Fuzzy iterative learning control with application

Control applications have become important for accuracy and high precision in industrial systems with the developing technology. By eliminating the deficiencies of classical controllers with modern control methods, it has provided the systems to work in desired performance specifications. In this study, a fuzzy PID-type iterative learning control method was developed and a real-time experimental application of DC motor speed control was made. Alternatively, adaptive fuzzy PID-type iterative learning control has been developed. The methods are created by combining the adaptive method, fuzzy logic control and iterative learning control. The proportional, integral and derivative (KP, KI, KD) gains of the PID controller are adjusted according to fuzzy logic. The fuzzy logic controller is developed according to fuzzy rules, thus ensuring the system is fundamentally robust. Fuzzy rules are used to adjust each PID parameter. Adaptive method is used for adaptation of fuzzy logic control input and one of PID parameters. There are two adaptive algorithms in this system. The algorithms helped to adjust PID parameters. In the iterative learning controller (ILC) part, a new control signal is generated by using the PID parameters generated from the fuzzy logic controller and the previously generated control signal. With this method applied on the DC motor, the transient response, tracking response and disturbance reduction situations were examined. The results show that adaptive fuzzy PID-type ILC method has better time domain characteristics and gives better DC motor performance. Keywords: Iterative Learning Control, ILC, Fuzzy Logic, Adaptive, PID