A single network adaptive critic (SNAC) architecture for optimal control synthesis for a class of nonlinear systems
A single network adaptive critic (SNAC) architecture for optimal control synthesis for a class of nonlinear systems
复制标题
DOI：
10.1016/j.neunet.2006.08.010
复制
发表时间：
2006-12
期刊：
影响因子：
--
通讯作者：
R. Padhi;N. Unnikrishnan;Xiaohua Wang;S. Balakrishnan
中科院分区：
文献类型：
--
作者：
R. Padhi;N. Unnikrishnan;Xiaohua Wang;S. Balakrishnan
尽管动态规划以状态反馈形式提供了最优控制解决方案，但该方法仍因计算和存储要求而不堪重负。使用自适应批评 (AC) 神经网络结构实现的近似动态规划已发展成为一种强大的替代技术，在解决最优控制问题时无需过多的计算和存储需求。在本文中，提出了一种对 AC 架构的改进，称为“单网络自适应批评家（SNAC）”。这种方法适用于各种非线性系统，其中最优控制（稳态）方程可以用状态变量和共状态变量明确表示。这一术语的选择是基于这样一个事实：它消除了作为典型双网络 AC 设置一部分的一个神经网络（即动作网络）的使用。因此，SNAC 架构提供了三个潜在优势：更简单的架构、更少的计算负载以及消除与消除网络相关的近似误差。为了证明这些优点以及使用 SNAC 的控制综合技术，使用 AC 和 SNAC 方法解决了两个问题，并对它们的计算性能进行了比较。这些问题之一是现实生活中的微机电系统 (MEMS) 问题，这表明 SNAC 技术适用于复杂的工程系统。
Even though dynamic programming offers an optimal control solution in a state feedback form, the method is overwhelmed by computational and storage requirements. Approximate dynamic programming implemented with an Adaptive Critic (AC) neural network structure has evolved as a powerful alternative technique that obviates the need for excessive computations and storage requirements in solving optimal control problems. In this paper, an improvement to the AC architecture, called the “Single Network Adaptive Critic (SNAC)” is presented. This approach is applicable to a wide class of nonlinear systems where the optimal control (stationary) equation can be explicitly expressed in terms of the state and costate variables. The selection of this terminology is guided by the fact that it eliminates the use of one neural network (namely the action network) that is part of a typical dual network AC setup. As a consequence, the SNAC architecture offers three potential advantages: a simpler architecture, lesser computational load and elimination of the approximation error associated with the eliminated network. In order to demonstrate these benefits and the control synthesis technique using SNAC, two problems have been solved with the AC and SNAC approaches and their computational performances are compared. One of these problems is a real-life Micro-Electro-Mechanical-system (MEMS) problem, which demonstrates that the SNAC technique is applicable to complex engineering systems.