Abstract
Predictive control algorithms are promising also in the case of nonlinear systems. Long-range predictive control algorithms are derived here for the nonlinear Hammerstein model. A quadratic cost function is minimized, which considers the quadratic deviation of the reference signal and the output signal predicted in a future horizon and punishes also the squares of the control increments. The predictive incremental form of the Hammerstein model is used to predict the output signal. Suboptimal versions of the control algorithm are given with different assumptions for the control signal during the control horizon. Some properties of these algorithms are shown through simulation examples. Behaviour of long-range predictive control algorithms is compared with the performance of one-step-ahead predictive control algorithms.
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