Adaptive Event-triggered Control Research Articles

Adaptive event-triggered control for uncertain nonlinear systems with unknown measurement sensitivity via output feedback

Adaptive event-triggered control for uncertain nonlinear systems with unknown measurement sensitivity via output feedback

Event-triggered adaptive finite-time synchronization control for dual-motor servo systems with uncertainties and torque disturbance

Event-triggered adaptive finite-time synchronization control for dual-motor servo systems with uncertainties and torque disturbance

Adaptive event-triggered secure control for IT2 fuzzy systems vulnerable to hybrid attacks and its application

This work addresses the adaptive event-triggered control issue that interval type-2 (IT2) fuzzy systems are vulnerable to hybrid attacks.They were initially developed for fuzzy systems by thoroughly addressing the characteristics of hybrid attacks, which include deception attacks and replay attacks. Our main goal is to mitigate the effects of hybrid attacks and protect communication resources by developing an event-triggered controller for IT2 fuzzy systems with adjustable thresholds. The proposed hybrid attacks are described by a stochastic random variable that satisfies the Bernoulli distribution. Based on Lyapunov-Krasvoskii stability theory, the resulting closed-loop IT2 fuzzy system clearly satisfies sufficient criteria to ensure mean square stability. Also, the weight matrices of the trigger condition and controller gain matrices are obtained using LMI techniques. Lastly, a numerical example is provided to illustrate the effectiveness and applicability of the proposed control strategy.

Double adaptive event-triggered strategy for leader-following group consensus of heterogeneous multi-agent systems in cooperative–competitive networks

This article investigates adaptive event-triggered control for leader-following group consensus of hybrid heterogeneous multi-agent systems (HMASs) composed of single- and double-integrator agents under cooperative–competitive relationships. By using fully distributed control protocol and adaptive protocol, the leader-following group consensus can be achieved without global information. Additionally, a novel adaptive double dynamic event-triggered mechanism (DDETM) is proposed, in which two independent adaptive event-triggered conditions are devised to maintain events of communication and updates of controllers. Furthermore, adaptive parameters and internal dynamic variables are employed to reduce the number of triggers and exclude Zeno phenomenon. By utilizing Lyapunov stability theory, some sufficient conditions are acquired for the leader-following group consensus of the HMASs. Finally, simulation examples are given to prove the effectiveness of the proposed method.

H∞ adaptive ETF control for degenerate jump systems under actuator faults and impulsive deception attacks with application to DCMIP device

This article considers the issue of H∞ adaptive event-triggered feedback control for degenerate jump systems under actuator faults and impulsive deception attacks. Based on periodic sampling strategy, a mode-dependent adaptive event-triggered scheme is developed, which can save communication resources, avoid the Zeno phenomenon and take full advantage of Markovian mode information. By constructing the neoteric Lyapunov-Krasovskii (L-K) functional containing piecewise linear impulsive auxiliary functions and applying singular value decomposition (SVD) technique, the fresh stochastic admissibility conditions for degenerate Markovian jump systems under actuator faults and impulsive deception attacks are obtained under the framework of linear matrix inequalities (LMIs), and then the adaptive event-triggered scheme with weighted matrices and feedback controller are designed simultaneously. By applying freedom weight matrix method and the property that the network-induced delay derivative is one, the non-strict LMI conditions are transformed into strict ones, such that the stochastic admissibility of the degenerate jump systems and state feedback controller gains are realized adequately. Finally, a direct current motor-controlled inverted pendulum (DCMIP) device is used to confirm the feasibility of the presented approach.

Attack-Dependent Adaptive Event-Triggered Security Fuzzy Control for Nonlinear Networked Cascade Control Systems Under Deception Attacks

This article investigates the issue of H∞ security output feedback control for a nonlinear networked cascade control system with deception attacks. First, to further reduce the amount of communication data, reasonably schedule network resources, and alleviate the impact of multi-channel deception attacks, an attack-dependent adaptive event-triggered mechanism is introduced into the primary network channel, and its adaptive triggered threshold can be adjusted according to the random attack probability. Secondly, the output dynamic quantization of the secondary network channel is considered. Then, a novel security cascade output feedback controller design framework based on the Takagi–Sugeno (T-S) fuzzy networked cascade control system under deception attacks is established. In addition, by introducing the Lyapunov–Krasovskii stability theory, the design conditions of the controller are given. Finally, the effectiveness and superiority of the proposed design strategies are verified by two simulation examples of power plant boiler–turbine system and power plant boiler power generation control system.

