Robust iterative learning control for random switched singular systems in time domain

Abstract

A robust iterative learning control algorithm is proposed for continuous-time switched singular systems disturbed by random measurement noise. Firstly, the switched singular systems is transformed into a differential-algebraic equation composed of slow subsystem and fast subsystem by nonsingular transformation. Then, the control input is continuously modified by the state measurement errors and the learning gain with attenuation characteristics. The convergence of each subsystem is strictly proved in theory, and the sufficient conditions for the convergence of this algorithm are given. Theoretical analysis shows that the algorithm can effectively suppress the measurement noise and make the system state completely track the desired trajectory in a finite time interval. Finally, compared with the fixed gain iterative learning control method, the simulation results show that the tracking accuracy of the proposed algorithm is significantly improved.