Event-triggered Approach Research Articles

Optimal Consensus Control for Continuous-Time Linear Multiagent Systems: A Dynamic Event-Triggered Approach.

This article investigates the optimal consensus problem for general linear multiagent systems (MASs) via a dynamic event-triggered approach. First, a modified interaction-related cost function is proposed. Second, a dynamic event-triggered approach is developed by constructing a new distributed dynamic triggering function and a new distributed event-triggered consensus protocol. Consequently, the modified interaction-related cost function can be minimized by applying the distributed control laws, which overcomes the difficulty in the optimal consensus problem that seeking the interaction-related cost function needs all agents' information. Then, some sufficient conditions are obtained to guarantee optimality. It is shown that the developed optimal consensus gain matrices are only related to the designed triggering parameters and the desirable modified interaction-related cost function, relaxing the constraint that the controller design requires the knowledge of system dynamics, initial states, and network scale. Meanwhile, the tradeoff between optimal consensus performance and event-triggered behavior is also considered. Finally, a simulation example is provided to verify the validity of the designed distributed event-triggered optimal controller.

Fixed-time attitude control for hypersonic morphing vehicles: A dynamic memory event-triggering approach

This paper focuses on achieving fast attitude tracking and resource preservation for hypersonic morphing vehicles (HMVs) in continuous morphing stages. Firstly, the most remarkable feature of an HMV are the morphing wings, leading to dramatic aerodynamic property changes. As a result, fast tracking and excellent adaptation to various morphing statuses are necessary to guarantee the success of flight missions. Thus, this work employs the fixed-time stability theory to establish a high-performance controller, including variable exponent coefficients. Compared to the traditional constant exponent coefficient-based methods, some singularity problems are avoided, and the global fixed-time stability is ensured. Then, in order to implement the control input in a low-resource occupation, the dynamic event-triggering mechanism is presented to provide a flexible updating policy. The proposed event-triggering technology has a more concise and efficient structure than the traditional methods. In this case, the controller and the dynamic variable of the event-triggering method simultaneously adopt the exponent coefficients. Finally, the closed-loop stability is proven via the Lyapunov thesis, and simulation results show the effectiveness and the advantage of the proposed control strategy.

Quantized feedback integral sliding mode control for uncertain networked linear systems via event-triggered approach

This paper investigates the design problem of quantized event-triggered (ET) integral sliding mode (ISM, SM) controller for uncertain networked linear systems. Firstly, this paper proposes a novel ISM surface constructed using only quantized ET states and applied to the design of the controller. The quantized ET ISM surface in this paper divides the integrals from t0 to t into the sum of adjacent ET intervals and replaces ET states with quantized ET states. Secondly, based on state error and quantized ET SM error, novel ET conditions are constructed to decide whether to update the current control signal or not. Thirdly, this paper proves the existence of minimum inter-event times under “zoom-out” and “zoom-in” phases respectively, avoiding Zeno phenomenon. Finally, to prove the validity of the proposed method, two comparative simulation results are given.

Finite-Time Based Fault-Tolerant Control for Half-Car Active Suspension System With Cyber-Attacks: A Memory Event-Triggered Approach

Finite-Time Based Fault-Tolerant Control for Half-Car Active Suspension System With Cyber-Attacks: A Memory Event-Triggered Approach

State estimation of singularly perturbed Semi-Markov jump coupled neural networks: A two-time-scale event-triggered approach

The state estimation problem of singularly perturbed semi-Markov jump coupled neural networks is investigated in this paper, in which a two-time-scale event-triggered mechanism is proposed to adapt the two-time-scale character of the singularly perturbed coupled neural networks such that mitigate the burden of data communication. A dual-rate sampling strategy is used for slow and fast states such that the proposed two-time-scale event-triggered mechanism contains two independent event-triggered conditions corresponding to the slow and fast states respectively. Based on the two-time-scale event-triggered mechanism, sufficient conditions for the H∞ performance analysis of singularly perturbed semi-Markov jump coupled neural networks are established. Furthermore, under the circumstances that the singularly perturbed parameter does not exceed the presupposed upper bound, a design algorithm is developed for the two-time-scale state estimator to ensure that the error dynamics are stochastically stable. Finally, an example is used to illustrate the effectiveness of this method used in singularly perturbed semi-Markov jump coupled neural networks.

