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We propose an event-triggered control strategy based on a dynamic output-feedback controller for stabilizing discrete-time linear parameter-varying systems. Such a controller has an anti-windup term and is partially dependent on time-varying parameters. Two event generators are introduced on the sensor-to-controller and controller-to-actuator channels to economize the limited network resources. They decide whether the current output measurement and control input should be sent through the network or not. Sufficient conditions in terms of linear matrix inequalities are provided to ensure the regional asymptotic stability of the closed-loop system and estimate its domain of attraction. The transmission activity is indirectly reduced thanks to appropriate optimization procedures. A numerical example testifies the efficiency of the proposed methodology. [ABSTRACT FROM AUTHOR]

We propose a systematic design method to compute an observer-based output feedback controller for fuzzy Takagi–Sugeno (T–S) systems with unmeasurable premise variables, subject to state and control constraints, based on the theory of invariant sets. Sufficient conditions are established for a polyhedron defined in the augmented state-space (state + estimation error) to be positively invariant. From these conditions, a bilinear optimization problem is formulated for the simultaneous computation of the gains of the controller and of a positively invariant polyhedron guaranteeing the satisfaction of the constraints. Numerical examples illustrate the effectiveness of the method. [ABSTRACT FROM AUTHOR]

We propose convex conditions for stabilization of nonlinear discrete-time systems with time-varying delay in states through a fuzzy Takagi-Sugeno (T-S) modeling. These conditions are developed from a fuzzy Lyapunov-Krasovskii function and they are formulated in terms of linear matrix inequalities (LMIs). The results can be applied to a class of nonlinear systems that can be exactly represented by T-S fuzzy models inside a specific region called the region of validity. As a consequence, we need to provide an estimate of the set of safe initial conditions called the region of attraction such that the closed-loop trajectories starting in this set are assured to remain in the region of validity and to converge asymptotically to the origin. The estimate of the region of attraction is done with the aid of two sets: one dealing with the current state, and the other concerning the delayed states. Then, we can obtain the feedback fuzzy control law depending on the current state, xk, and the maximum delayed state vector, xk−d̅. It is shown that such a control law can locally stabilize the nonlinear discrete-time system at the origin. We also develop convex optimization procedures for the computation of the fuzzy control gains that maximize the estimates of the region of attraction. We present two examples to demonstrate the efficiency of the developed approach and to compare it with other approaches in the literature. [ABSTRACT FROM AUTHOR]