An adaptive version of a novel robust predictive control for a class of non-linear systems is presented. The non-linear system is separated into linear and non-linear parts by Taylor series expansion and then the latter part is identified by a neural network, which is then compensated in the control algorithm such that feedback linearization can be achieved. Thus the influence of the non-linearity and model uncertainties may be eliminated or reduced. In the case of time-varying or unknown systems the linear part of the system model is estimated by an RLS (recursive least-squares) algorithm. Simulation results show that the proposed scheme may improve the system performance.
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