Adaptive Event-triggered Control Research Articles

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

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.

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

Reinforcement learning-based adaptive event-triggered control of multi-agent systems with time-varying dead-zone

In this paper, a novel reinforcement learning (RL)-based adaptive event-triggered control problem is studied for non-affine multi-agent systems (MASs) with time-varying dead-zone. The purpose is to design an efficient event-triggered mechanism to achieve optimal control of MASs. Compared with the existing results, an improved smooth event-triggered mechanism is proposed, which not only overcomes the design difficulties caused by discontinuous trigger signals, but also reduces the waste of communication resources. In order to achieve optimal event-triggered control, RL algorithm of the identifier-critic-actor structure based on fuzzy logic systems (FLSs) is applied to estimate system dynamics, evaluate control performance, and execute control behavior, respectively. In addition, considering time-varying dead-zone in non-affine MASs brings obstacles to controller design, which makes system applications more generalized. Through Lyapunov theory, it is proved that the optimal control performance can be achieved and the tracking error converges to a small neighborhood of the origin. Finally, simulation proves the feasibility of the proposed method.

Event-Triggered Adaptive Fixed-Time Trajectory Tracking Control for Stratospheric Airship

Event-Triggered Adaptive Fixed-Time Trajectory Tracking Control for Stratospheric Airship

Disturbance observer-based nonsingular fixed-time fuzzy adaptive event-triggered output feedback control of uncertain nonlinear systems

This article examines the fuzzy adaptive event-triggered output feedback control issue of eliminating fixed-time singularity for a type of uncertain nonlinear systems with disturbance observer structures. By introducing novel bounded time-varying functions (TVFs), a set of more general intermediate-variable-based disturbance observers (IVBDOs) is constructed. Combining the corresponding auxiliary reduced-power function and the quartic Lyapunov method, a co-design scheme for a fuzzy approximation high-order controller and an improved event-triggered mechanism with a judgment indicator threshold is proposed. Especially, the developed framework not only removes the singularity during the fixed-time design process but also further reduces the communication resources. By means of theoretical analysis, the devised solution achieves fixed-time stable control (FTSC) of the system and the tracking error converges to the desired neighborhood around the origin. After verifying the absence of Zeno behavior, the efficacy of the presented methodology is supported by two simulation examples.

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.

Integral BLF-Based Adaptive Dynamic Event-Triggered Boundary Control for a Flexible Riser System.

For the flexible riser systems modeled with partial differential equations (PDEs), this article explores the boundary control problem in depth for the first time using a dynamic event-triggered mechanism (DETM). Given the intrinsic time-space coupling characteristic inherent in PDE computations, implementing a state-dependent DETM for PDE-based flexible risers presents a significant challenge. To overcome this difficulty, a novel dynamic event-triggered control method is introduced for flexible riser systems, focusing on optimizing available control inputs. In order to save computational costs from the controller to the actuator, a dynamic event-triggered adaptive boundary controller is designed to effectively reduce boundary position vibrations. Additionally, considering external disturbances, an adaptive bounded compensation term is incorporated to counteract the influence of external disturbances on the system. Addressing boundary position constraints, a new integral barrier Lyapunov function (iBLF) tailored specifically for flexible riser systems is introduced, thereby alleviating conservatism in the controller design of flexible risers modeled by PDEs. At last, the validity of the proposed method is demonstrated through a simulation example.

Observer-Based Adaptive Event-Triggered Control for Nonlinear Networked Systems Under Multiple Cyber Attacks

Observer-Based Adaptive Event-Triggered Control for Nonlinear Networked Systems Under Multiple Cyber Attacks

Dynamic performance-guaranteed adaptive event-triggered trajectory tracking control for underactuated surface vehicles

This paper investigates the trajectory tracking control problem of underactuated surface vehicles in the presence of model uncertainties and external disturbances. A dynamic performance-guaranteed adaptive event-triggered control scheme is proposed to transform the original constrained control problem into an equivalent unconstrained problem and ensure that the trajectory tracking is achieved with prescribed performance. By developing a dynamic event-triggered mechanism with adjustable thresholds that can be updated in an adaptive way, the communication burden and actuator wear can be effectively alleviated. To avoid differential explosions that may increase system complexities, an improved transformation-adapted dynamic surface control (DSC) is designed to eliminate adverse effects induced by performance functions and make the DSC applicable to performance-transformed systems. By utilizing the parameter compression algorithm, model uncertainties and external disturbances can be effectively addressed, and the adaptive laws can be integrated into a concise form, with only two online updating parameters required. Comprehensive analysis is provided to verify that all error signals are semi-globally uniformly ultimately bounded (SGUUB). Compared to existing approaches, the proposed control scheme exhibits lower update frequency.

