Dynamic Event-triggered Mechanism Research Articles

Switching-like dynamic event-triggered load frequency control for multi-area power systems with electric vehicles under DoS attacks

The load frequency control (LFC) for multi-area power systems with electric vehicles (EVs) is investigated under Denial of Service (DoS) attacks. First, a new dynamic event-triggered mechanism is proposed, where the event parameters can be switched according to whether DoS attacks occur or not. Then, a time-delay model with parameter uncertainty is developed by considering the state of charge (SOC) of EVs batteries and DoS attacks. Particularly, asymptotic stability criterion with prescribed H∞ performance is derived, and the relationship between the packet losses induced by DoS attacks and the event parameters is revealed. Further, a co-design scheme of the controller gain and the event-triggered weighting matrix is provided. Finally, simulation results show that the proposed method is advantageous to resist DoS attacks and save communication resources.

Dynamic event‐triggered asynchronous security load frequency control for semi‐Markov jump multi‐area power systems against deception attacks

AbstractThis paper focuses on the issue of dynamic event‐triggered (DET) asynchronous security load frequency control (LFC) for uncertain semi‐Markov jump interconnected multi‐area power systems (IMAPSs). Initially, the semi‐Markov process which in the presence of partially unknown transition rates (TRs) is exactly introduced to model the switching behaviors of the IMAPSs. Subsequently, the DET mechanism is scheduled to alleviate the transmission frequency and guarantee the desired control performance. Furthermore, the probabilistic deception attacks are taken into consideration, which is much more apt for the practical engineering background. Additionally, a hidden Markov model (HMM) is borrowed to depict the modes asynchronous phenomenon between the security controller and the semi‐Markov jump IMAPSs. Afterwards, on the basis of Lyapunov‐Krasovskii functional method and stochastic analysis technique, sufficient conditions with two cases are derived in the form of linear matrix inequalities (LMIs) to guarantee the stochastic stability with an performance of the semi‐Markov jump IMAPSs. Ultimately, a numerical example is exhibited to verify the solvability and practicability of the proposed control strategy.

Event‐triggered control of Itô stochastic nonlinear delayed systems with state quantization

AbstractThe problem of event‐triggered control (ETC) of stochastic nonlinear delayed systems with state quantization is investigated. In order to reduce the communication burden and computational cost, an ETC mechanism and state quantization are introduced in the control scheme. Two configurations are considered to investigate their interaction: quantization before event‐trigger (QE) and quantization after event‐trigger (EQ), respectively. Based on this, a static event‐triggered mechanism (ETM) is designed while avoiding the Zeno phenomenon. The system allows known input‐to‐state stability (ISS) control law, followed by utilizing the Halanay inequality to achieve stabilization. In both configurations, the stabilization of the closed‐loop system is guaranteed. We show that communication times can be reduced by adjusting parameters and selecting the suitable configuration. Moreover, to further enhance resource conservation, we illustrate the feasibility of dynamic ETMs in this framework. Numerical simulation results validate the effectiveness of the proposed ETC schemes.

Average consensus time-varying formation control of underactuated autonomous underwater vehicles based on event triggering decoupling mechanism

This paper focuses on the challenge of time-varying formation control for underactuated autonomous underwater vehicles (AUVs). To tackle this challenge, an average consensus time-varying formation control based on event triggering decoupling mechanism (ETDM-ACTVF) is proposed. Firstly, the average consensus time-varying formation (ACTVF) based on the affine transformation are introduced, and the control objective considering the dynamic characteristics of underactuated AUVs is proposed. The trajectory tracking controller is designed by the back stepping method. Secondly, an event triggering decoupling mechanism (ETDM) achieves the state decoupling to constrain the uncontrollable state. Moreover, a dynamic event triggering mechanism is employed to reduce the execution frequency of actuators. Finally, the excellent performance of the proposed algorithm in addressing both the underactuated AUV time-varying formation control and decoupling control problems is demonstrated through simulation results.

State estimation and sliding-mode fault-tolerant control of a class of nonlinear systems with actuator faults under the event-triggered mechanism

The current work is an analytical study of the problem of state estimation and fault-tolerant control (FTC) based on dynamic event triggered mechanism (ETM) in the event of actuator fault for a class of nonlinear systems in a network environment. First, a state estimator module is designed in conjunction with the designed dynamic ETM, and an integral sliding surface (SS) is designed in the estimation space to obtain a variable time delay system after augmenting the system by combining the state estimator module and the SS. Second, the sufficient conditions that can make the augmented system stable under the ETM with H∞ performance index are obtained by the Lyapunov–Krasovskii function. The sufficient conditions are decoupled after the design of the sliding mode control (SMC) law that can stabilize the system, and the calculation of the state estimation gain matrix and the SMC gain matrix are obtained. Finally, the effectiveness of the method proposed in this study is verified with two simulation comparison experiments.

