This article presents a general methodology for the design of adaptive control systems which can learn to operate efficiently in dynamical environments possessing a high degree of uncertainty. Multiple models are used to describe the different environments and the control is effected by switching to an appropriate controller followed by tuning or adaptation. The study of linear systems provides the theoretical foundation for the approach and is described first. The manner in which such concepts can be extended to the control of nonlinear systems using neural networks is considered next. Towards the end of the article, the applications of the above methodology to practical robotic manipulator control is described.

中文翻译：

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[25年前]

本文介绍了一种自适应控制系统设计的通用方法，该方法可以学习在具有高度不确定性的动态环境中有效运行。使用多个模型来描述不同的环境，并通过切换到适当的控制器，然后进行调整或调整来实现控制。线性系统的研究为该方法提供了理论基础，并首先进行介绍。接下来考虑将这样的概念扩展到使用神经网络来控制非线性系统的方式。在本文结尾处，描述了上述方法在实际机器人操纵器控制中的应用。