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Model-Based Event-Triggered Implementation of Hybrid Controllers Using Finite-Time Convergent Observers

In this paper, we explore the conditions for asymptotic stability of the hybrid closed-loop system resulting from the interconnection of a nonlinear plant, an intelligent sensor that generates finite-time convergent estimates of the plant state, and a controller node that receives opportunistic samples from the sensor node when certain model-based event-triggering conditions are met. The proposed method is endowed with a degree of separation, in the sense that the controller design is independent of the sensor design. This is achieved under mild regularity conditions imposed on the hybrid closed-loop system and the existence of persistently flowing solutions. We demonstrate the versatility of the method by implementing it on: 1) a sampled-data controller for regulation of linear plants; 2) a synergistic controller for attitude stabilization of rigid bodies. The effectiveness of these novel controllers is demonstrated through numerical simulations.

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