Name
Abstract | ||
|---|---|---|
A self-tuning expert fuzzy controller has been developed and applied in real time to a process control problem. As in other expert systems, the knowledge base consists of rules describing the control law in terms of the process error and the resulting control action. Conditions and conclusions of each rule are fuzzy variables which are described through their membership curves. The inference engine used is the backward chaining process of the Prolog language. To implement the self-tuning property, the membership curve of the controller output has been changed according to an error based performance index. A control supervisor makes this tuning decision as a function of past or predicted future set-point errors of the control system. To verify the viability of this fuzzy controller, it has been applied to control the speed of a DC motor operating under different loading conditions. The paper also discusses the stability problems associated with this control scheme. |
Year | DOI | Venue |
|---|---|---|
1989 | 10.1145/66617.66646 | IEA/AIE (1) |
Keywords | Field | DocType |
chaining process,fuzzy control technique,membership curve,fuzzy controller,control law,control system,control scheme,control supervisor,controller output,resulting control action,process control problem,self tuner,process control,real time,expert system,dc motor,fuzzy control,knowledge base,performance index | Data mining,Control theory,Fuzzy classification,Defuzzification,Computer science,Control theory,Fuzzy set operations,Fuzzy logic,Automatic control,Real-time computing,Fuzzy control system,Fuzzy number | Conference |
ISBN | Citations | PageRank |
0-89791-320-5 | 1 | 0.39 |
References | Authors | |
3 | 1 | |