Sliding-mode fault-tolerant control of the six-rotor UAV with dead-zone-input under event-triggered mechanism

This paper investigates the problem of event-triggered mechanism(ETM)-based sliding-mode fault-tolerant control (FTC) for a six-rotor Unmanned Aerial Vehicle (UAV) with dead zone input (DZI) cases, considering potential actuator and sensor faults. Initially, a dynamic ETM is designed, followed by the development of a non-fragile observer utilizing this designed ETM. An integral sliding surface (SS) is then designed in the observation space, and the system is augmented and treated as a variable time delay system. Subsequently, sufficient conditions to ensure the stability of the augmented system with an H∞ performance index γ are obtained using the Lyapunov-Krasovskii function. Next, a sliding mode control (SMC) law is formulated to guide the sliding variables to the SS in finite time. Furthermore, sufficient conditions for ensuring system stability with an H∞ performance index γ are decoupled, and the calculation methods for the non-fragile observer gain matrix and the sliding mode gain matrix are obtained. Finally, to validate the effectiveness of the proposed method in this paper, simulation experiments are conducted.

Adaptive fuzzy prescribed settling time consensus control for multi-agent systems with input hysteresis via dynamic event-triggered mechanism

An adaptive fuzzy prescribed settling time consensus control is proposed based on the dynamic event-triggered mechanism for a class of uncertain nonlinear multi-agent systems (UNMASs) subject to input hysteresis. In practical systems, the plant inevitably suffers from input hysteresis and unknown nonlinear characteristics, which dramatically deteriorate its performance. Therefore, to eliminate the effects of those constraints, an adaptive fuzzy prescribed settling time control scheme is constructed by applying backstepping technique and first-order filter. Wherein each agent needs to be equipped with only one adaptive law for fuzzy-logic-system approximation, reducing the computational burden. Simultaneously, the synchronization error is mapped using a transformation function to achieve prescribed settling time consensus control. Moreover, a dynamic event-triggered mechanism is employed to reduce unnecessary communication transmission and save energy. With the presented method, all agents synchronize to the virtual leader within a prescribed settling time. Theoretical analysis and simulation examples verify the presented method.

Exponential stabilization of aero-engine T-S fuzzy system with decentralized dynamic event-triggered mechanism

Exponential stabilization of aero-engine T-S fuzzy system with decentralized dynamic event-triggered mechanism

Fixed-time containment control for nonlinear multi-agent systems via dynamic event-triggered mechanism over directed graphs

This paper focus on a dynamic event-triggered mechanism to solve fixed-time containment control problems for nonlinear multi-agent systems (MASs) with uncertain disturbances over directed graphs. An event-triggered algorithm based on dynamic variables is proposed to achieve the fixed-time containment control of MASs, which can effectively reduce the number of event-triggered instants. By modifying the auxiliary variables and reconstructing the dynamic variables, the effect of input time-delays in MASs is successfully eliminated. It is noteworthy that only the auxiliary variables are modified while the structure of the dynamic variables is not changed, which further indicates that the designed dynamic event-triggered algorithm is also suitable for solving multi-agent systems with input time-delays. In addition, the setting time associated with the time-delays is also computed and the Zeno behavior is successfully avoided by computing the minimum time interval between event triggering as non-negative. Finally, simulation results are given to verify the effectiveness of proposed control protocol.

Dynamic event-triggered-based online IRL algorithm for the decentralized control of the input and state constrained large-scale unmatched interconnected system

This article proposed a novel adaptive decentralized control (ADC) method for the continuous-time state-constrained and input-constrained large-scale unmatched interconnection system by the means of the adaptive critic design in the edge dynamic event-triggered (EDET) mechanism. The barrier function is used to transform the state-constrained system to the common system without constrained states. To overcome the influence of the unmatched interconnection terms, the auxiliary systems are devised for each subsystem of the large-scale system. Moreover, the non-quadratic utility functions are introduced to constrain the input of the auxiliary systems. The decentralized control scheme of the large-scale unmatched interconnection system can be realized by solving the optimal control schemes of the auxiliary systems. Then with the help of the single critic neural network (NN), the approximate optimal control policies are acquired by designing the approximate cost functions. After that, an integral reinforcement learning (IRL)-based scheme is proposed to solve the integral Bellman equation rather than the complex coupled Hamilton–Jacobi-Bellman equation (HJBE). Simultaneously, the EDET mechanism is introduced to reduce the computation efforts and communication loads. The asymptotic stability of the constrained large-scale unmatched interconnection system is proved. In addition, the infamous Zeno behavior is effectively avoided. Finally, two simulation cases are given to verify the effectiveness of the proposed algorithm.

