Event-triggered Dynamic Output Feedback Control Research Articles

Event-triggered dynamic output-feedback control for LPV systems

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.

Parallel event-triggered dynamic output feedback control for nonlinear networked systems with randomly occurring multiple communication delays

This paper investigates the issue of parallel event-triggered (PET) dynamic output feedback control for networked control systems (NCSs) built by the discrete-time T–S fuzzy model. Initially, a novel PET dynamic output feedback controller is designed. Based on saving network resources and enhancing transmission efficiency, the PET strategy makes full use of relative and absolute triggering condition information. And the dynamic output feedback control can not only address unmeasurable states but also provide a better response to the internal information of the system. The random multiple communication delays and the ℓth-order Rice fading model with different channel coefficients, meanwhile, are both applied in the system. It is closer to the actual situation. Subsequently, new sufficient conditions of membership function dependence are proposed via the staircase function approximation method combined with Lyapunov stability. It guarantees that the system is exponentially mean square stable (EMSS) with H∞ performance. Ultimately, the presented results are validated using two examples. In the future, we will explore the correlative research of T–S fuzzy Markov jump NCSs.

Improved Event-Triggered Dynamic Output Feedback Control for Networked T-S Fuzzy Systems With Actuator Failure and Deception Attacks.

This article addresses the problem of event-triggered dynamic output feedback controller design for networked Takagi-Sugeno (T-S) fuzzy systems subject to actuator failure and deception attacks. In order to save network resources effectively, two event-triggered schemes (ETSs) are introduced to test whether the measurement output and control input are transmitted under network communication. While the ETS brings advantages, it also leads to a mismatch between the premise variables of the system and the controller. To solve this problem, an asynchronous premise reconstruct method is considered, which relaxes the condition of the previous results that the premises of the plant and the controller are synchronous. Furthermore, two crucial factors, including actuator failure and deception attacks, are taken into consideration simultaneously. Then, the mean square asymptotic stability conditions of the resultant augmented system are derived by utilizing the Lyapunov stability theory. Besides, controller gains and event-triggered parameters are co-designed with the help of linear matrix inequality techniques. Finally, a cart-damper-spring system and a nonlinear mass-spring-damper mechanical system are presented to verify the theoretical analysis.

Resilient Event-Triggered Dynamic Output Feedback Control for Networked Takagi–Sugeno Fuzzy Systems Under Denial-of-Service Attacks

Resilient Event-Triggered Dynamic Output Feedback Control for Networked Takagi–Sugeno Fuzzy Systems Under Denial-of-Service Attacks

Periodic Event-Triggered Dynamic Feedback Synchronization Control of Discrete-Time Neural Networks.

This article investigates the event-triggered synchronization control problem of discrete-time neural networks (DNNs) in the case of periodic sampled-data. A discrete-time periodic event-triggered mechanism is adopted to evaluate the measurements, which avoids formulating the triggering function in a continuous manner and saves energy consumption. Under this framework, an event-triggered dynamic output-feedback controller is designed to achieve the goal of synchronization. A piecewise Lyapunov functional is constructed to analyze the sawtooth-like pattern of sampled-error signals. Thereafter, the synchronization criteria are formulated for the considered DNNs. The co-designed issue is further discussed for the control gains and triggering parameter. Finally, a simulation example is presented to show the effectiveness of the proposed method.

Periodic event-triggered dynamic output feedback control for networked control systems subject to packet dropouts

This paper proposes a co-design method of dynamic output feedback control law and periodic event-triggered control (PETC) strategy to tolerate a maximum allowable number of successive packet dropouts (MANSDs) in networked control systems (NCSs). For the purpose of striking a balance between saving limited communication resources and reducing complexity of the transmission implementation, we present the periodic event-triggering mechanism (PETM), in which the triggering conditions only needs to be monitored at each event-verifying instant. In NCSs, packets often suffer from some interference in network transmission, such as packet losses. For packet dropouts, we introduce a dropout variable and model the whole closed-loop systems as a hybrid system. Furthermore, some stability conditions for co-design are given by a novel Lyapunov function and stability theorems of hybrid systems, and the explicit designs parameters of controller and triggering conditions are presented to ensure L2 stability. Finally, two illustrated examples are given to show the effectiveness of the proposed design methods.

