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Most of the available results of adaptive iterative learning control (AILC) hitherto have considered the control systems with known linearly parameterized structures. A dynamical linearization approach is developed for a general nonlinear multiple input multiple output systems. And then a discrete-time adaptive ILC approach is presented to deal with the ILC problems of nonlinear MIMO systems with...
In this paper, a discrete adaptive ILC is presented for a time-varying nonlinear system with both parametric and nonparametric uncertainties. A novel estimation of parametric and nonparametric uncertainties is constructed just using the past input and output data, and the uncertainties are completely compensated such that the output tracking error is only affected by external disturbance. As a main...
In this work we present a discrete-time adaptive iterative learning control (AILC) scheme to deal with systems with time-varying parametric uncertainties. Using the analogy between the discrete-time axis and the iterative learning axis, the new adaptive ILC can incorporate a Recursive Least Squares (RLS) algorithm, hence the learning gain can be tuned iteratively along the learning axis and pointwisely...
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