International Journal of Scientific & Engineering Research Volume 4, Issue 1, January-2013 1 ISSN 2229-5518 IJSER © 2013 http://www.ijser.org Fuzzy Control System Review Abdullah J. H. Al Gizi*, M.W. Mustafa, Malik A. Alsaedi, N. Zreen A bstract — Overall intelligent control system which runs on fuzzy, genetic and neural algorithm is a promising engine for large –scale devel- opment of control systems . Its development relies on creating environments where anthropomorphic tasks can be performed autonomously or proac- tively with a human operator. Certainly, the ability to control processes with a degree of autonomy is depended on the quality of an intelligent control system envisioned. In this paper, a summary of published techniques for intelligent fuzzy control system is presented to enable a design engineer choose architecture for his particular purpose. Published concepts are grouped according to their functionality. Their respective performances are com- pared. The various fuzzy techniques are analyzed in terms of their complexity, efficiency, flexibility, start-up behavior and utilization of the controller with reference to an optimum control system condition. Index Terms— Fuzzy, Intelligent Control System ——————————  —————————— 1 INTRODUCTION RESERCH BACKGROUND An intelligent system has the ability to act logically in an uncertain environment to achieve certain be- havioral sub goals which support the system's ultimate goal. Control systems are a key enabling technology for the increase in functionality and safety of many critical applications such as transportation systems, manufacturing systems, medical devices, and networked embedded systems . Modern power systems are non-linear and behave in a highly complex man- ner with continuous extensive variations in their operating conditions. Design of this type of systems requires knowledge in many multi-disciplines. The most popular technique is to use Fuzzy controller in which expert knowledge can be incor- porated into the design. Most of Fuzzy controllers which are used in industry have the same structure as incremental PD or PID controllers. Controller design using Genetic Algorithm and neural network has been combined with Fuzzy controller to form an intelligent control scheme. The first feedback de- vice on record was the water clock invented by the Greek Ktesibios in Alexandria Egypt around the 3rd century B.C . [2] . This was certainly a successful device as water clocks of sim- ilar design were still being made in Baghdad when the Mon- gols captured that city in 1258 A.D. The first mathematical model to describe plant behavior for control purposes is at- tributed to J.C. Maxwell who in 1868 used differential equa- tions to explain instability problems encountered with James Watt's flyball governor; the governor was introduced in 1769 to regulate the speed of steam engine vehicles.[1] . When J.C. Maxwell used mathematical modeling and methods to explain instability problems encountered with James Watt's flyball governor, it demonstrated the importance and usefulness of mathematical models and methods in understanding complex phenomena and signaled the beginning of mathematical sys- tem and control theory. It also signaled the end of the era of intuitive inventions. Control theory made significant strides in the past 120 years, with the use of frequency domain methods and Laplace transforms in the 1930s and 1940s and the devel- opment of optimal control methods and state space analysis in the 1950s and 1960s. Ideas such as optimal control (in the 1950s and 1960s) and stochastic, robust, adaptive and nonline- ar control methods (in the 1960s till today), have made it pos- sible to control complex dynamical systems more accurately than the original flyball governor. A. Scope of this review Owing to recent rising interest in intelligent control sys- tems , it has been necessary to collect and classify these con- trol systems and explain how their control techniques were developed. Despite the increase in the number of papers de- scribing intelligent control techniques, understanding of the application of these techniques among the community of prac- tice is somewhat sketchy. This is because those papers specifi- cally deal only with research works which are aimed at achieving overall intelligent control using the techniques of fuzzy logic.This paper will attempt at classifying intelligent fuzzy control systems according to the control techniques used. There will be a discussion on how their intelligent con- trol can be improved. 2 FUZZY LOGIC CONTROLLERS There are two main types of fuzzy logic based con- troller [5-12]. The first is the madman type fuzzy logic control- ler which is adaptive and where the system to be controlled is not explicitly identified. The second is the Takagi-surgeon type fuzzy logic controller (FLC) which is indirectly adaptive and where the system to be controlled is identified using T-S fuzzy model . The controller is designed based on the identi- fied model. Rule base approach provides a useful framework for the definition of different methods of logic control [13-15]. Controller design using the rule based approach would as- semble three component implementation phases. These are the knowledge acquisition phase, the model development phase and the model testing phase Examples of rule base ————————————————  This work was supported in part by the Universiti Teknologi Malaysia, under MOHE Scheme, GUP Grant No. 01H80  Mohd Wazir Bin Mustafa is with the Faculty of Electrical Engineering, Universiti Teknologi M alaysia, Johor, M alaysia (e-mail: wa- zir@fke.utm.my).  Abdullah J. H. Al Gizi is with the Faculty of Electrical Engineering, Uni- versiti Teknologi Malaysia, Johor, Malaysia (corresponding author 0060102831074; e-mail: abdullh969@ yahoo.com).