Hybrid Event-Triggered Dynamic Control of Positive Markovian Jump Systems

This paper presents two classes of hybrid event-triggered dynamic output-feedback control for positive Markovian jump systems. First, a dynamic estimation system is established. By combining the output triggering conditions of the plant and its dynamic estimation system, the static hybrid event-triggered mechanism is constructed. By employing linear programming, the output-feedback controller and auxiliary controller gain matrices are designed to reach the positivity and stochastic ℓ1 stability of the system. Then, a hybrid dynamic event-triggered control is proposed by integrating the system output and the controller system output with the dynamic event-triggered internal variable. Finally, one illustrative example shows the effectiveness of the proposed design.

Event-triggered dynamic output feedback control of switched cyber-physical systems with stochastic cyber attacks

This article is concerned with the problem of event-triggered dynamic output feedback control for switched cyber-physical systems subject to stochastic cyber attacks. Two kinds of stochastic cyber attacks obeyed the Bernoulli distribution are taken into account, and a switched stochastic system is derived. An event-triggered strategy is adopted to reduce unnecessary communication and save system resources, and then an event-triggered–based dynamic output feedback controller is designed. The sufficient conditions for the mean-square exponential stability of the switched stochastic system are given by taking advantage of the mode-dependent average dwell time method and the multiple Lyapunov functional technique. Finally, the validity of the proposed theoretical results is verified by two examples.

Sum-based event-triggered dynamic output feedback control for synchronization of fuzzy neural networks with deception attacks

Sum-based event-triggered dynamic output feedback control for synchronization of fuzzy neural networks with deception attacks

Event-Triggered State-Feedback and Dynamic Output-Feedback Control of PMJSs With Intermittent Faults

This article proposes the event-triggered state-feedback and dynamic output-feedback control of positive Markovian jump systems with intermittent actuator and sensor faults. An independent Markovian process is used to establish the mathematical model of the intermittent fault. An event-triggering condition based on 1-norm is proposed to implement the event- triggered scheme. An event-triggered state-feedback controller is designed using stochastic copositive Lyapunov functions. By virtue of a matrix decomposition approach, an event-triggered dynamic output-feedback controller is proposed for the systems. Under the designed controllers, the augmented system consisting of the original system and the controller system is positive and stochastic <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\ell _1$</tex-math></inline-formula> stable. Finally, one example is provided to verify the validity of the proposed design.

Event-triggered dynamic output-feedback control for a class of Lipschitz nonlinear systems

Event-triggered dynamic output-feedback control for a class of Lipschitz nonlinear systems

Event-Triggered Dynamic Output Feedback Control for Genetic Regulatory Network Systems

Event-Triggered Dynamic Output Feedback Control for Genetic Regulatory Network Systems

A sum-based discrete event-triggered dynamic output feedback control for interval type-2 fuzzy systems

The current study concentrates on the event-based controller design for nonlinear networked systems characterized by the interval type-2 (IT2) fuzzy model. An innovative sum-based discrete event-triggered mechanism (SDETM), whose triggering condition includes several previous measurement samples, is proposed. Compared to the traditional event-triggered mechanism (ETM), the novel SDETM requires less network resources consumption. A dynamic output feedback controller (DOFC) is designed to achieve stability of the system. A novel stability criteria is established via the Lyapunov–Krasovskii functional method with the prescribed H∞ performance. Co-design of the DOFC and SDETM parameters is carried out using the cone complimentarity linearization (CCL) algorithm. The effectiveness of the proposed method is demonstrated with two practical cases.

Formula omitted] synchronization of delayed neural networks via event-triggered dynamic output control

This paper investigates H∞ exponential synchronization (ES) of neural networks (NNs) with delay by designing an event-triggered dynamic output feedback controller (ETDOFC). The ETDOFC is flexible in practice since it is applicable to both full order and reduced order dynamic output techniques. Moreover, the event generator reduces the computational burden for the zero-order-hold (ZOH) operator and does not induce sampling delay as many existing event generators do. To obtain less conservative results, the delay-partitioning method is utilized in the Lyapunov–Krasovskii functional (LKF). Synchronization criteria formulated by linear matrix inequalities (LMIs) are established. A simple algorithm is provided to design the control gains of the ETDOFC, which overcomes the difficulty induced by different dimensions of the system parameters. One numerical example is provided to demonstrate the merits of the theoretical analysis.