Fixed-time nonlinear-filter-based consensus control for nonlinear multiagent systems

This paper investigates the fixed-time consensus control problem for a class of nonlinear multiagent systems (MASs) based on dynamic event-triggered mechanism. In contrast to the already-existing fixed-time control schemes, which cannot address the singularity problem, by constructing a virtual signal and using inequality technology, a new processing method is provided to solve such a problem. In addition, a novel fixed-time nonlinear filter that converges in a fixed time is introduced, which effectively solves the “explosion of complexity” problem existed in backstepping. Moreover, to improve the utilization of resources, an event-triggered mechanism with internal dynamic variables is proposed. It is worth noting that some existing static event-triggered mechanisms are special cases of the dynamic ones. On this basis, an effective fixed-time event-triggered control scheme is developed for MASs, which minimizes the data transmission as much as possible while ensuring the system performance. More importantly, the scheme can ensure the convergence of the system in a fixed time. Finally, simulation results are shown to support the validity of the developed approach.

Event-triggered [formula omitted] control for discrete-time switched systems with saturation nonlinearities

This paper studies the event-triggered H∞ control based on the average dwell time (ADT) method for discrete-time switched system with input saturation and state saturation. Based on the convex hull method, the state feedback controller and the dynamic output feedback controller are designed respectively. The influence of input saturation and state saturation on the dynamic performance of the system is eliminated. The dynamic event-triggered mechanism is introduced, which saves the communication resources and computation resources of the system. Based on ADT, the H∞ exponential stability of the closed-loop system is guaranteed.Finally, the effectiveness of the proposed method is verified by the numerical examples.

Decentralized optimal control of large-scale partially unknown nonlinear mismatched interconnected systems based on dynamic event-triggered control

In this paper, a novel decentralized control method is proposed for nonlinear mismatched large-scale interconnected systems subjected to partially unknown dynamics by designing auxiliary control for each subsystem. It is demonstrated that the control sequence consisting of the optimal control policies of auxiliary control can stabilize the system asymptotically, leading to decentralized control of the large-scale system. An integral reinforcement learning (IRL) method is firstly proposed, replacing the traditional policy iterative algorithm to analyze the optimal control problem of each edge subsystem with partially unknown dynamics. After that, the edge-based dynamic event-triggered control algorithm is proposed based on the static event-triggered control method, and an internal dynamic variable characterized by a first-order filter is defined. A single critic neural network (NN) is then designed to learn the approximate optimal control strategy under the dynamic event-triggered mechanism adaptively. The stability analysis is proposed to demonstrate that the state of the event-based pulse system is ultimately uniformly bounded (UUB) and the Zeno behavior is eliminated successfully. Finally, the effectiveness of the proposed algorithm is verified by two simulation examples to realize decentralized control of mismatched large-scale systems.

Fixed-time event-triggered sliding mode cooperative path-following control with prescribed performance for USVs based on lumped disturbance observer

In order to solve the problem of Unmanned Surface Vehicles being unable to accurately cooperative path-following under the parameter uncertainties, actuator faults, and external disturbances, this paper mainly combines the fixed-time and sliding mode theory to design the control strategy. Firstly, a fixed-time disturbance observer is adopted to estimate composite disturbances. Then, a fixed-time heading-surge guidance law that satisfies the fixed-time stability of the position error system is designed, and the synchronization of the formation on the full paths is realized by the fixed-time path parameter consensus. On this basis, a fixed-time dynamic event-triggered sliding mode controller with prescribed performance is proposed to ensure that the velocity error system is stable in fixed time and the tracking error is strictly within the specified range. Due to the application of fixed-time theory in cooperative path-following, the whole closed-loop system is proved to be fixed-time stable. Moreover, the dynamic event-triggered mechanism is introduced to reduce the energy consumption. Finally, the effectiveness and superiority of the proposed control method are verified by simulations.

Cognitive-Radio-Based Resource Management for Smart Transportation: A Sliding Mode Control Approach

Smart transportation is one of the key components of the smart city. A significant amount of data is produced by the numerous vehicles connected via wireless networks. This demands smart spectrum management for efficient data communication. The optimal bandwidth usage can be obtained by opportunistically sharing the spectrum among high-priority class primary and low-priority class secondary users’ vehicles. In this study, we develop a mathematical model to ensure effective resource allocation in Cognitive Radio based Smart Transportation Networks (CR-STN) considering the mobility, bandwidth constraint and interference models. Robust sliding mode control is synthesized using an exponential reaching law to avoid data packet congestion in CR-STN to ensure smart spectrum management. In addition, a dynamic event-triggered mechanism is incorporated with the sliding mode controller to reduce computational complexity and energy expenses. The effectiveness of the suggested controller has been validated by simulation results.

