TABLE OF CONTENTS Current control methodologies can generally be divided into model based and model free. The first contains conventional controllers, the second so-called intelligent controllers [1] [3]. Conventional control designs involve constructing dynamic models of the target system and the use of mathematical techniques to derive the required control law. Therefore, when a mathematical model is difficult to obtain, either due to complexity or the numerous uncertainties inherent in the system, conventional techniques are less useful. Intelligent control may offer a useful alternative in this situation.
An intelligent system learns from experience. As such, intelligent systems are adaptive. However, adaptive systems are not necessarily intelligent. Key features of adaptive control systems are that they
continuously identify the current state of the system
compare the current performance to the desired one and decide whether a change is necessary to achieve the desired performance
modify or update the system parameters to drive the control system to an optimum performance.
These three principles, identification, decision and modification, are inherent in any adaptive system. A learning system on the other hand is said to be intelligent because it improves its control strategy based on past experience or performance. In other words, an adaptive system regards the current state as novel (i.e. localisation), whereas a learning system correlates experience gained at previous plant operating regions with the current state and modifies its behaviour accordingly [4] for a...
