Adaptive Identification and Control of Dynamical Systems Using Neural Networks

使用神经网络的动态系统的自适应识别和控制

• 批准号: 9811390
• 负责人: Kumpati Narendra
• 金额: $ 43.63万
• 依托单位: Yale University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1998
• 资助国家: 美国
• 起止时间: 1998-09-15 至 2001-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9811390&HistoricalAwards=false
• 关键词: Adaptive; Identification; Control; Dynamical; Systems

9811390NarendraThe field of control is inherently interdisciplinary in nature and extends from design, development and production on the one hand to mathematics on the other. The objective of control is to influence the behavior of dynamical systems. Achieving fast and accurate control under different environmental conditions, even while assuring stability and robustness, is the aim of all control systems design.The best developed part of control theory deals with linear systems, and most of the controllers used in modern industry are based on linear control principles. When some of the parameters of the system are unknown, we have an adaptive control problem. The complexity of the problem is substantially greater when the plant characteristics are known but distinctly nonlinear, and becomes truly formidable when some of its parameters/functions are unknown or vary with time. Very few methods currently exist for controlling such systems. However, as applications in industry are becoming more complex and the frontiers of technology are being extended, such problems are being encountered with increasing frequency. This is the case both in well established areas such as process control and aircraft control, as well as new areas such as space technology, robotics, and manufacturing. New methods for addressing such problems using neural networks will be studied in this project.This project consists of four parts. The first part deals with some of the important questions related to neural network based identification and control that require further investigation. In the second part, the problem of control based on pattern recognition, and the use of neural networks in optimal control, are studied. The third part re-examines the question of stability of neural network based control systems. The PI believes that the first three parts are essential for a better understanding of the difficulties encountered in nonlinear adaptive control, and that they will set the stage for the fourth and final part, which contains the main thrust of the work. Here, a detailed study will conducted of the problem of controlling both linear and nonlinear dynamical systems using multiple models, when time variations and external perturbations are present.

9811390 Narendra控制领域本质上是跨学科的，从设计，开发和生产延伸到数学。 控制的目的是影响动力系统的行为。 在不同的环境条件下实现快速和精确的控制，甚至在保证稳定性和鲁棒性的同时，是所有控制系统设计的目标。控制理论中发展最好的部分涉及线性系统，现代工业中使用的大多数控制器都是基于线性控制原理。 当系统的某些参数未知时，我们有一个自适应控制问题。 当植物特性是已知的，但明显的非线性，并成为真正的强大时，它的一些参数/功能是未知的或随时间变化的问题的复杂性是相当大的。 目前存在非常少的用于控制这样的系统的方法。 然而，随着工业中的应用变得越来越复杂，技术前沿不断扩展，越来越频繁地遇到这样的问题。 这在诸如过程控制和飞行器控制等成熟领域以及诸如空间技术、机器人和制造等新领域都是如此。 本计画将研究利用类神经网路解决此等问题的新方法。 第一部分涉及一些重要的问题，需要进一步调查的神经网络为基础的识别和控制。 第二部分研究了基于模式识别的控制问题，以及神经网络在最优控制中的应用。 第三部分重新审视基于神经网络的控制系统的稳定性问题。 PI认为，前三部分对于更好地理解非线性自适应控制中遇到的困难至关重要，并且它们将为第四部分也是最后一部分奠定基础，该部分包含工作的主要内容。 在这里，将进行详细的研究问题的控制线性和非线性动力系统使用多个模型，当时间变化和外部扰动。