Iterative Learning and Fractional Reset Control

Currently, reset control focuses on using structures with new resetting rules to avoid the occurrence of limit cycles and improve the performance of the system. A common problem in reset control is the steady-state error since it has not the same characteristic as the linear integrator, which causes the occurrence of limit cycles in many cases, specially in first order systems. It is shown that most of the reported methods to prevail this problem — resetting to non-zero values — are not robust. This paper investigates a robust solution for such phenomena using fractional order control and iterative learning control (ILC). The proposed controller is able to eliminate the limit cycle in presence of model mismatch and repetitive disturbances. Likewise, an easy way to tune is described. Simulation results are given to demonstrate its applicability and performance robustness of the designed controller is discussed.