Soft Switching System Based on Weighted Probabilities for Stochastic Hybrid Multiple Model-based Control Systems

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Stochastic hybrid model-based control refers to controlling uncertain systems, which are modeled as a multiple-model set with a varying variable structure and the use of interacting multiple model (IMM) estimator and generalized predictive control (GPC) algorithm as described in [1]. For a hard switching system, the plant model is determined by the selection of the “most reliable” model in the model set. However as indicated in [2], the hard switching system can be destabilized with some switching sequences even if every model in the model set is globally stabilized. Now we consider the use of a soft switching system where the plant model is formed by the weighted probabilities from several models in the model set. It provides a smoother and smaller offset error in a tracking process. This paper presents some stabilizability conditions for the soft switching signals of continuous and discrete stochastic hybrid model-based control systems.