Abstract
In this paper, an adaptive generalized predictive control (GPC) strategy based on the just-in-time learning (JITL) technique is developed. In the proposed controller design, process nonlinearities are accounted for by the set of local models obtained on-line by the JITL technique and the optimal control actions are obtained by solving the quadratic optimization problem formulated in the GPC design framework. Simulation results are presented to illustrate the advantage of the proposed GPC design and a comparison with its conventional counterparts is made.
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