ABSTRACT
Neural control is an attractive tool when the model of the plant to be controlled is not well known and input–output data are available in the form of measurements. In such cases, neural networks (NNs) can be used to approximate the plant dynamics as a black box model and a suitable control law can be designed to regulate the system response. Both static and dynamic NNs have been used for controller design and their general approximation and learning abilities make NNs an attractive tool when a mathematical model of the plant is not known. In this chapter, we review several of the commonly used neural control techniques.
