Adaptive Event-triggered Mechanism Research Articles

Event-Triggered Data-Driven Security Formation Control for Quadrotors Under Denial-of-Service Attacks and Communication Faults.

In this article, the security formation control problem is investigated for underactuated quadrotors involving nonlinear coupled dynamics, subject to denial-of-service (DoS) attacks and uncertain communication faults. A security formation control method is proposed, including a distributed resilient observer and a hierarchical data-driven controller. The observer with an adaptive event-triggered mechanism is developed to restrain the influence of DoS and communication faults on interaction information among quadrotors, and Zeno behavior of all observers can be avoided. The optimal control laws are learned iteratively based on observation data and system data by utilizing reinforcement learning without knowledge of system dynamics. The stability of the constructed closed-loop control system is proven, and sufficient conditions are established for the unreliable network. Simulation results demonstrate the advantages of the proposed security control method.

Fuzzy control design for time-delay systems under adaptive event-triggered mechanism

Fuzzy control design for time-delay systems under adaptive event-triggered mechanism

Adaptive event-triggered pseudolinear consensus filter for multi-UAVs bearings-only target tracking

An improved version of the pseudolinear filter is proposed, namely an adaptive event-triggered instrumental variable-based pseudolinear information consensus filter, with a special focus on three-dimensional(3D) unbiased target tracking with multiple unmanned aerial vehicles (UAVs) bearings-only measurements under communication faults. To deal with the unknown communication faults, a credibility-based weighted average consensus rule is constructed, which considers the network topology and the information quality of UAVs. Furthermore, an adaptive event-triggered mechanism is designed to reduce the computation burden and communication bandwidth. The selective-angle-measurement strategy (SAMS)-based instrumental variable is integrated into the pseudolinear information consensus filter to achieve unbiased estimates. It is proved that the estimates of all UAVs will eventually achieve consistency, and its estimation errors are bounded. Finally, numerical simulations are given to verify the advantages of the proposed filtering algorithm in terms of enhanced accuracy and faster convergence rates.

Adaptive Event-Triggered Neural Network Fast Finite-Time Control for Uncertain Robotic Systems

A fast convergence adaptive neural network event-triggered control strategy is proposed for the trajectory tracking issue of uncertain robotic systems with output constraints. To cope with the constraints on the system output in the actual industrial field while reducing the burden on communication resources, an adaptive event-triggered mechanism is designed by using logarithm-type barrier Lyapunov functions and an event-triggered mechanism. Meanwhile, the combination of neural networks and fast finite-time stability theory can not only approximate the unknown nonlinear function of the system, but also construct the control law and adaptive law with a fractional exponential power to accelerate the system’s convergence speed. Furthermore, the tracking errors converge quickly to a bounded and adjustable compact set in finite time. Finally, the effectiveness of the strategy is verified by simulation examples.

Adaptive event-triggered PID load frequency control for multi-area interconnected wind power systems under aperiodic DoS attacks

In this article, an adaptive event-triggered proportional–integral–derivative (PID) load frequency control (LFC) strategy is designed for multi-area interconnected wind power systems under aperiodic denial-of-service (DoS) attacks. First, to relieve the frequency fluctuation caused by wind power integration, a dynamic model including the doubly-fed induction generator is established for multi-area interconnected wind power systems. Second, to avoid superfluous occupation of network communication bandwidth, an adaptive event-triggered mechanism with memory property is proposed under aperiodic DoS attacks. Then, with the aid of intermittent control method, a memory-based PID LFC strategy is achieved to stabilize the frequency of wind power systems. Third, by constructing a switched Lyapunov-Krasovskii functional, two exponential stability criteria meeting the H∞ norm bound are derived. Finally, simulation examples are provided to verify the effectiveness of the presented control strategy.