Event-triggered policy for dynamic output stabilization of discrete-time LPV systems under input constraints

This paper concerns the event-triggered dynamic output-feedback control of discrete-time linear parameter-varying (LPV) systems subject to saturating actuators. Two independent event-triggering schemes are introduced to determine whether the current signals should be transmitted (a) from the sensor to the controller and (b) from the controller to the actuator. As a result, the communication resources can be significantly saved. Both the emulation-based problem and the co-design problem are addressed. Sufficient conditions based on linear matrix inequalities (LMIs) are derived to ensure the regional asymptotic stability of the origin for the closed-loop system. A convex optimization procedure is proposed to determine the controller matrices and the event-triggering parameters aiming at reducing the number of updates on the independent channels sensor-to-controller and controller-to-actuator. At last, numerical examples are employed to testify to the validity of the proposed methods. • We propose convex co-design conditions for discrete-time saturated LPV systems. • The proposed controller is a dynamic output feedback controller with anti-windup term. • The output and control updates are independently orchestrated by two ETMs. • The proposed convex co-design condition can be adapted to recover the emulation mode. • Convex optimization procedures are provided to reduce the sampling activity.

Co-design of an event-triggered dynamic output feedback controller for discrete-time LPV systems with constraints

This paper investigates an event-triggered control design approach for discrete-time linear parameter-varying (LPV) systems under control constraints. The proposed conditions can simultaneously design a parameter-dependent dynamic output feedback controller and an event generator, ensuring the closed-loop system’s regional asymptotic stability. Based on the Lyapunov stability theory, these conditions are given in terms of linear matrix inequalities (LMIs). Moreover, using some proposed optimization procedures, it is possible to minimize the number of sensor transmissions, maximize the estimation of the region of attraction of the origin, and incorporate optimal control criteria into the formulation. Through numerical examples, some comparisons with other approaches in the literature evidence the proposed technique’s efficacy.

Event-triggered Dynamic Output Feedback Controller for Discrete-time LPV Systems with Constraints

This paper examines an event-triggered control design approach for discrete-time linear parameter-varying (LPV) systems under control constraints. A parameter-dependent dynamic output-feedback controller with an event-triggering condition is designed to ensure the regional asymptotic stability of the closed-loop system while minimizing a quadratic cost function. Sufficient conditions are derived in terms of linear matrix inequalities (LMIs) thanks to the use of a parameter-dependent quadratic Lyapunov function. Also, convex optimization schemes are proposed using these conditions to minimize an upper bound of a given cost function or to maximize the size of the region of closed-loop stability. Examples illustrate the proposal.

Dual-terminal decentralized event-triggered control for switched systems with cyber attacks and quantization

This article concerns dual-terminal event-triggered communication and decentralized control of switched systems that are the target of cyber attacks. Due to different properties of physical system, this system uses decentralized communication channels to transmit feedback data. In order to make efficient use of communication resources in each channel, the information sent by the sub-system needs to meet the given event-triggering conditions before it can be released. Moreover, the quantization is employed in both sides of the controller to further improve the data transmission efficiency. Then, considering that the triggered and quantified data are affected by dual-terminal cyber attacks, the event-triggered closed-loop switched (CLS) systems under attacks are derived. Furthermore, by utilizing average dwell time (ADT) technique and piecewise Lyapunov function (LF) method, sufficient conditions are given to ensure that the event-triggered CLS systems subject to dual-terminal cyber attacks are globally exponentially stable (GES). Accordingly, the design conditions for the gains of event-triggered dynamic output feedback (DOF) controllers and the parameters of decentralized event-triggering mechanisms (DETMs) are presented. Finally, simulations for verifying the system stability with and without cyber attacks are given.

Event-triggered dynamic output feedback control for networked Markovian jump systems with partly unknown transition rates

This paper addresses the problem of event-triggered dynamic output feedback control for uncertain Markovian jump systems with partly unknown transition rates. An event-triggered dynamic output feedback control scheme is proposed to stabilize a class of uncertain networked Markovian jump systems with partly unknown transition rates, and a suitable Lyapunov–Krasovskii functional is then introduced to guarantee the asymptotical stability in mean-square sense of the closed-loop controlled system. It is shown that the proposed control scheme is relatively convenient to choose the control gain matrices by the use of the Matlab LMIs Toolbox, and thus it might be convenient to application in practice. Finally, numerical examples are presented to illustrate the effectiveness of the proposed methodology.

Robust Output Regulation of Linear Systems by Event-Triggered Dynamic Output Feedback Control

In this article, we study the robust output regulation problem of linear systems by an event-triggered output feedback control law. We first summarize the solvability of the linear robust output regulation problem by the internal model design. Then, we implement this continuous-time control law by an event-triggered output feedback control law together with an output-based triggering mechanism. We show that, under almost the same solvability conditions as those for the linear robust output regulation problem, the robust output regulation problem can also be solved by an event-triggered output feedback control law without incurring Zeno phenomenon. Furthermore, we show that, it is possible to solve the robust output regulation problem practically while preventing the interevent time from approaching zero as time tends to infinity.