Event-Triggered Backstepping Control of Fractional-Order Chaotic Systems with Dead Zone Via Disturbance Observer

This paper comes up with an adaptive event-triggered tracking control scheme for a kind of fractional-order strict feedback nonlinear systems with actuator dead-zone inputs and uncertain external disturbances. The presented strategy can overcome the defect in standard backstepping control scheme which leads to the “explosion of complexity” problem of virtual signals, and strengthen the transmission efficiency by an event-triggered scheme. An error compensation mechanism is presented for promoting the tracking accuracy and reducing the effect filter error. The designed controller guarantees that the tracking error converges into a small neighborhood near the origin, and all closed-loop signals are bounded. Moreover, Zeno behavior will be avoided. The numerical simulation is given to verify the accuracy and effectiveness of the designed technique.

Adaptive Event-Triggered Consensus Control of Nonlinear Multi-Agent Systems via Output Feedback Methodology: An Application to Energy Efficient Consensus of AUVs

For dealing with the energy consumption in multi-agent systems (MASs), an event-triggered (ET) methodology is promising, which relies on the activation of communication devices only when communication of data is needed. This paper explores the leaderless consensus for nonlinear MASs using an adaptive ET approach via an output feedback methodology. This adaptive ET scheme is preferred as it can adapt to the environment through setting a communication threshold. The proposed approach renders the observed states of agents by use of nonlinear observers in an output feedback control dilemma, making it more practical. Simple Luenberger observers are developed to avoid the problem of always measuring agents’ states. The strategy of adaptive ET-based control is employed to minimize resource use and information transmission. Design conditions for the observer-based adaptive ET consensus control of nonlinear MASs have been derived via a Lyapunov function, containing state estimation error, consensus error, adaptation term, and nonlinearity bounds. In contrast to the existing methods, the present approach applies a more practical output feedback schema, uses adaptive ET proficiency, and deals with nonlinear agents. An example of a formation of autonomous underwater vehicles achieving the basic consensus realization between displacement and velocity is included to illustrate the viability of the resultant approach.

Distributed two-channel dynamic event-triggered adaptive finite-time fault-tolerant containment control for multi-leader UAV formations

This paper addresses the distributed adaptive finite-time containment control problem in multi-leader UAV formations with actuator failures, limited communication, and external disturbances. A two-channel dynamic event-triggered strategy based on adaptive and sliding mode control is proposed as a containment control scheme, which solves the contradiction between the need to include containment error in the continuous term of the sliding mode surface in the adaptive law and the discontinuity of the communication between neighboring subsystems (follower UAVs) by constructing intermediate variables. Unlike the traditional event triggering condition that can only be applied to asymptotically convergent systems, the dynamic triggering condition designed in this paper, based on auxiliary variables, hyperbolic tangent function, and adaptive techniques, realizes finite-time convergence during the adjustment of the system and sets a more reasonable lower limit for the triggering thresholds, thus further reducing the communication frequency and ensuring the finite-time convergence of the system. The stability of the closed-loop system can be proved by the Lyapunov theorem. Zeno behavior can be avoided. The simulation results verify the effectiveness of the developed control algorithm.

Fuzzy Adaptive Event-Triggered Consensus Control for Nonlinear Multiagent Systems Under Jointly Connected Switching Networks.

