Stable Hybrid Control Based on Discrete-Event Automata and Receding-Horizon Neural Regulators

A hybrid control scheme for nonlinear discrete-time systems is addressed. Such a scheme is composed of two control levels: a continuous level characterized by a finite set of neural receding horizon feedback control laws, and a discrete-event level aimed at choosing the best control action to be applied to the plant, depending on the current system conditions and on external events that may have occurred. The two-level scheme presents two major innovative aspects: first, a new class of hybrid automata, namely, discrete-time discrete-event automata, is used for the modeling of the proposed hybrid control scheme. Secondly, receding-horizon regulators are used that are based on neural approximators, at the continuous level. In the paper, the stability analysis of the proposed hybrid control system is carried out, and its practical applicability is shown for a case study relevant to traffic control on freeways. The example is very significant, if one takes into account the complexity of the considered transportation system and also the fact that the reported simulation results are based on real-traffic data, and hence they well represent critical traffic conditions on freeways.