Adaptive Identification and Control of Dynamical Systems Using Neural Networks

控制动态系统所涉及的基本过程包括系统的数学建模、基于实验数据的识别、输出的处理以及依次使用它们来合成控制输入以实现期望的行为。 自动控制的目的是快速、准确和稳定地实现后者。 具有多输入多输出的已知线性对象的控制问题是一个困难的问题。 当系统的参数未知时，问题变得相当困难，需要自适应控制。 当被控对象是已知的但非线性时，困难更大。 当被控对象是具有未知特性的非线性时，我们就有了一个真正令人生畏的非线性自适应控制问题。 在诸如空间技术、制造和机器人技术等新领域以及诸如过程控制和飞行器控制等已建立的领域中的许多应用中，此类问题正以越来越高的频率出现。 该提案涉及使用神经网络控制此类系统的理论和实践方面。 本文的第一部分将讨论非线性系统的表示问题和控制器的存在性。 在第二部分中，将讨论与选择神经网络作为标识符和控制器相关的问题。 第三部分将讨论稳定性这一重要问题。 第四部分对多模型控制方法进行了详细的研究，近三年来的实证研究表明，为了有效地科普环境的快速变化，需要一种基于多模型的控制方法。 建议的四个部分代表了使用神经网络的非线性自适应控制的四个重要方面。

The fundamental processes involved in controlling a dynamical system include the mathematical modeling of the system, identification based on experimental data, processing of the outputs, and using them in turn to synthesize control inputs to achieve desired behavior. The aim of automatic control is to achieve the latter rapidly, accurately, and in a stable fashion. The problem of controlling a known linear plant with multiple inputs and multiple outputs is a difficult one. When the parameters of such systems are unknown, the problem is considerably more difficult, and adaptive control is needed. The difficulty is substantially grater when the plant is known but nonlinear. When the plant is nonlinear with unknown characteristics, we have a nonlinear adaptive control problem which is truly formidable. In numerous application in new areas such as space technology, manufacturing, and robotics, as well as established areas such as process control and aircraft control, such problems are arising with increasing frequency. The proposal deals with theoretical and practical aspects of the control of such systems using neural networks. The first part of this work will deal with the representation problem of non linear systems and the existence of controller for them. In the second part, questions related to the choice of neural networks as identifiers and controllers will be addressed. The third part will deal with the important question of stability. In the fourth part, a detailed study of control using multiple models will be carried on. Empirical studies carried out in the past three years suggest that such a control methodology based on multiple models is needed to cope efficiently with rapid changes in the environment. The four part of the proposal represents four of the important aspects of nonlinear adaptive control using neural networks.