Neural networks for control

Neural Network Control of Robot Manipulators
I Xplore - Neural Networks For Control: An Overview In this article, the author describes neural network controllers for robot manipulators in a variety of applications, including position control, force control, parallel-link mechanisms, and digital neural network control. These "model-free" controllers offer a powerful and robust alternative to adaptive control.
In recent years there has been increasing interest in universal model-free controllers. Mimicing the functions of human processes, these controllers learn about the systems they are controlling on-line, and thus automatically improve their performance. So far, neural networks have made their mark in the areas of classification and pattern recognition; with this success they've become an important tool in the repertoire of the signal processor and computer scientist. However, the same cannot be said for neural networks in system theory applications.
There has been a good deal of research on the use of neural networks for control, although most of the articles have been ad hoc discussions lacking theoretical proofs and repeatable design algorithms. As a result, neither the control systems community nor US industry have fully accepted neural networks for closed-loop control applications. In this article, I address the major problems facing neural network control


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