Extended State Observer based Iterative Learning Control for Systems with Nonrepetitive Disturbances

A novel extended state observer (ESO) based iterative learning control (ILC) scheme is investigated, including three channels, namely, feedforward, feedback, and disturbance rejection channels. The goal of this work is to achieve high-accuracy tracking of nonlinear systems in the presence of nonrepetitive disturbances under repetitive operating conditions. The ESO is used to estimate and offset the nonrepetitive disturbance in real time, which reduces the sensitivity of the controller to system parameters and operating environments. The convergence of control scheme are analyzed, and the estimation accuracy of the observer for disturbances with different frequencies is demonstrated. Finally, an implementation to an automatic guided vehicle (AGV) is illustrated to verify the effectiveness of the proposed control scheme.