This article studies the fuzzy adaptive event-triggered (ET) consensus control issue of nonlinear multiagent systems (NMASs) under jointly connected switching networks. Since the leader and its high-order derivatives are unknown under jointly connected switching networks, a novel distributed ET reference generator equipped with an ET mechanism is constructed to estimate them. Meanwhile, the continuous information transmission among agents is avoided and the network channel utilization is optimized. Subsequently, fuzzy logic systems (FLSs) are employed to approximate unknown dynamics, and a fuzzy adaptive ET consensus control algorithm only using intermittent communication is designed by backstepping control methodology. It is demonstrated that all the closed-loop signals are semi-globally uniformly ultimately bounded (SGUUB), with the tracking errors converging to a small neighborhood around zero. Finally, we apply the developed fuzzy adaptive ET consensus control algorithm to unmanned surface vehicles (USVs), and the simulation results verify the effectiveness of the proposed ET consensus control algorithm.

Adaptive event-triggered stochastic estimator-based sampled-data fuzzy control for fractional-order permanent magnet synchronous generator-based wind energy systems

This study aims to develop an adaptive event-triggered estimation sampled-data control method for a fractional-order permanent magnet synchronous generator (PMSG)-based wind energy system (WES) using the Takagi–Sugeno (T–S) fuzzy approach. Unlike existing PMSG-based WES control schemes, we propose an aperiodic event-triggered communication scheme with an adaptive mechanism to reduce data transmission. To address the challenge of limited communication resources, we introduce an adaptive event-triggered (AET) estimation method with aperiodic sampling, significantly reducing communication overhead while maintaining stable WES performance. This method employs transmission techniques based on absolute errors, resulting in high data transmission efficiency. First, the fractional-order model for the PMSG-based WES is represented using linear sub-models through the T–S fuzzy approach. Next, a fuzzy aperiodic AET mechanism is proposed for the PMSG model with augmented estimation error systems. Then, the fractional Lyapunov function theory is employed to derive linear matrix inequalities (LMIs), ensuring bounded mean-square stability for the PMSG-based WES with the augmented estimation error systems. Additionally, the desired estimator control gains are determined through solvable LMIs. Finally, simulation studies are presented to demonstrate the superiority and feasibility of the proposed control scheme.

Event-triggered adaptive tracking control for uncertain nonlinear systems with preassigned prescribed performance constraints under three cases

Event-triggered adaptive tracking control for uncertain nonlinear systems with preassigned prescribed performance constraints under three cases

Robust adaptive event-triggered control for unmanned surface vessel–unmanned aerial vehicle: Application to sea–air synchronized lawn mowing search operation

This paper investigates a robust adaptive event-triggered control algorithm with a consideration of the synchronized lawn mowing search guidance for a heterogeneous system comprising both an unmanned surface vessel (USV) and an unmanned aerial vehicle (UAV). For ensuring a cooperative global search navigation of the USV–UAV, a synchronized lawn mowing search guidance is developed with two key elements: the L1-based virtual ship (L1VS) and L1-based virtual aerial (L1VA), where the former can generate the smooth reference path for a USV according to the search task scope, while the latter can program the reference path for a UAV on the basis of its search requirements (search speed, detected capacity and communication distance). In the control loop, a robust adaptive path following control law is designed by utilizing an event-triggered mechanism and a minimal learning parameter (MLP) technique. In particular, the event-triggered mechanism has a potential role for a reduction of the unnecessary transmission resource usage for the channel of the controller to the actuator. Besides, only one learning parameters on each channel by introducing the MLP technique, implying a lower computational burden. Through the Lyapunov theorem, the semi-global uniform ultimate bounded (SGUUB) stability properties of the proposed control system. Finally, two numerical simulations are carried out to evaluate the effectiveness of the proposed strategy and the advantages compared with existing techniques.

Event-Triggered Adaptive Neural Impedance Control of Robotic Systems.

This article presents an event-triggered adaptive neural impedance control (ETANIC) scheme for robotic systems, where the combination of impedance control (IC) and event-triggered mechanism can significantly reduce the computational burden and the communication cost under the premise of ensuring the stability and tracking performances of the robotic systems. The IC is used to achieve the compliant behavior of the robotic systems in response to the environment. The uncertainties of the robotic systems are estimated by the radial basis function neural network (RBFNN), and the update laws for RBFNN are derived from the designed Lyapunov function. The stability of the whole closed-loop control system is analyzed by the Lyapunov theory, and the event-triggered conditions are designed to avoid the Zeno behavior. The numerical simulation and experimental tests demonstrate that the proposed ETANIC scheme can achieve better efficiency for controlling the robotic systems to perform the interaction tasks with the environment in comparison to the adaptive neural IC (ANIC).

