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首页> 外文期刊>Neural Networks: The Official Journal of the International Neural Network Society >A single network adaptive critic (SNAC) architecture for optimal control synthesis for a class of nonlinear systems.
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A single network adaptive critic (SNAC) architecture for optimal control synthesis for a class of nonlinear systems.

机译：用于一类非线性系统的最优控制综合的单网络自适应批评家（SNAC）体系结构。

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摘要

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.
机译：即使动态编程以状态反馈的形式提供了最佳的控制解决方案，该方法也被计算和存储需求所淹没。用自适应关键点（AC）神经网络结构实现的近似动态编程已发展成为一种强大的替代技术，它消除了解决最佳控制问题时对过多计算量和存储量的需求。在本文中，提出了一种对AC体系结构的改进，称为“单网络自适应评论家（SNAC）”。这种方法适用于各种各样的非线性系统，其中可以根据状态变量和代价变量明确表示最优控制（平稳）方程。该术语的选择是基于这样的事实，即它消除了使用作为典型双网络AC设置一部分的一个神经网络（即动作网络）的事实。结果，SNAC体系结构提供了三个潜在的优势：体系结构更简单，计算负荷更少以及消除了与消除后的网络相关的近似误差。为了证明这些优点和使用SNAC的控制综合技术，AC和SNAC方法解决了两个问题，并比较了它们的计算性能。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。

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