Fuzzy Control System Performance Enhancement by Iterative Learning Control

This paper suggests low-cost fuzzy control solutions that ensure the improvement of control system (CS) performance indices by merging the benefits of fuzzy control and iterative learning control (ILC). The solutions are expressed in terms of three fuzzy CS (FCS) structures that employ ILC algorithms and a unified design method focused on Takagi-Sugeno proportional-integral fuzzy controllers (PI-FCs). The PI-FCs are dedicated to a class of servo systems with linear/linearized controlled plants characterized by second-order dynamics and integral type. The invariant set theorem by Krasovskii and LaSalle with quadratic Lyapunov function candidates is applied to guarantee the convergence of the ILC algorithms and enable proper setting of the PI-FC parameters. The linear PI controller parameters tuned by the extended symmetrical optimum method are mapped onto the PI-FC ones by the modal equivalence principle. Real-time experimental results for a dc-based servo speed CS are included.