Event-triggered Control Problem Research Articles

Adaptive Neural Event-Triggered Control of Networked Markov Jump Systems Under Hybrid Cyberattacks.

This article is concerned with the neural network (NN)-based event-triggered control problem for discrete-time networked Markov jump systems with hybrid cyberattacks and unmeasured states. The event-triggered mechanism (ETM) is used to reduce the communication load, and a Luenberger observer is introduced to estimate the unmeasured states. Two kinds of cyberattacks, denial-of-service (DoS) attacks and deception attacks, are investigated due to the vulnerability of cyberlayer. For the sake of mitigating the impact of these two types of cyberattacks on system performance, the ETM under DoS jamming attacks is discussed first, and a new estimation of such mechanism is given. Then, the NN technique is applied to approximate the injected false information. Some sufficient conditions are derived to guarantee the boundedness of the closed-loop system, and the observer and controller gains are presented by solving a set of matrix inequalities. The effectiveness of the presented control method is demonstrated by a numerical example.

Dynamic event-triggered control for discrete-time nonlinear Markov jump systems using policy iteration-based adaptive dynamic programming

This paper investigates a dynamic event-triggered optimal control problem of discrete-time (DT) nonlinear Markov jump systems (MJSs) via exploring policy iteration (PI) adaptive dynamic programming (ADP) algorithms. The performance index function (PIF) defined in each subsystem is updated by utilizing an online PI algorithm, and the corresponding control policy is derived via solving the optimal PIF. Then, we adopt neural network (NN) techniques, including an actor network and a critic network, to estimate the iterative PIF and control policy. Moreover, the designed dynamic event-triggered mechanism (DETM) is employed to avoid wasting additional resources when the estimated iterative control policy is updated. Finally, based on the Lyapunov difference method, it is proved that the system stability and the convergence of all signals can be guaranteed under the developed control scheme. A simulation example for DT nonlinear MJSs with two system modes is presented to demonstrate the feasibility of the control design scheme.

Event-triggered control for second-order multi-agent systems under denial-of-service attacks and white noises

Under the influence of additive communication noises and system noises, we investigate the event-triggered control problem for second-order multi-agent systems composed of double integrators or LC oscillators under random denial-of-service (DoS) attacks. Different from the previous cases where the attackers completely interrupt communication networks, we consider that attackers interrupt the communication network with a specific probability and can attack part or all communication links randomly. Based on this, the conditions on the attack duration and attack success probability are given when the system can still achieve consensus under random DoS attacks. In addition, the consensus bounds are expressed. Finally, two types of LC oscillator systems are used to illustrate the effectiveness of results.

Event-Triggered Adaptive Output-Feedback Control of Switched Stochastic Nonlinear Systems With Actuator Failures: A Modified MDADT Method.

This article investigates the adaptive event-triggered output-feedback control problem for a class of switched stochastic nonlinear systems with actuator faults. In the existing works, the developed results on adaptive control for switched stochastic nonlinear systems are almost based on the average dwell-time method, and how to construct a desired adaptive controller in the frame of the mode-dependent average dwell time (MDADT) remains a control dilemma. By presenting a general adaptive control rule based on the MDADT, this article implements the adaptive output-feedback control for the switched stochastic system under interest. In the process of controller design, fuzzy-logic systems, a flexible approximator, are utilized to approximate the unknown nonlinear functions. The dynamic surface design approach is employed to avoid taking derivatives of the constructed virtual controls to decrease the difficulty of complex calculation greatly. Meanwhile, a switched observer is designed to estimate the unknown states. In the frame of backstepping design, an event-triggered-based adaptive output-feedback controller is constructed such that all signals existing in the closed-loop system are ultimately bounded under a class of switching signals with MDADT property. Finally, the simulation results show the validity of the proposed control strategy.

Period-Sampled Switching Event-Triggered Control of Switched Linear Systems

This brief focuses on the event-triggered control (ETC) problem for a class of switched linear systems in which the controller updates the sampled information of both the system states and the switching signal only at the moment of event triggering. When the system switching occurs in the holding interval, the controller does not update the switching signal, which leads to the asynchronous switching behavior between the controller and the switched system. To solve the problem, we develop a new switching event-triggered mechanism (ETM) with periodic sampling by introducing a relative threshold function and an asynchronous related jump function with a tuning parameter. This period-sampled switching ETM can save computing resources and avoid Zeno behavior. Then, based on the multiple Lyapunov functional technique and the average dwell time (ADT) method, some sufficient conditions for global uniform exponential stability (GUES) of the closed-loop system are proposed. Besides, the corresponding solvability conditions for the co-design of the switching signal and the event-triggered controller are established. Finally, two numerical examples are provided to illustrate the effectiveness of the proposed method.

Event-triggered adaptive control of hypersonic vehicles subject to actuator nonlinearities

This paper investigates the event-triggered adaptive control problem for air-breathing hypersonic vehicles (AHVs) subject to physical actuator nonlinearities. In contrast with abundant AHV control strategies belonging to the traditional sample-data framework, the presented adaptive control works with an event-triggered mechanism, which naturally reduces the executing frequency of scramjet fuel-to-air equivalency ratio (FER) and aerodynamic control surface (ACS). During the event-triggered controller design, practical actuator characteristics including magnitude-rate composite constraints and servo transient dynamics are synthetically considered. In the velocity loop, the built-in FER saturation, which is a fusion of physical FER magnitude and rate constraints, is handled by reshaping the velocity reference with the help of an adaptive reference governor. Meanwhile in the altitude loop, a neural control allocator directly generates feasible ACS commands via a neural network that is well trained by means of the optimal control allocation. As the result, ACS magnitude-rate composite constraints and servo transient dynamics can be accommodated without implementing any time-consuming optimization searching process. Theoretical analysis indicates the guaranteed closed-loop stability and tracking performance while excluding the undesired Zeno behavior. Simulations validate the effectiveness of the proposed event-triggered adaptive control.

