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This paper addresses the co-design of event-triggered dynamic output-feedback (DOF) control for discrete-time linear parameter-varying (LPV) systems. Two independent event-triggering schemes are introduced to determine when to transmit data from the sensor to the controller and from the controller to the actuator channels. Enhanced conditions employing linear matrix inequalities (LMIs) are derived to establish constructive criteria for simultaneously designing the gain-scheduled DOF controller and the triggering mechanisms, ensuring the asymptotic stability of the closed-loop LPV system's origin. Additionally, a convex optimisation problem is proposed to enlarge the inter-event sampling on the independent channels of sensor-to-controller and controller-to-actuator. Numerical examples are provided to illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach for resilient event-triggered control for linear parameter-varying (LPV) systems under denial-of-service (DoS) attacks and stochastic deception attacks. The proposed networked control system comprises a gain-scheduled controller and an event-triggering mechanism. Besides that, a scenario where DoS attacks restrict the data rate is investigated. This is done by considering a deterministic DoS attack model that limits the frequency and duration of the DoS attack sequence on average. On the other hand, in the absence of DoS attacks the network may be subject to the occurrence of stochastic deception attacks that follow a given Bernoulli distribution. Furthermore, a switching approach provides new co-design conditions based on linear matrix inequalities (LMIs) to guarantee the mean square exponential stability of the closed-loop system even in the presence of hybrid attacks. It is also proposed an LMI-based optimization strategy to enhance resilience against the considered class of DoS attacks while minimizing the number of transmissions. Finally, a numerical example is provided to illustrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

This article handles the problem of periodic event‐triggered control co‐design for the stabilization of Takagi‐Sugeno fuzzy models subject to stochastic deception attacks whose occurrence follows a given Bernoulli distribution. A novel delay‐dependent condition is presented to simultaneously design the event‐triggering mechanism and the state‐feedback controller to ensure the local mean‐square asymptotic stability of the closed‐loop system. The co‐design condition is derived as linear matrix inequalities by considering Lyapunov–Krasovskii stability arguments. In addition, an optimization procedure is provided to estimate an enlarged set of admissible initial conditions and the corresponding ultimate bound within the validity domain of the fuzzy model. Finally, numerical examples are provided to demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper addresses the dynamic periodic event‐triggered control for local stabilization of nonlinear networked control systems with communication delays. Based on a polytopic quasi‐linear parameter‐varying (quasi‐LPV) model of the nonlinear plant and the Lyapunov–Krasovskii stability theory, a local stability analysis condition is established. Then a constructive co‐design condition is proposed to jointly design the state‐feedback control law and the trigger rule. The local asymptotic stability of the closed‐loop equilibrium point of interest is ensured with a guaranteed region of attraction where trajectories remain inside even in the presence of communication delays. An optimization procedure is proposed to perform the co‐design considering the objectives of enlarging the guaranteed region of attraction and reducing the number of transmissions. Numerical examples indicate a trade‐off between these two objectives. Also, the examples illustrate the effectiveness of the proposed dynamic event‐triggered control co‐design approach over both its static counterpart and periodic time‐triggering mechanisms. [ABSTRACT FROM AUTHOR]

This article addresses the problem of state and unknown inputs (UIs) estimation for nonlinear systems with arbitrary relative degree with respect to the UIs. For this purpose, a novel nonlinear unknown input observer (UIO) is proposed, which is able to decouple the UIs by using the derivatives of the output signal. The error dynamics is attained by an exact handling and a factorization of its gradient to obtain a local polytopic representation suitable for input‐affine nonlinear systems. For that representation, a novel design condition based on convex optimization and linear matrix inequalities is proposed to exponentially stabilize the estimation error and to guarantee the validity of the proposed nonlinear UIO. Numerical simulations indicate the effectiveness of the proposed approach for different classes of nonlinear systems, for which the UIs could be totally decoupled from the state estimation. [ABSTRACT FROM AUTHOR]

This paper introduces a novel local synthesis condition for gain‐scheduling static output‐feedback control of discrete‐time linear parameter‐varying (LPV) systems under actuator saturation and state constraints. The proposed condition is formulated as a set of parameter‐dependent linear matrix inequalities that are incorporated into a convex optimization procedure to provide an enlarged estimate of the region of attraction of the closed‐loop equilibrium. In contrast with related works, the proposed approach allows handling a wider class of LPV systems since no rank condition is required to employ the so‐called equality constraints. [ABSTRACT FROM AUTHOR]

This article proposes a data‐stream‐driven event‐triggered control strategy using evolving fuzzy models learned by granulation of input–output samples of nonlinear systems with unknown time‐varying dynamics. The evolving fuzzy model is obtained online from a data stream ensuring data coverage based on the principle of justifiable granularity and controlled by an event‐triggering learning mechanism dependent on the model accuracy. This evolving fuzzy model is used to design event‐triggered fuzzy controller to stabilize networked control systems while reducing the used communication resources. The event‐triggered learning mechanism is employed to determine the instants in which the event‐triggered fuzzy controller should be redesigned. Numerical examples illustrate the effectiveness of the proposed learning event‐triggered fuzzy control algorithm. [ABSTRACT FROM AUTHOR]