Experiences with fuzzy logic and neural networks in a control course

Control system education must include experimental exercises that complement the theory presented in lectures. These exercises include modeling, analysis, and design of a control system. Key concepts and techniques in the area of intelligent systems and control were discovered and developed over the past few decades. Although some of these methods have significant benefits to offer, engineers are often reluctant to utilize new intelligent control techniques for several reasons. In this paper, fuzzy logic controllers have been developed using speed and mechanical power deviations, and a neural network has been designed to tune the gains of the fuzzy logic controllers. Student feedback indicates that theoretical developments in lectures on control systems were only appreciated after the laboratory exercises.