Research Initiation Award: Real-Time Optimal Control of a Class of Nonlinear Systems Using Neural Networks

研究启动奖：利用神经网络对一类非线性系统进行实时最优控制

• 批准号: 9309486
• 负责人: Levent Acar
• 金额: $ 8.59万
• 依托单位: Missouri University of Science and Technology
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1993
• 资助国家: 美国
• 起止时间: 1993-09-15 至 1999-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9309486&HistoricalAwards=false
• 关键词: Research; Initiation; Award; Real; Time

9309486 Acar As part of this proposal, an optimal controller based on artificial neural networks will be developed for a class of nonlinear systems. The Proposed controller will be capable of adapting and optimizing a receding horizon quadratic cost function in real time for a system which has unknown nonlinearities in its state variables. The complete controller design will consist of various stages of modeling the nonlinearities, estimating future values of the system states, designing a critic for the cost, and learning the optimal control in real time. All these stages of the controller design will involve various types of neural networks which may utilize different forms of supervised learning methods integrated with the control problem. Briefly the objectives of this work are as follows. developing and simulating a real- time optimal controller based on neural networks. Comparing its performance with the (off-line) optimal controller and two other neural network based variations. Obtaining some stability bounds (or attraction regions) of the neural network controlled system experimentally and analytically. Implementing the controller on the two hardware experiments in the Neuro-Controller Laboratory at the University of Missouri-Rolla. ***

9309486 Acar作为本提案的一部分，将开发基于人工神经网络的一类非线性系统的最优控制器。所提出的控制器将能够适应和优化的滚动时域二次成本函数在真实的时间的系统，其中有未知的非线性在其状态 变量 完整的控制器设计将包括对非线性建模、估计系统状态的未来值、设计成本评价以及真实的学习最优控制的各个阶段。 控制器设计的所有这些阶段将涉及各种类型的神经网络，这些神经网络可以利用与控制问题集成的不同形式的监督学习方法。 这项工作的目标简述如下。 开发并仿真了一种基于神经网络的真实的实时最优控制器。 比较其性能与（离线）最优控制器和其他两个基于神经网络的变化。 通过实验和解析的方法得到了神经网络控制系统的稳定界（或吸引域）。 在Missouri-Rolla大学的神经控制器实验室的两个硬件实验上实现控制器。 ***