Switching Event-Triggered Adaptive Neural Network Control for Switched Nonlinear Systems Under Hybrid Attacks

Switching Event-Triggered Adaptive Neural Network Control for Switched Nonlinear Systems Under Hybrid Attacks

Adaptive Event-Triggered Saturation-Tolerant Control for Multiagent Systems Based on Finite-Time Fuzzy Learning

Adaptive Event-Triggered Saturation-Tolerant Control for Multiagent Systems Based on Finite-Time Fuzzy Learning

Event-triggered distributed consensus control of heterogeneous multi-agent system under communication and actuator faults

In this paper, a heterogeneous leader-follower multi-agent system is studied under simultaneous time-varying communication faults and actuator faults. First, the state of the leader is modeled as the closed-loop reference model (CRM) where the states of the direct-connected followers are fed to the leader to improve the leader-follower tracking capability. An event-triggered communication mechanism is designed for the agent information sharing among its neighbors so as to reduce the communication burden. The designed event-triggered mechanism, capable of adjusting the triggering interval threshold within a certain range, can be applied in practical multi-robot collaborative control to accommodate the varying requirements for different triggering intervals. Considering the time-varying communication link failures, a new distributed event-triggered observer is designed for each follower to estimate the system states so as to reduce the state error, whereas the adaptive distributed event-triggered estimators are further designed for the non-directly connected followers to estimate the coefficient matrix of the leader system. Then, an estimator is designed for the actuator fault estimation to mitigate their impact on the system consistency. Finally, an adaptive control strategy is proposed to ensure the consistency of the leader-follower system under the time-varying communication link faults and actuator faults. It is also shown that Zeno behavior is excluded for each agent and the effectiveness of the proposed adaptive event-triggered control strategy is verified on a heterogeneous multi-agent system.

Distributed adaptive event-triggered finite-time fault-tolerant containment control for multi-UAVs with input constraints and actuator failures

This article investigates the distributed adaptive finite-time containment control problem for multi-UAVs with input constraints, actuator failures, communication limitations, and external disturbances. First, a new smoothing function is used to smooth the asymmetric input constraint signals so that the input constraint and actuator fault control problem can be transformed into a variable gain control problem. Subsequently, a new Nussbaum function is proposed to solve the variable gain control problem. A new adaptive event-triggered strategy is designed to solve the communication limitation problem, and the trigger threshold has the characteristic of adaptive adjustment that can be dynamically decreased. In response to external disturbances, an adaptive law is designed to estimate and compensate for the boundaries of disturbances. It follows from the analysis based on Lyapunov theory that under the proposed controller, the followers will converge to the convex envelope formed by the leaders in a finite time, and Zeno-free is achieved. Simulation results are provided to verify the effectiveness of the developed adaptive event-triggered finite-time fault-tolerant containment control laws.

Event-triggered adaptive fault-tolerant control with pre-specified performance for AUVs trajectory tracking

This paper investigates the problem of fault-tolerant control of the preset performance of autonomous underwater vehicles (AUVs) in case of external disturbances and actuator failures. To begin with, a specific new nonlinear auxiliary function with dual performance constraints is designed to establish the foundation for achieving prescribed performance in the closed-loop system. Subsequently, an event-triggered fault tolerant controller employing Nussbaum gain technique is introduced to accomplish the precision trajectory tracking control of the AUVs by predetermining control index. It is shown that the effect by resulting in actuator faults is on-line compensated by an adaptive estimator. Theoretical analyses demonstrate the boundedness of the signals of the designed closed-loop control system, and the Zeno phenomenon can be effectively avoided. Finally, simulation experiments are conducted to verify the effectiveness of the proposed methods.

Adaptive event-triggered impulsive control for stochastic complex networks with delays under deception attacks

Adaptive event-triggered impulsive control for stochastic complex networks with delays under deception attacks