Intelligent Control : An Overview of Techniques ∗

Intelligent control achieves automation via the emulation of biological intelligence. It either seeks to replace a human who performs a control task (e.g., a chemical process operator) or it borrows ideas from how biological systems solve problems and applies them to the solution of control problems (e.g., the use of neural networks for control). In this chapter we will provide an overview of several techniques used for intelligent control and discuss challenging industrial application domains where these methods may provide particularly useful solutions. This chapter should be viewed as a resource for those in the early stages of considering the development and implementation of intelligent controllers for industrial applications. It is impossible to provide the full details of a field as large and diverse as intelligent control in a single chapter. Hence, here the focus is on giving the main ideas that have been found most useful in industry. Examples of how these methods have been used are given, and references for further study are provided. The chapter will begin with a brief overview of the main (popular) areas in intelligent control which are fuzzy control, neural networks, expert and planning systems, and genetic algorithms. In addition, complex intelligent control systems, where the goal is to achieve autonomous behavior, will be summarized. In each case, applications will be used to motivate the need for the technique. Moreover, we will explain in broad terms how to go about applying the methods to challenging problems. We will summarize the advantages and disadvantages of the approaches and discuss comparative analysis with conventional control methods. ∗Chapter in: T. Samad, Ed., “Perspectives in Control: New Concepts and Applications,” IEEE Press, NJ, 2001.