Aperiodic Sampled-Data-Based Control for T-S Fuzzy Systems: An Improved Fuzzy-Dependent Adaptive Event-Triggered Mechanism

This paper is devoted to designing a novel aperiodic sampled-data-based event-triggered control strategy for Takagi-Sugeno (T-S) fuzzy systems. Firstly, via taking the structural features of fuzzy subsystems and the available information of fuzzy membership functions (FMFs) into consideration, an improved fuzzy-dependent adaptive event-triggered mechanism (FDAETM), which designs different adaptive event-triggered mechanisms for corresponding fuzzy subsystems, is proposed to provide extra design flexibility and further optimize communication efficiency. Then, the two-side looped-functional method (TSLFM) and dynamic partitioning approach are introduced in the construction of the novel Lyapunov-Krasovskii functional (LKF). These two methods contribute to deriving preferable stability criterion and stabilization approach via relaxing the positive definite constraint on LKF and fully utilizing the inner system state during the whole aperiodic sampling interval. Eventually, two simulation examples are introduced to verify the effectiveness of the proposed control strategy and its advantages in lightening communication frequency.

Switched Observer-Based Adaptive Event-Triggered Load Frequency Control for Networked Power Systems Under Aperiodic DoS Attacks

This paper is concerned with a resilient load frequency control (LFC) problem for networked power systems against the aperiodic denial-of-service (DoS) attack. First, considering the intermittent nature of the aperiodic DoS attack and the unavailability of the partial system states simultaneously, a switched observer-based time-delay networked power model is established. Second, to save network bandwidth resources, a novel adaptive event-triggering mechanism is designed by applying a switch-adjusting threshold parameter scheme. Then, a switched observer-based PI controller is designed and a piecewise Lyapunov functional analysis approach is developed such that the exponential stability of closed-loop augmented power systems is derived with desired H∞ performance in forms of linear matrix inequality (LMI). Finally, a power system example with IEEE 39-bus is provided to illustrate the effectiveness of the designed schemes.

Adaptive event-triggered fault-tolerant control for Markov jump nonlinear systems with time-varying delays and multiple faults

In this paper, a state feedback H∞ controller is developed to solve the fault-tolerant (FT) control problem of a class of discrete-time networked Markov jump nonlinear systems (MJNSs), which are affected by time-varying delays, external disturbances, actuator and sensor faults, and network-induced time delays, simultaneously. The mode-dependent adaptive event-triggered mechanism is introduced to capture as many valid features of the desired signal as possible while maximizing network resource savings. Then, based on the Lyapunov–Krasovskii (L–K) theory, sufficient conditions for the closed-loop system to be stochastically stable and to satisfy a specified H∞ performance index are obtained. Based on the above, the design methods of the FT controller are given in the form of linear matrix inequalities (LMIs). Finally, the effectiveness of the designed FT controller is verified by an example simulation.

Distributed adaptive event-triggered fault detection filter of positive semi-Markovian jump systems

This paper presents a distributed fault detection filter (FDF) for positive semi-Markovian jump systems (PSMJSs) based on an adaptive event-triggered mechanism (AETM) scheme. A novel AETM is first proposed for the systems. A weighted fault model is introduced to assist the estimation of the original fault. Utilizing stochastic copositive Lyapunov function (CLF), a distributed FDF whose parameters are designed by virtue of linear programming (LP), is proposed. Under the designed filter, the positivity and stochastic stability with L1/L_-gain performance of a fault detection system is reached. The contribution of this paper contains three aspects: (i) An AETM is constructed for the considered systems, (ii) A distributed FDF is designed for the systems, and (iii) The hybrid L1/L_-gain performance is achieved with respect to the disturbance and the fault. Two illustrative examples are given for verifying the validity of the results.

An adaptive event-triggered quantized secondary regulation strategy for microgrids with actuator faults

This paper focuses on the event-triggered quantized secondary regulation problem for islanded alternating-current (AC) microgrids with actuator faults. In order to handle the actuator faults, including bias faults and partial loss of effectiveness faults, a distributed secondary regulation strategy is proposed, which achieves voltage and frequency regulations, as well as power sharing. Due to the limited communication bandwidth, a uniform quantizer is introduced for information transmission. Meanwhile, to save system resources and relieve the communication burden, an adaptive event-triggered mechanism is designed. Finally, the proposed strategy is validated by using an islanded AC microgrid test system built in MATLAB/SimPowerSystems, which indicates that the proposed strategy increases the reliability of system and reduces the controller update times.