Decentralized Adaptive Event-Triggered Fault-Tolerant Cooperative Control of Multiple Unmanned Aerial Vehicles and Unmanned Ground Vehicles with Prescribed Performance under Denial-of-Service Attacks

Decentralized Adaptive Event-Triggered Fault-Tolerant Cooperative Control of Multiple Unmanned Aerial Vehicles and Unmanned Ground Vehicles with Prescribed Performance under Denial-of-Service Attacks

Adaptive event-triggered robust output regulation for uncertain switched T-S fuzzy systems

This paper focuses on the adaptive event-triggered robust output regulation issue for uncertain switched Takagi–Sugeno fuzzy systems with unknown exosystems and model uncertainty. The purpose is to pursue the output regulationcharacteristic and the disturbances suppression characteristic. First, an event-triggered mechanism is introduced to ease the communication burden. Then, on the basis of the event-triggered program, an adaptive event-triggered robust output regulation control law is developed to battle the uncertainty and disturbances. Further, constraints are arrived to force the solvability of the adaptive event-triggered robust output regulation issue for uncertain switched Takagi–Sugeno fuzzy systems by virtue of the average dwell-time way. In the end, a simulation instance with a mass–spring-damping model employed is arranged to showcase the practicality and effectiveness of the developed control methodology.

Adaptive event-triggered control for stochastic nonholonomic systems with unknown virtual control coefficients

Adaptive event-triggered control for stochastic nonholonomic systems with unknown virtual control coefficients

Disturbance-observer-based adaptive neural event-triggered fault-tolerant control for uncertain nonlinear systems against sensor faults

In this article, the adaptive event-triggered fault-tolerant control (FTC) issue of uncertain nonlinear systems suffering from sensor and actuator faults as well as external disturbances is studied. A disturbance observer (DO) is constructed to compensate for external disturbances and approximation errors generated by neural networks (NNs). Then, switching event-triggered control and command filtering techniques are introduced to balance the communication frequency and tracking performance of the controlled systems while avoiding “explosion of complexity” issue caused by iterative derivations of virtual controllers. Furthermore, compensation signals are designed to eliminate filtered errors. Finally, it is proven that the developed FTC scheme can esure that all signals are bounded and free from Zeno phenomenon. Simulation results of a spring damping mechanical system verifies the merits of the designed control algorithm.

Discrete-Time Event-Triggered Type-2 fuzzy wavelet neural network control for Multi-Motor servo system

This paper investigates a discrete-time event-triggered adaptive neural network control scheme for a multi-motor servo system (MMSS) to realize a desired position tracking performance. First, a discrete-time model of the MMSS, including a dead-zone and a nonlinear friction, is established based on the Euler’s discretization. Then, a discrete-time adaptive neural network controller is designed by integrating a type-2 fuzzy wavelet neural network (T2FWNN) and the backstepping technology. The neural network is not only used to estimate uncertain nonlinearities, but also can handle the non-causal problem caused by the conventional backstepping method. Meanwhile, a fixed threshold event-triggered mechanism along with the incorporation of a dead-zone operator is superimposed into the actual controller thus saving communicational resources. Besides, stability analysis proves that all the signals in the closed MMSS are bounded, and the position tracking error converges to a small neighborhood of the origin. Finally, abundant simulation experience results demonstrate the effectiveness and robustness of the proposed scheme.

Distributed adaptive event-triggered consensus control for high-order multi-agent systems

In this paper, a distributed adaptive event-triggered control approach is proposed for the consensus problem of high-order multi-agent systems (MASs) with unknown dynamics and external disturbances. First, to save communication resources, the event-triggered control is proposed to ensure the consensus of MASs, where the trigger condition is established based on the neighbors’ information. The approximation properties of neural networks are used to address the unknown dynamics and external disturbances. Second, rigorous theoretical analysis proves that the MASs can achieve the leader-following consensus, as long as the communication graph of MASs contains a spanning tree with the leader as the root node. The filtered tracking errors and adaptive parameters estimated errors are proved to be uniformly ultimately bounded without Zeno behavior. Finally, the simulations and experiments illustrate the feasibility and effectiveness of the theoretical results.

