A Direct Parameter-Error Codesign Approach of Discrete-Time Saturated LPV Systems

Abstract

Considering a discrete-time linear parametervarying (LPV) systems with input constraints, the paper deals with the co-design of a parameter-dependent statefeedback controller and a new parameter-dependent eventtriggering mechanism. Two independent event-triggering policies are introduced at the sensor node to economize the limited network resources. They indicate whether the current state or the current scheduling parameters should be transmitted to the controller or not. In this sense, the controller scheduling parameters can differ from those of the system, yielding a certain degree of robustness concerning parameter deviations. Sufficient conditions, given in terms of linear matrix inequalities (LMIs), ensure the regional asymptotic stability of the closed loop. To formulated them, we use the Lyapunov theory along with the generalized sector condition. Moreover, we formulate a convex optimization procedure indirectly reducing the transmission activity over the network. The proposed methodology effectiveness is attested through numerical examples and comparisons with related works from the literature.

Nonstandard anti-windup approach for event-triggered control purpose

“…Over the years, several kinds of event-triggering policies have been proposed in the literature. Among them, the most commonly used consists of a static rule (see, for example, [10,26,12,5]). By adding an internal dynamic variable into the previous one, we have an augmented rule with dynamics (see, for example, [7,22]).…”

Event-triggered neural network control using quadratic constraints for perturbed systems

“…As a consequence, the ETC is capable of reducing the execution rate of control tasks while guaranteeing suitable properties for the system. Recently, various mechanisms have been proposed for various systems in the literature [4,11,32,1]. In the context of neural networks, the event-triggering technique has been used mostly to transmit states during the learning process.…”