Distributed Adaptive Dynamic Event-Triggered Secondary Control for Islanded Microgrids With Disturbances

This paper addresses the event-triggered secondary control problem for islanded microgrid with disturbances. Adaptive distributed control law is proposed for regulating voltage and frequency to desired values, while avoiding using global information. A novel distributed adaptive dynamic event-triggering mechanism (DETM) is proposed, the adaptive parameters are incorporated into the ETM design, such that not only strictly positive minimum event-triggering interval (METI) can be guaranteed, even in the presence of disturbances, but also global information is not required for implementation of the ETM. By means of auxiliary function, a novel Lyapunov function is constructed for stability analysis and ETM design. Compared with the existing results, with the proposed event-triggered control strategy, strictly positive METI and no dependence on global information can be achieved simultaneously. Moreover, the proposed adaptive event-triggered control strategy is robust to external disturbances. Finally, the effectiveness of the proposed method is verified by simulation results.

Finite-Time Membership Function-Dependent $H_{\infty }$ Control for T–S Fuzzy Systems via a Dynamic Memory Event-Triggered Mechanism

This paper considers the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H_{\infty }$</tex-math></inline-formula> control problem in the finite-time horizon for a class of T-S fuzzy systems that frequently work under a certain fuzzy rule. By assigning different weights to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H_{\infty }$</tex-math></inline-formula> indexes of different subsystems, a novel finite-time membership function-dependent <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H_{\infty }$</tex-math></inline-formula> performance is proposed. Then, better disturbance rejection can be achieved for this specific type of T-S fuzzy system. In order to save the system resources, an event-triggered mechanism is developed. Different from the widely existing dynamic event-triggered mechanism, a dynamic memory event-triggered mechanism is established by introducing dynamic variables with memory. Rigorous theoretical analysis shows that the proposed dynamic memory event-triggered mechanism possesses larger event-triggered intervals than the existing dynamic event-triggered mechanism. This facilitates the implementation of the proposed scheme in real systems, as real physical components require time intervals between successive event executions. Further, based on the newly proposed index and dynamic memory event-triggered mechanism, the finite-time event-triggered <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H_{\infty }$</tex-math></inline-formula> control problem of the considered system is solved. Finally, a practical example is employed to display the efficacy of the proposed scheme.

ADP-based event-sampled optimized consensus tracking of stochastic multi-agent systems with actuator faults

This article concentrates on the event-triggered optimized leader-follower consensus control for stochastic nonlinear multi-agent systems (MASs) with actuator bias faults. An adaptive state identifier is designed to estimate unknown states in real time. In addition, an adaptive dynamic programming (ADP)-based algorithm with a critic-actor architecture is developed to learn the optimized control policy online. Based on the online estimating information from the designed fault estimator, a fault-tolerant (FT) controller is designed to compensate for the deleterious effects of actuator bias faults. The proposed control scheme employs a dynamic event-triggered mechanism (ETM) to reduce network resources by tuning dynamic parameters. All the signals can be proven to be bounded by utilizing the Lyapunov stability method, and the Zeno behavior can be excluded. Simulation results show the merit and validity of the proposed control scheme.

Dynamic event–triggered security control for stochastic partial differential equation systems via a unified pointwise measurement/control strategy

This article focuses on the security control for parabolic partial differential equation systems driven by fractional Brownian motion based on a unified pointwise measurement/control strategy. Initially, by employing the Takagi–Sugeno fuzzy rule, nonlinear stochastic systems are transformed into a series of linear subsystems. Then, based on the improved dynamic event-triggered mechanism and pointwise measurements method, a security controller is presented to resist deception attacks. In addition, the Lyapunov direct method and some novel inequalities are used to ensure the considered system is mean-square exponentially stable, and the gain of the point controller is obtained. Finally, an application example is provided to verify the effectiveness of the proposed method.

Dynamic event-triggered attitude synchronization of multi-spacecraft formation via a learning neural network control approach

This paper addresses the robust attitude synchronization issue in a multi-spacecraft formation system subjected to limited communication, space disturbances, modeling uncertainties, and actuator faults. To accommodate limited inter-spacecraft communication, a dynamic event-triggered mechanism is designed to reduce the communication trigger frequency by dynamically adjusting the trigger threshold. Moreover, an event-based distributed self-learning neural-network control (SLN2C) law is developed to guarantee robust attitude synchronization during multi-spacecraft formation. In the SLN2C scheme, a learning radial basis function neural network (RBFNN) model is proposed to online approximate and compensate for lumped disturbances, in which an iterative learning algorithm with a variable learning intensity is adopted to update the weight matrix of the RBFNN model. Compared with the traditional fixed learning intensity, a variable one can reduce initial oscillation and weaken the saturation response. Numerical simulations and comparisons are performed to illustrate the effectiveness and superiority of the proposed event-based spacecraft attitude synchronization control method.