Adaptive event-triggered output tracking control for Markovian jump systems: A robust [formula omitted] MPC approach

This work focuses on the output tracking control for discrete-time Markov jump systems (MJSs) via a model predictive control (MPC) approach. Not only the mean-square stability (MSS), but also the H2 performance and H∞ performance is considered in the MPC controller design. To save communication resources, a version of adaptive event-triggered mechanism (AETM) is utilized. Different from the traditional static event-triggered mechanism, its threshold coefficient is time-varying and can be adjusted in real-time to fit the system evolution. Moreover, the asynchronous switching between the controller modes and the plant modes is taken into account. The design objective is to synthesis an adaptive event-triggered asynchronous MPC controller such that not only the augmented closed-loop system is MSS with a certain level of H2/H∞ performance, but also the communication burden can be reduced to some extent. It is the first attempt to investigate the output tracking control of MJSs via an MPC approach. By constructing a plant model-dependent Lyapunov function, a set of conditions is derived to determine a desired MPC controller. In addition, two MPC related issues, namely recursive feasibility and closed-loop MSS, are also studied. At last, an illustrative example is given to verify the availability of the theoretical findings.

Adaptive event-triggered based fault detection filtering for unmanned surface vehicles under DoS attacks

This paper proposes an adaptive event-triggered fault detection filter to warn of faults happening to unmanned surface vehicles in a network. First, the unmanned surface vehicle is modeled as a Markovian jump system which makes allowances for the influences of waves or other disturbances. Then, a fault detection filter is developed to produce crucial residual signals, despite disturbances and denial-of-service attacks. Inputs of the designed filter originate from a resilient adaptive event-triggered mechanism, of which the threshold can be adjusted for saving valuable network resources and mitigating adverse influences of denial-of-service attacks. By using the residual signals, detection evaluation functions are established and a detection logic algorithm is devised. Under this framework, the co-design of both the fault detection filter and the adaptive event-triggered scheme is given. Finally, the proposed method is proved to be applicable by simulating a real unmanned surface vehicle model.

Co-Design of Adaptive Event-Triggered Mechanism and Asynchronous H∞ Control for 2-D Markov Jump Systems via Genetic Algorithm.

This article concerns the co-design scheme of the adaptive event-triggered mechanism (AETM) and asynchronous H∞ control for two-dimensional (2-D) Markov jump systems. First, we introduce a hidden Markov model with the observation that the asynchronous phenomenon is inevitable between the plant mode and the controller mode. Besides, for economizing the communication times, an innovative 2-D AETM is constructed, which can dynamically regulate the event-triggered thresholds to strive for better system performance. Then, by utilizing the 2-D Lyapunov stability theory, nonlinear matrix inequalities are built to ensure the asymptotic mean-square stability with an H∞ performance for the closed-loop 2-D system. To avoid introducing any conservatism when handling the above nonlinear matrix inequalities, a binary-based genetic algorithm (BGA) is exploited to treat some variables as known, such that derive some directly solvable linear matrix inequalities. Finally, a simulation example is provided to verify the effectiveness of the proposed 2-D AETM-based asynchronous controller strategy with a BGA.

Adaptive Event-Triggered Mechanism to Synchronization of Reaction–Diffusion CVNNs and Its Application in Image Secure Communication

This article is centered on the formulation of a refined adaptive sampled-data-based event-triggering control (ASDBETC) scheme for the synchronization of reaction–diffusion complex-valued neural networks (RDCVNNs) with probabilistic time-varying delays (TVDs). A refined ASDBETC mechanism is first proposed in a hierarchy structure, which differs from some conventional sampled-data-based event-triggering control mechanisms with preordained invariable threshold. The refined ASDBETC mechanism can adaptively adjust the orientation and frequency of event-triggered threshold parameters via the variational tendency of states and the corresponding Euclidean distance between states. Therefore, an intact bidirectional regulative mechanism that is sensitive to the changes of state is legitimately established to provide additional flexibility, which is conducive to better compromise between communication resources and control performance. Through considering the effect of uncertainties, the random TVDs belonging to two different intervals by a probabilistic form are introduced. Then, by leveraging a novel time-related Lyapunov–Krasovskii functional (LKF) that contains the more realistic sampled behaviors on the entire sampled interval, new synchronization conditions and controller design method are derived for RDCVNNs. Finally, the advantages of the refined ASDBETC strategy, the availability, and practicability of the developed theories are corroborated via two simulation examples.

