On Iterative Learning Control for Nonlinear Time-varying Systems with Input Saturation

Input saturation is inevitable in many engineering systems. In this note, the focus is to design the proper iterative learning control algorithms when the desired trajectory cannot be realized within the saturation bound. Two new algorithms are proposed. First algorithm introduces a kind of “reference governor” which reduces the amplitude of the desired trajectory systematically such that the modified desired trajectory becomes realizable. The second algorithm employs some “barrier” functions to prevent the control input violating the hard input constraint. Simulation example and discussion are provided with some insights on how the proposed iterative learning algorithms work.