Adaptive Event-triggered Control Research Articles

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.

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.

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 .

Adaptive Event-Triggered Fixed-Time Practical Tracking Control for Uncertain Nonlinear Systems

Adaptive Event-Triggered Fixed-Time Practical Tracking Control for Uncertain Nonlinear Systems

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.

Resilient adaptive event-triggered containment control of nonlinear multi-agent system under concurrent DoS attacks and disturbances

This paper presents a secure containment control problem of nonlinear Multi-Agent Systems (MASs) under aperiodic Denial of Service (DoS) attacks and external disturbances simultaneously. A novel adaptive neural network (NN)-based event-triggered control is considered that uses the nonlinear estimator to predict the state of other agents. Since data access is denied during DoS attacks, the overall system switches between two modes of stable and unstable containment behaviours. Therefore, the maximum of attack duration and frequency is determined such that the overall system evolution leads to containment convergence in the presence of DoS attacks. We proposed an adaptive NN-based distributed disturbance observer to estimate external disturbances in a nonlinear system's dynamics. The state estimator predicts neighbouring agents' states, and each agent's input and event times are determined without monitoring other agents. The directed graph topology is used to determine data exchange among agents instead of an undirected graph that reduces implementation conditions. Zeno-free behaviour is also proved by analysis of the system. Eventually, the numerical simulation of the proposed approach is shown. Abbreviations: DoS attacks, Containment control of multi-agent system

Event-Triggered Adaptive Finite-Time Control Using a Fuzzy State Observer for Nonlinear Cyber-Physical Systems Under Deception Attacks.

This article presents a new event-triggered adaptive finite-time control strategy using a fuzzy state observer for a class of nonlinear cyber-physical systems (CPSs) under malicious deception attacks with a more general form. Compared with the traditional assumptions on the deception attacks in the existing results, a more general assumption on deception attacks is given in this article. During the design process, real system states are initially estimated by developing an improved state observer, which effectively addresses the problem of state unavailability. Then, a coordinate transformation technology, in which the estimated states of observer are considered, is presented to stabilize the studied system. By constructing the singularity-free finite time virtual controls, the singularity problem in the traditional finite time design algorithms is cleverly avoided. Furthermore, to minimize communication overhead, a final finite-time controller is established by using a relative threshold event-triggered scheme. The developed event-triggered adaptive finite-time control strategy guarantees that all signals in the closed-loop system are semi-globally bounded in finite time without Zeno behavior. Finally, the correctness of the proposed control strategy is validated through two simulation results.

Dynamic event-triggered adaptive control for state-constrained strict-feedback nonlinear systems with guaranteed feasibility conditions

Dynamic event-triggered adaptive control for state-constrained strict-feedback nonlinear systems with guaranteed feasibility conditions

Dynamic event-triggered adaptive control for state-constrained strict-feedback nonlinear systems with guaranteed feasibility conditions

Dynamic event-triggered adaptive control for state-constrained strict-feedback nonlinear systems with guaranteed feasibility conditions

Hybrid Adaptive Event-Triggered Consensus Control with Intermittent Communication and Control Updating

Hybrid Adaptive Event-Triggered Consensus Control with Intermittent Communication and Control Updating

Decentralized Adaptive Event-Triggered Fault-Tolerant Synchronization Tracking Control of Multiple UAVs and UGVs With Prescribed Performance

Decentralized Adaptive Event-Triggered Fault-Tolerant Synchronization Tracking Control of Multiple UAVs and UGVs With Prescribed Performance

Fixed-time event-triggered adaptive control of manipulator system with input deadzone and model uncertainty

There exist universal model uncertainty and input deadzone in mechatronic motion plant due to unknown model parameters and actuator physical feature, which will degrade the motion performance and stability. In this study, a fixed-time event-triggered adaptive control is proposed in n-DOF manipulator system to address these problems. Firstly, the dynamic model of n-DOF manipulator with joint friction and input deadzone is constructed by Lagrange technique. Then a radial basis function neural network (RBFNN) is designed to approximate uncertain dynamics. Meanwhile, an event-triggered mechanism (ETM) is used to reduce the control update frequency and to save the communication resource. Furthermore, an adaptive estimation law is adopted to compensate unknown input deadzone parameters. Based on backstepping iteration technique, a fixed-time convergent controller is presented to guarantee the system state errors convergence to zero neighborhood in finite time irrelevant to arbitrary initial state. Finally, the effectiveness of the proposed control scheme is verified by sufficient comparative simulation and experimental results.

Neuroadaptive event-triggered tracking control for nonlinear systems with dynamic fault feedback and prescribed time convergence

This paper investigates the problem of event-triggered adaptive prescribed time tracking control for nonlinear systems with combining multiplicative and additive dynamic sensor fault. A simple state observer is designed by fusing fault output and reference signal to estimate unmeasurable states, where the unknown unmeasurable uncertain functions are approximated by neural network technology. Then, the prescribed time performance function is constructed based on the time scale function, and a suitable function transformation is introduced that the tracking error can reach the desired residual set in the prescribed time, where the initial value of the error can be arbitrarily selected regardless of the prescribed time performance function. Moreover, the desired accuracy and the prescribed time can be specified on request. The designed adaptive event-triggered controller can effectively compensate the sensor fault in the output feedback control and reduce the communication burden, and the feasibility of the proposed method is verified by the simulation result.