Adaptive event-triggered filtering for semi-Markov jump systems under communication constraints

This paper investigates the problem of adaptive event-triggered filtering for a class of semi-Markov jump systems. A more general scenario of semi-Markov jump systems under communication constraints is explored, where the output signal occurs with saturation nonlinearity, packet loss, and quantization. The saturation nonlinearity and packet dropout are described by two random variables subject to Bernoulli distribution. The mode-dependent adaptive event-triggered mechanism is designed by combining the system mode information. Based on the output error, the trigger threshold adaptive law is designed to make the threshold change dynamically. The mode-dependent time-varying filter is constructed and sufficient conditions for the desired filter with H∞ performance are obtained. Finally, a single-link robot arm system verifies the effectiveness of the proposed filter design method, and the adaptive event-triggered protocol can effectively reduce the number of data transmissions.

Robust adaptive event-triggered path following control for autonomous surface vehicles in shallow waters

This paper utilizes an adaptive event-triggered mechanism to investigate the path following control of autonomous surface vehicles (ASVs) in shallow waters. Given the challenges posed by the shallow water environment to ASV maneuverability and heading stability, the controller design must enhance the robustness and reduce model complexity. Firstly, a time-varying drift angle-based course guidance law was proposed using a position error dynamics model and a reduced-order extended state observer. Secondly, to simplify the controller's design steps, the ASV's surge and sway motion models are decoupled and transformed. Then, an event-triggered mechanism controller was designed to activate the ASV's rudder angle and shaft speed. In addition, a new robust term was introduced to further estimate the residuals of the RBF neural network, the external disturbances and the event-triggered residual term, thus eliminating the requirement for an upper bound on the unknown disturbances and residuals. The proposed control method based on the robust adaptive event-triggered mechanism can guarantee that all estimation and tracking errors are uniformly ultimately bounded. Simulation results demonstrate that the controller can ensure effective path following of vessels in shallow waters.

Improved Sampled-Data Controller Design for T–S Fuzzy Systems with an Adaptive Event-Triggered Mechanism

Improved Sampled-Data Controller Design for T–S Fuzzy Systems with an Adaptive Event-Triggered Mechanism

Adaptive event-triggered control for networked interconnected systems with cyber-attacks

This paper investigates the secure adaptive event-triggered controller design for networked interconnected systems (NISs) with cyber-attacks. Firstly, an adaptive event-triggered mechanism (AETM) with dynamic threshold parameter is adopted to economize the limited network bandwidth which takes the abrupt data into consideration. A model of cyber-attacks is established for NISs with consideration of the malicious cyber-attacks. On account of the model of cyber-attacks and AETM, a mathematical model of NISs is established. For the built system, the sufficient condition for the asymptotic stability of the system is obtained by making use of Lyapunov stability theory and linear matrix inequality (LMI) technique, and the design algorithm of the controller is proposed. Finally, a simulation example is given to verify the validity of the theoretical results.

An Adaptive Event-Triggered Consensus Approach for MASs With Network-Induced Delays

This study investigates the consensus control problem of linear multi-agent systems (MASs) with network-induced delays (NIDs). A state feedback controller based on an event-triggered transmission scheme is proposed, where the novel adaptive event-triggering mechanism (ETM) is used to lower the frequency of data transmission. In contrast to previous works, sufficient conditions related to the consensus for the resulting closed-loop control systems are derived using a discontinuous looped Lyapunov functional (DLLF) and the corresponding reciprocally convex protocol. It is demonstrated that the consensus errors of resulting closed-loop control systems are asymptotically stable and the Zeno behavior is excluded. Finally, a numerical example of an unmanned aerial vehicle (UAV) model is presented to demonstrate the effectiveness of our proposed theoretical results.

Fully Distributed Event-Triggered Bipartite Output Formation Control for Heterogeneous MASs With Directed Graphs

This brief considers the bipartite output time-varying formation control problem for heterogeneous multi-agent systems over signed directed graphs under an adaptive event-triggered mechanism. A fully distributed bipartite controller is designed to estimate the state of the leader and form the desired antagonistic formation shape only using the relative output information. With the help of the event-triggered mechanism, the proposed protocol prevents continuous communication among neighbor agents under the directed topology. Also, Zeno behavior is verified to be ruled out. Furthermore, the proposed protocol not only avoids using the eigenvalues of the Laplacian matrix but also does not need to continuously monitor the information of neighbor agents. Finally, the effectiveness of the designed protocol is illustrated by a numerical simulation.