Research On Data-driven Point-to-point Iterative Learning Control Metho

Based on iterative dynamic linearization technology,this paper studies two datadriven point-to-point iterative learning control schemes for unknown repetitive non-affine nonlinear single input single output(SISO)/multiple input multiple output(MIMO)discrete-time systems.Further,for the point-to-point iterative learning control scheme designing of the MIMO systems,high-order learning law technology is introduced,which also urges us to establish an index function to quantitatively analyze the influence of different-order learning laws on convergence rate.The main work of this paper is summarized as follows.1.Integrating the idea of predictive control and point-to-point iterative learning control,this paper presents a data-driven predictive point-to-point iterative learning control scheme for a class of unknown repetitive SISO non-affine nonlinear discrete-time systems.The tracking task is driven by the optimal control input sequence generated by the proposed algorithm,and the tracking errors at the specified sampling time instants are minimized.The advantage of this scheme is that the controller framework and its stability analysis only depend on the I/O data generated by the closed-loop systems,and since the proposed scheme does not involve the operation of matrix inversion,it will not produce huge computational pressure when the dimension of the prediction matrix is too large.2.For a class of unknown repetitive MIMO non-affine nonlinear repetitive discretetime systems,a novel data-driven high-order point-to-point iterative learning control scheme is proposed.The control input objective function of this method consists of two parts.Part of it is the high-order error information.The other is the control input increments within the time sub-intervals divided by prescribed desired points.The control law is designed by optimizing this function and it consists of only the known control input signals in the current iteration and the error data in previous iterations.Further,based on the contraction mapping principle and equivalent iterative dynamic linearization data model,the convergence of the point-to-point tracking error of the proposed scheme and the bounded-input bounded-output(BIBO)stability of the closed-loop system are proved.3.Based on the convergence analysis and stability analysis of the DDHOPTPILC scheme,a scalar index function is proposed to evaluate the convergence rate of point-topoint tracking error for the proposed data-driven high-order point-to-point iterative learning controller.By rigorous mathematical analysis,it is proved that the high-order factor and step factor satisfying certain conditions can ensure that the convergence rate of the high-order point-to-point iterative learning controller is faster than that of the loworder controller.