Adaptive event-triggered exponential convergence tolerant control for fuzzy communication delay network systems with actuator faults, deception attacks and disturbances

Adaptive event-triggered exponential convergence tolerant control for fuzzy communication delay network systems with actuator faults, deception attacks and disturbances

Fully distributed adaptive event-triggered bipartite containment control of linear multiagent systems with actuator faults

This paper studies a new type of fully distributed double event-triggered bipartite containment (DETBC) problem for linear multiagent systems (MASs). Meanwhile, multiple leaders with non-zero inputs and time-varying multiplicative and additive actuator faults (TMAAFs) are considered. First, in contrast to traditional event-triggered mechanisms (ETMs), the proposed novel double ETMs can guarantee that information transfer between agents and controller updates proceeds asynchronously. Some adaptive parameters are introduced in the trigger functions to enhance the dynamic adjustment ability of double ETMs. The trigger threshold depends on the relative state-dependent, which is independent of continuous information from neighbors. Then, a fault-tolerant DETBC protocol is designed to solve actuator fault problems. Moreover, compared with other relevant works, our control protocol is fully distributed, which avoids reliance on global information. Finally, a simulation experiment is conducted to validate the feasibility of the designed protocol.

Event-Triggered Adaptive Containment Control for Heterogeneous Stochastic Nonlinear Multiagent Systems.

This article investigates the event-triggered adaptive containment control problem for a class of stochastic nonlinear multiagent systems with unmeasurable states. A stochastic system with unknown heterogeneous dynamics is established to describe the agents in a random vibration environment. Besides, the uncertain nonlinear dynamics are approximated by radial basis function neural networks (NNs), and the unmeasured states are estimated by constructing the NN-based observer. In addition, the switching-threshold-based event-triggered control method is adopted with the hope of reducing communication consumption and balancing system performance and network constraints. Moreover, we develop the novel distributed containment controller by utilizing the adaptive backstepping control strategy and the dynamic surface control (DSC) approach such that the output of each follower converges to the convex hull spanned by multiple leaders, and all signals of the closed-loop system are cooperatively semi-globally uniformly ultimately bounded in mean square. Finally, we verify the efficiency of the proposed controller by the simulation examples.

Event-Triggered Adaptive Neural Network Tracking Control for Uncertain Systems With Unknown Input Saturation Based on Command Filters.

This brief presents a modified event-triggered command filter backstepping tracking control scheme for a class of uncertain nonlinear systems with unknown input saturation based on the adaptive neural network (NN) technique. First, the virtual control functions are reconstructed to address the uncertainties in subsystems by using command filters. A piecewise continuous function is employed to deal with the unknown input saturation problem. Next, an event-triggered tracking controller is developed by utilizing the adaptive NN technique. Compared with standard NN control schemes based on multiple-function-approximators, our controller only requires a single NN. The closed-loop system stability is analyzed based on the Lyapunov stability theorem, and it is shown that the Zeno behavior is also avoided under the designed event-triggering mechanism. Simulation studies are performed to validate the effectiveness of our controller.

Adaptive Event-Triggered Time-Varying Output Group Formation Containment Control of Heterogeneous Multiagent Systems

Adaptive Event-Triggered Time-Varying Output Group Formation Containment Control of Heterogeneous Multiagent Systems

Decentralized Output-Feedback Adaptive Event-Triggered Control for Interconnected Nonlinear Delay Systems with Actuator Failures

This paper investigates decentralized adaptive event-triggered fault-tolerant control for interconnected nonlinear delay systems with actuator failures. The actuator failures suffered include loss of effectiveness and bias faults. A control scheme based on the K-filter is proposed, which effectively compensates for the effects of unknown actuator failures. A hyperbolic tangent function and neural network are introduced to approximate the unknown interconnection function and nonlinear delay function. By introducing the dynamic surface control method, the “explosion of complexity” issue is addressed. Furthermore, our proposed controller can ensure that all states of the corresponding closed-loop system are semi-globally uniformly ultimately bounded and that the tracking error can converge to a small neighborhood of zero. Meanwhile, Zeno behavior can be effectively avoided. Finally, the validity of the proposed control scheme is verified using a simulation example.

Guaranteed Performance Event-Triggered Adaptive Consensus Control for Multiagent Systems under Time-Varying Actuator Faults

A guaranteed performance event-triggered adaptive consensus control is established for uncertain multiagent systems under time-varying actuator faults. To eliminate the impact caused by actuator faults, an adaptive neural network compensation strategy is developed. Simultaneously, by implementing the asymmetric barrier Lyapunov function and transform function, a prescribed time consensus control with guaranteed performance, is constructed. Furthermore, to reduce the frequency of information transmission, an adjustable switching event-triggered control (ASETC) is proposed by using a modified hyperbolic tangent function. It combines the advantage of the relative threshold strategies and the characteristics of the hyperbolic tangent function, giving better flexibility in saving network resources and guaranteeing system performance. By applying the constructed control method, systems with prescribed performance consensus in a prescribed time are achievable while limited network resources and unknown time-varying faults are present. Some simulation examples implemented in MATLAB (R2022a) are given to demonstrate the above results.