IEEE Trans Neural Netw Learn Syst. 2022 Sep;33(9):4840-4850. doi: 10.1109/TNNLS.2021.3061414. Epub 2022 Aug 31.
This article intends to address an online optimal adaptive regulation of nonlinear discrete-time systems in affine form and with partially uncertain dynamics using a multilayer neural network (MNN). The actor-critic framework estimates both the optimal control input and value function. Instantaneous control input error and temporal difference are used to tune the weights of the critic and actor networks, respectively. The selection of the basis functions and their derivatives are not required in the proposed approach. The state vector, critic, and actor NN weights are proven to be bounded using the Lyapunov method. Our approach can be extended to neural networks with an arbitrary number of hidden layers. We have demonstrated our approach via a simulation example.
本文旨在使用多层神经网络 (MNN) 解决仿射形式和部分不确定动态的非线性离散时间系统的在线最优自适应调节问题。演员-评论家框架估计最优控制输入和价值函数。瞬时控制输入误差和时间差分分别用于调整评论家网络和演员网络的权重。在提出的方法中不需要选择基函数及其导数。使用李雅普诺夫方法证明了状态向量、评论家、演员神经网络权重是有界的。我们的方法可以扩展到具有任意数量隐藏层的神经网络。我们通过一个仿真示例展示了我们的方法。
