Neural networks and fuzzy logic in electrical engineering control courses

International Journal of Electrical Engineering Education, Jan 2003 by Jurado, Francisco, Ca�o, Antonio, Ortega, Manuel

Abstract

Control system education must include experimental exercises that complement the theory presented in lectures. These exercises include modelling, analysis and design of a control system. Key concepts and techniques in the area of intelligent systems and control have been discovered and developed over the past few decades. While some of these methods have significant benefits to offer, engineers are often reluctant to utilise 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.

Keywords fuzzy logic; intelligent control; modelling; neural networks

Undergraduate students in computer science learn best when they are given the opportunity to apply software concepts to real world systems, and intelligent control applications present attractive possibilities for giving them such opportunity. An example of how to take advantage of these possibilities is given in this paper, which describes a specific neural network technique that has been developed and applied to the problem of tuning fuzzy controllers.

To master the technique, the students start by learning about neurons and fuzzy logic, but they soon find themselves 'training' a multilayered neural network that they themselves have built.

The electronic computer has been used extensively in the electric power industry from the moment the computer became commercially available. This was a natural development since the size and complexity of most power system problems make the computer an essential tool for the electric power system analyst and designer.

Electric power system analysis tools, with computational power similar to or better than programs first developed during the 1960s and 1970s, and easy-to-use, attractive, and providing a graphical user interface, are just becoming available.1,2

The main difficulty in a control course is the large amount of mathematical models and equations. The repetitive algebra and the necessity for a complete understanding of the physical concepts embedded in the equations require the use of a suitable computational tool. The introduction of MATLAB in this course is due to its facility to build up mathematical functions and also due to its powerful graphical user interface in order to display the results.

We describe a laboratory exercise required in a control class and the relationship between the exercise and the theory presented in lectures. We conclude with a summary of our experiences with the laboratory exercises and the feedback received from our students and graduates.

Intelligent control

Major concepts and techniques in the area of intelligent systems and control have been discovered and developed over the past few decades.3 While some of these methods have significant benefits to offer, engineers are often reluctant to utilise new intelligent control techniques for several reasons:

1 there has been a lack of rigorous engineering analysis to verify, for example, stability properties and performance characteristics;

2 there is not an established track record for the reliability and robustness of such techniques;

3 there has not been comparative analysis to determine their advantages/disadvantages relative to conventional methods; and

4 the approaches are not widely understood by practising engineers. The relative lack of attention given to the potential of intelligent control is cause for some concern, indicating a definite need for applications-directed research and education in these areas.

Curricula for control engineering programs have undergone substantial change in the past years as modern techniques for analysis and design find their way into these courses. It is quite natural then, that newer technologies such as intelligent control should be introduced into university curricula. Along with the continuously evolving curricula, there remains a constant in control engineering education: the recognised need for laboratory experience in the curricula. More and more examples of high-quality control laboratories are appearing in universities worldwide.4 Moreover, more and more educators recognise the importance of a complete educational experience involving theory and practice.

With these thoughts in mind, it has been our goal to bring the newest technologies into the curricula, through both lecture and laboratory courses. With regard to the treatment of intelligent control in the courses our intent has not been to give an in-depth treatise on the theory of fuzzy sets and neural nets. We found that electrical engineering undergraduates have little difficulty in coming up to speed in the area in a relatively short amount of time.

The graduate students are exposed to the more advanced topics, and at a higher level of sophistication. Several projects in simulation and design analysis are given. The graduate students are required to complete the entire lecture-laboratory course.