Adaptive ILC methods with less adaption parameters for non-parameterized nonlinear continuous systems with nonsingular control gain matrices

In this article, for non-parameterized nonlinear continuous (NPNC) multiple-input multiple-output (MIMO) systems, two combined iteration-domain and time-domain adaptive iterative learning control (ILC) algorithms are proposed to track iteration-varying reference trajectories repetitively over a finite time interval. Different from the general requirement in adaptive control community that the control gain matrices of the controlled systems are real symmetric and positive-definite, only the nonsingular property of the control gain matrices is assumed. Moreover, there are just two adaption parameters and one adaption parameter involved in the proposed two adaptive ILC algorithms respectively such that the computation load and memory-space are greatly saved. A simulation example is utilized to illustrate the effectiveness of the two proposed adaptive ILC algorithms with less adaption parameters.