Swarm of Drones for Surveillance Monitoring of a Grounded Target: An event-triggered approach

In this paper, we present a novel formation control approach in the framework of Event-Triggered (ET) control to provide a solution to the surveillance problem. To do this, we identify two main challenges which are the switching topology of the drones and the limited bandwidth of the communication network, which are also valid in formation applications. To provide a solution to switching topologies, we propose a networked continuous controller that is robust in the presence of connection switching between drones and the target. Then, we propose a networked controller with ET communication in some aperiodic instants which reduces the required bandwidth and load within the communication network. We guarantee the stability of the developed ET controller and prove that the Zeno behavior cannot occur. To validate the method, we present realistic 3D simulation results conducted in the Simulink environment of Matlab® for different scenarios. The results of the study show the effectiveness of the proposed controller, especially for limited bandwidth channels as the ETC scheme has decreased the load within the communication network while resulting in a robust and efficient formation performance. We also considered moving target scenarios with missing possibilities to validate the robustness of the proposed method.

Fuzzy H ∞ control of nonlinear DC microgrids under aperiodic DoS attacks – an event-triggered approach

This paper studies the fuzzy H ∞ control of nonlinear DC microgrids subject to the dynamic event-triggered mechanism (ETM), aperiodic DoS attacks, noises and mismatching premises. First, using the information of DC microgrid's T-S fuzzy model and aperiodic DoS attacks, a discrete-time resilient dynamic ETM is proposed, which can save constrained system resources, as well as actively exclude attack-induced dropouts and Zeno behaviour. Second, a fuzzy switched system model is built, which provides a unified platform to evaluate effects of all the affecting factors such as the dynamic ETM and DoS attacks. Third, by constructing a piecewise Lyapunov functional, criteria for exponential stability with guaranteed H ∞ performance are obtained, and an event-triggered fuzzy injection current controller is further designed. Simulation results confirm that, in the presence of aperiodic DoS attacks and noises, the proposed controller can stabilise the nonlinear DC microgrids, while the dynamic ETM works well in reducing the triggering rate without dropouts. Tradeoffs can be made between control and communication resources, and the proposed fuzzy controller achieves shorter settling times and smaller overshoots than the robust controller.

An Adaptive Smooth Event-Triggered Approach to Recursive Estimation for Nonlinear Time-Varying Systems With Uncertain Communication Topologies

An Adaptive Smooth Event-Triggered Approach to Recursive Estimation for Nonlinear Time-Varying Systems With Uncertain Communication Topologies

Output-mask-based adaptive NN control for stochastic time-delayed multi-agent systems with a unified event-triggered approach

This paper investigates a class of output-mask-based adaptive neural network (NN) tracking control for nonlinear stochastic time-delayed multi-agent systems (STMASs) based on a unified event-triggered approach. The output signal relies on an output mapping acted as a mask, defined as a privacy-protection-like method, so that the internal state of one agent cannot be identified by other distrustful eavesdroppers or attackers. Moreover, the construction of a unified event-triggered control scheme retains the advantages of the saturation threshold triggering strategy, incorporates distributed errors, and increases the flexibility of thresholds. Furthermore, for stochastic time-delay multi-agent systems, the initial value limitation of the conventional first-order filter is removed by a first-order Levant differentiator, and a new estimation term in the fuzzy observer is established to solve the nonlinear fault. The unknown function in pure-feedback form is addressed via combining Butterworth low-pass filter and radial basis function neural networks (RBF NNs). Finally, the boundedness of all signals in the closed-loop systems is demonstrated, and the effectiveness of the proposed algorithm is verified by some simulation results.

