性系统最优控制方案的性能。
