Data-Driven High-Order Point-to-Point ILC With Higher Computational Efficiency

For a class of unknown MIMO non-affine nonlinear repetitive discrete-time 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. One includes the high-order error information, the other consists of the control inputs within the time sub-intervals divided by prescribed desired points. The control law is designed by optimizing this function and it comprises only the known control input signals in the current iteration and the error data in previous iterations. Further, the convergence analysis is conducted in a data-driven way and does not need precise mathematical models. In addition, a scalar index function is set up to evaluate the tracking error convergence rate. By choosing the appropriate high-order factor and corresponding step-size factors, the convergence rate of higher-order learning law is shown to have a faster speed than that of lower-order one. Simulation experiments verify the effectiveness and advantage of this method.