Event-triggered prescribed-time tracking for uncertain nonlinear systems with unknown control gain and output constraints

This paper uses a prescribed-time event-triggered approach to explore the control of uncertain strict-feedback nonlinear systems with output constraints and an unknown time-varying control gain. The proposed method utilizes a universal barrier Lyapunov function (BLF) to ensure compliance with the output constraints. Furthermore, a technique known as congelation of variables is employed to handle the unknown time-varying control gain. Approximating the unknown dynamics using radial basis function neural network (RBFNN), a neural prescribed-time controller is designed based on the backstepping approach. Additionally, the event-triggered mechanism is employed to conserve computational and communication resources. With this scheme, all signals are guaranteed to converge to a compact set within the prescribed time frame. Finally, numerical simulations are presented to illustrate the effectiveness of the proposed algorithm.

Research on the optimization of the temperature field distribution of a multi-source microwave agent system based on event-triggered

Aiming at the problem of optimizing temperature distribution and heating efficiency in a multi-source microwave agent system, a collaborative tracking strategy using an event-triggering approach is explored. Firstly, setting the control object as a switch to optimize the system control process, a new cooperative switching strategy of event-triggered is designed to intensify the process of energy distribution by changing the time–frequency distribution of power. Secondly, exploring multi-agent architecture with an event-triggered multi-microwave source system to minimize communication frequency and energy consumption for faster agreement. Then, The material grouping strategy divides all materials into four categories based on their characteristics. Two materials, potato (group (a)) and SiC (group (c)) are selected for experimentation. Finally, a simple and easy-to-implement multi-source microwave numerical heating model was constructed using finite element analysis. The heating law described in this article was tested through experiments. The results showed that using this method increased the utilization rate of microwave energy in SiC by a maximum of 9.67% compared to traditional heating methods. Furthermore, it improved the temperature uniformity while maintaining excellent quality. Additionally, the energy utilization efficiency of potatoes was maximized by 8.76%, and the temperature uniformity was improved mostly by 12.04%.

Scheduling Networked Guidance Data Transmissions Based on a New Event-Triggering Approach

Scheduling Networked Guidance Data Transmissions Based on a New Event-Triggering Approach

Cooperative Control of Multiagent Systems: A Quantization Feedback-Based Event-Triggered Approach.

This article addresses the synchronization tracking problem for high-order uncertain nonlinear multiagent systems via intermittent feedback under a directed graph. By resorting to a novel storer-based triggering transmission strategy in the state channels, we propose an event-triggered neuroadaptive control method with quantitative state feedback that exhibits several salient features: 1) avoiding continuous control updates by making the parameter estimations updated intermittently at the trigger instants; 2) resulting in lower-frequency triggering transmissions by using one event detector to monitor the triggering condition such that each agent only needs to broadcast information at its own trigger times; and 3) saving communication and computation resources by designing the intermittent updating of neural network weights using a dual-phase technique during the triggering period. Besides, it is shown that the proposed scheme is capable of steering the tracking/disagreement errors into an adjustable neighborhood close to the origin, and the existence of a strictly positive dwell time is proved to circumvent Zeno behavior. Both theoretical analysis and numerical simulation authenticate and validate the efficiency of the proposed protocols.

Robust model predictive control for perturbed nonlinear systems via an error differential-integral based event-triggered approach

This paper aims to establish a new event-triggered model predictive control (MPC) strategy for perturbed nonlinear systems working in a networked environment, which can effectively save the computation and communication resources while ensuring control performance. The core of the framework is a new event-triggered mechanism, which is built by combining the differential and integral (D-I) information of the error between the optimal state and the actual state in a pre-specified time horizon. Based on such a triggering mechanism, a D-I based event-triggered robust MPC algorithm is proposed to stabilise the system and reduce the computation/communication resource consumption. In addition, sufficient conditions to ensure the feasibility and stability of the proposed MPC algorithm and avoid Zeno behaviour are obtained through rigorous theoretical analysis. Finally, the proposed control framework is implemented in the nonlinear cart-damper-spring and autonomous underwater vehicle systems to verify its effectiveness.

