Iterative extended state observer based data driven optimal iterative learning control

In this work, an iterative extended state observer based data-driven optimal iterative learning control is proposed for a class of nonlinear non-affine discrete-time system with exogenous disturbances and operated repetitively over a finite time interval. First, a modified iterative dynamic linearization method is proposed to linearize the controlled system into an affine form related to control input, where the uncertainties are incorporated into a nonlinear term; second, an iterative extended state observer is developed to estimate the nonlinear uncertainty term as a compensation for the disturbances; finally, both a parameter iterative updating law and an optimal learning control law are proposed via the optimization technique by designing two objective functions. The bounded convergence of tracking error is proved rigorously through mathematical analysis. Simulation results have been provided to verify the effectiveness of the proposed method. The proposed new iterative dynamic linearization method can reduce the dynamic complexity of the linearized control gain greatly such that it easy to be estimated. The proposed iterative extended state observer can learn from repetitions, and thus estimate the non-repetitive disturbances effectively. Moreover, the controller design and analysis in this work are data driven, depending on the input-and-output data only without using other explicit model information. © 2018, Editorial Department of Control Theory & Applications South China University of Technology. All right reserved.