Accelerated predictive norm-optimal iterative learning control

This paper proposes a novel technique for accelerating the convergence of the previously published predictive norm-optimal iterative learning control (NOILC) methodology. The basis of the results is a formal proof that the predictive NOILC algorithm is equivalent to a successive projection algorithm between linear varieties in a suitable product Hilbert space. This leads to two proposed accelerated algorithms together with well-defined convergence properties. The results show that the proposed accelerated algorithms are capable of ensuring monotonic error norm reductions and can outperform predictive NOILC by more rapid reductions in error norm from iteration to iteration. In particular, examples indicate that the approach can improve the performance of predictive NOILC for the problematic case of non-minimum phase systems. Realization of the algorithms is discussed and numerical simulations are provided for comparative purposes and to demonstrate the numerical performance and effectiveness of the proposed methods.