Remote State Estimation of Steered Systems with Limited Communications: an Event-Triggered Approach

Remote State Estimation of Steered Systems with Limited Communications: an Event-Triggered Approach

A Distributed Event-Triggered Approach for Decentralized Multi-Period Portfolio Optimization via the Alternating Direction Method of Multipliers

A Distributed Event-Triggered Approach for Decentralized Multi-Period Portfolio Optimization via the Alternating Direction Method of Multipliers

Optimized Distributed Filtering for Saturated Systems With Amplify-and-Forward Relays Over Sensor Networks: A Dynamic Event-Triggered Approach.

In this article, the optimized distributed filtering problem is studied for a class of saturated systems with amplify-and-forward (AF) relays via a dynamic event-triggered mechanism (DETM). The AF relays are located in the channels between sensors and filters to prolong the transmission distance of signals, where the transmission powers of sensors and relays can be described by a sequence of random variables with a known probability distribution. With the purpose of alleviating the communication burden and preventing data collision, the DETM is used to schedule the transmission cases of nodes by dynamically adjusting the triggered threshold according to the practical requirements. An upper bound matrix (UBM) of the filtering error (FE) covariance is first provided under the sense of variance constraint and the proper filter gain is further constructed via minimizing the proposed UBM. In addition, the boundedness evaluation regarding the trace of the UBM is provided. Finally, simulation experiments are used to illustrate the usefulness of the developed distributed recursive filtering scheme.

Resilient Output Control of Multiagent Systems With DoS Attacks and Actuator Faults: Fully Distributed Event-Triggered Approach

Resilient Output Control of Multiagent Systems With DoS Attacks and Actuator Faults: Fully Distributed Event-Triggered Approach

A Concurrent Event-Triggered Approach for Fuzzy Adaptive Control of Nonlinear Strict-Feedback Systems.

If a control input signal constructed by backstepping is used to generate an event-triggered control signal, then not only the real control input is discontinuous, but all the virtual control signals that make up the control signal are also discontinuous and have the same triggering instants with the control input. This fact makes the control design and stability analysis more difficult and complex via backstepping. This article focuses on this problem and works at coming up a new backstepping design procedure of event-triggered fuzzy adaptive control for nonlinear strict feedback systems. A concurrent event-triggered mechanism is proposed to ensure that the real control input and the virtual control signals have the same triggering instants. Then, a systemic backstepping design procedure is developed to construct event-triggered fuzzy adaptive controller. The Lyapunov technique for nonlinear impulsive system is employed to provide the closed-loop stability analysis. It is shown that the derived event-triggered fuzzy adaptive controller ensures that the closed-loop system output well follows the desired tracking trajectory and the other closed-loop signals remain bounded. Eventually, two examples are provided to further verify the applicability and reliability of the presented control scheme.

Dynamic event-triggered asynchronous filtering of Markovian jump systems against cyber-attacks

The paper focuses on the asynchronous filter design based on hidden Markovian model for Markovian jump systems with time-delay and external disturbances. Cyber nonlinearities in the communication environment are also considered. To further reduce the network bandwidth usage, an internal dynamic variable is introduced based on the previously extensively studied (static) event-triggered method, which is called dynamic event-triggered approach. The hidden Markovian model is utilized to characterize the asynchronous phenomenon caused by clock signals out of sync between system and filter mode. Then, according to the augmented filtering error system dynamics, some less conservative sufficient conditions are proposed to guarantee the stochastic stability with H∞ performance. Consecutively, the filter parameter-solving problem is synthesized by a convex optimization problem. Finally, two numerical simulations are presented to demonstrate the effectiveness of the proposed approach.