Event-triggered synchronization for delayed reaction–diffusion neural networks under hybrid deception attacks

This paper focuses on designing some event-triggered mechanisms to synchronize the delayed reaction–diffusion neural networks (DRDNNs) under hybrid stochastic deception attacks. Firstly, two different kinds of stochastic deception attacks are considered for DRDNNs system. Based on the distinct characteristics of the attack signals, a novel adaptive event-triggered control mechanism is proposed to synchronize DRDNNs under hybrid deception attacks subject to Lipschitz form, and a novel aperiodic event-triggered control mechanism is proposed to synchronize DRDNNs under hybrid deception attacks subject to bounded form. Using the Lyapunov theory and some inequality techniques, several event-triggered synchronization criteria for DRDNNs under hybrid deception attacks are established. Furthermore, Zeno behavior is strictly excluded by proving the existence of lower bounds for the intervals between any adjacent events in the two designed event-triggered mechanisms. Finally, two numerical examples are provided to illustrate the effectively and superiority of the two proposed event-triggered mechanisms.

Periodic event-triggered adaptive neural control of USVs under replay attacks

This work aims to address the control issue of unmanned surface vehicles (USVs) under replay attacks, where the influence of internal/external uncertainties and actuator’s physical constraints are taken into account. In the control design, a hyperbolic tangent function is used to replace the actuator saturation nonlinearity. To solve the design problem of mismatched unknown time-varying control gain caused by replay attacks, the single-parameter-learning technique is introduced, which avoids the operation of estimating for the unknown gain directly. To compensate for the effect of lumped uncertainties including attacks, unknown dynamics and external disturbances, the adaptive neural network with the finite covering principle is involved in the kinematic and dynamic channels, respectively. Furthermore, to reduce the update rate of actuator and alleviate the actuator wear, a periodic event-triggering mechanism (PETM) is established in the controller-actuator (C-A) channel. Finally, a periodic event-triggered (PET) adaptive neural tracking control solution for USVs under replay attacks is proposed, and it is verified that the control solution can force the trajectory of USVs to follow the reference trajectory even if there exists the effect of replay attacks. In addition, all signals in the closed-loop control system of USVs under replay attacks are bounded, and simulation and comparison results demonstrate the effectiveness of the control strategy.

Event-Triggered Adaptive Neural Control for MIMO Nonlinear Systems With Rate-Dependent Hysteresis and Full-State Constraints via Command Filter.

This article presents an event-triggered adaptive acrlong NN command-filtered control for a class of multi-input and multi-output (MIMO) nonlinear systems with unknown rate-dependent hysteresis in the actuator and the constraints on full states. The acrlong ETM is used to reduce the communication frequency between controller and actuator. The command filter technique is first employed to solve the dilemma between the nondifferentiable control signal at triggering instants and rate-dependent hysteresis input premise while avoiding the "explosion of complexity" problem. During the backstepping design, the barrier Lyapunov functions are utilized to guarantee that system states will stay in certain regions and the unknown nonlinear items are approximated by adaptive neural networks. The compensating signals are constructed to eliminate filtering errors. The estimates of unknown hysteresis parameters are updated by adaptive laws. The stability analysis is given and the effectiveness of the proposed method is verified by simulation.

Adaptive event-triggered security control for networked teleoperation systems.

An adaptive event-triggered security control method is proposed for networked robotic teleoperation systems subject to time-varying delays and false data injection (FDI) attacks. An event-triggered scheme is designed via the position and velocity signals of the master and slave robots, where the triggering thresholds can change adaptively with the system states. The position and velocity triggered signals and the feedback error between the operator and the environment forces are utilized to detect whether the transmitted signals suffered from FDI attacks. Then, a switching controller is designed, which is able to adopt the corresponding control strategy according to the corresponding detection result. The stability of the system and the convergence of the tracking errors are proved through Lyapunov functions, and the proposed method is validated through practical experiments. The proposed method is able to adaptively adjust the triggering frequency and effectively reduce the transmitted data, thus saving the network resources. Meanwhile, it can ensure the stability of networked teleoperation systems under time-varying delays and FDI attacks, as well as the position and force tracking performance.