Ieee Tencon '93 / Bcijnr Defuzzif'ication Method for a Faster and More Accurate Control

Fuzzy control is achieved by formulating a rule-base which is based on experience gathered by human operators Those systems which cannot be modeled mathematically benefit most from fuzzy control strategy since the imprecise data can be captured using linguistic variables in the rule-base. Fuzzy logic has certain disadvantages. The number of computations required for arriving at a certain output response for given inputs is very large and thus the system response is sluggish. Therefore to adapt fuzzy systems to real-time applications we need to use faster algorithms andlor parallel processing.Also the process of defuzzification required to produce a single output value may lead to errors which can undo the advantages of fine control normally achievable using fuzzy logic. Special analytical techniques a ensure that the complications of the defuzzification process are simplified;the method used retains the desirable features of fuzzy control. This paper aims to present a comparison between the conventional fuzzy controller and a fuzzy logic controller based on the techniques mentioned above. EXPERIMENTAL I The problem sought lo be tackled is that of inverted pendulum. In this classical control problem, a pole is attached to a vehicle by a hinge such that from an upright position it can fall only to right or left. The aim is 10 monitor the pole's angular position and speed and move the vehicle left or right accordingly, so as to keep the pole upright. The conventional fuzzy control approach using triangular distribution for primary sets and the suggested approach using parabolic distribution have been implemented in software. The observations from the programs are discussed in this paper.