A fuzzy expert system for complex closed-loop control: a non-mathematical approach

A fuzzy expert system uses fuzzy logic control (Zadeh, 1965) which is based an a 'superset' of Boolean logic: that has been extended to handle the concept of 'partial truth' and it replaces the role of a mathematical model with another that is built from a number of rules with fuzzy variables such as output temperature and fuzzy terms such as 'hot', fairly cold', 'probably correct'. In control areas, an expert system using fuzzy logic principals has advantages over conventional Proportional-Integral-Derivative (PID) dosed-loop control such as the ability to offer a method for representing and implementing the expert's knowledge, the ability to implement simple but robust solutions as well as comparatively less development and maintenance time of the involved control system. A fuzzy expert system may further outperform conventional PID control system when dealing with 'multiple-input multiple-output' control situations where more complex control algorithms for conventional PID system are required. This paper presents a methodology for the implementation of such a fuzzy expert system with a step-by-step, non-mathematical approach, which is able to handle complex closed-loop central situations. A practical example is employed to illustrate the 'road-map' of the methodology with realistic data based an the implementation of this system in a local company.