Dynamic event-triggered-based human-in-the-loop formation control for stochastic nonlinear MASs

The dynamic event-triggered (DET) formation control problem of a class of stochastic nonlinear multi-agent systems (MASs) with full state constraints is investigated in this article. Supposing that the human operator sends commands to the leader as control input signals, all followers keep formation through network topology communication. Under the command-filter-based backstepping technique, the radial basis function neural networks (RBF NNs) and the barrier Lyapunov function (BLF) are utilized to resolve the problems of unknown nonlinear terms and full state constraints, respectively. Furthermore, a DET control mechanism is proposed to reduce the occupation of communication bandwidth. The presented distributed formation control strategy guarantees that all signals of the MASs are semi-globally uniformly ultimately bounded (SGUUB) in probability. Finally, the feasibility of the theoretical research result is demonstrated by a simulation example.

Event-Triggered Cost-Guaranteed Control for Linear Repetitive Processes Under Probabilistic Constraints

In this article, the event-triggered cost-guaranteed control problem is investigated for a class of shift-varying linear repetitive processes (LRPs) with multiplicative noises under probabilistic constraints. First, in order to relieve the communication burden and improve the communication efficiency, the event-triggered mechanism is exploited for the shift-varying LRPs over the communication network of limited bandwidth. By constructing a novel event generator function, the order of the event triggering sequence is identified on two independent directions. Then, the probabilistic constraint is enforced onto the shift-varying LRPs under the influence of the event-triggered mechanism. With the aid of the Chi-square distribution and the completing-the-square technique, a controller design algorithm is proposed to ensure that both the probabilistic constraints and the quadratic cost index are satisfied by using the actual information at the triggering instants. Sufficient conditions are derived to guarantee the existence of event-based controllers, and the controller gains are obtained at each step by solving a series of matrix inequalities. Finally, an illustrative example is given to demonstrate the effectiveness of the proposed controller design method.

Event-Triggered Adaptive Fixed-Time Secure Control for Nonlinear Cyber-Physical System With False Data-Injection Attacks

This brief investigates the event-triggered adaptive fixed-time secure control problem for nonlinear cyber-physical systems (CPSs) under false data-injection attacks. By employing adding a power integrator technique, an adaptive controller along with the triggering mechanism is designed by using the compromised state variables. To compensate the effect of the unknown attacks, a dynamic switching function is introduced into the controller and a corresponding switching mechanism is developed based on the fixed-time stability theorem. Under the adaptive secure control scheme, the dynamic controller parameter can be regulated online to compensate the cyber attacks such that the fixed-time stability can be ensured for the considered nonlinear CPSs and Zeno behavior can be avoided. A simulation example is given to demonstrate the effectiveness of the proposed control strategy.

Dynamic Event-Triggered Communication-Based Adaptive Consensus Tracking of Uncertain Nonlinear Multiagent Systems in Pure-Feedback Form

This study is aimed at investigating a dynamic event-triggered communication-based adaptive distributed consensus control problem for a class of uncertain pure-feedback nonlinear multiagent systems with limited communication resources. The nonaffine nonlinear functions of multiagent systems are assumed to be unknown and heterogeneous, and intermittent inter-agent communication is considered to occur within a directed network. A novel adaptive distributed dynamic surface control strategy using neural networks is developed to manage non-differentiable virtual control laws associated with the intermittent communication between agents with uncertain nonaffine nonlinear parts. The key contribution of this research is the derivation of dynamic event-triggering conditions using distributed tracking errors to efficiently adjust inter-event times, while ensuring the consensus tracking performance and robustness against unexpected external disturbances. Compared with the existing static event-triggered communication approach, the proposed controller can alleviate the communication burden among agents. Using technical lemmas, it is shown that all signals of the considered system are semiglobally uniformly ultimately bounded, and Zeno behavior is strictly excluded. Comparative simulation results illustrate the effectiveness of the proposed dynamic event-triggered control approach.

Finite-time decentralized event-triggered feedback control for generalized neural networks with mixed interval time-varying delays and cyber-attacks

<abstract><p>This article investigates the finite-time decentralized event-triggered feedback control problem for generalized neural networks (GNNs) with mixed interval time-varying delays and cyber-attacks. A decentralized event-triggered method reduces the network transmission load and decides whether sensor measurements should be sent out. The cyber-attacks that occur at random are described employing Bernoulli distributed variables. By the Lyapunov-Krasovskii stability theory, we apply an integral inequality with an exponential function to estimate the derivative of the Lyapunov-Krasovskii functionals (LKFs). We present new sufficient conditions in the form of linear matrix inequalities. The main objective of this research is to investigate the stochastic finite-time boundedness of GNNs with mixed interval time-varying delays and cyber-attacks by providing a decentralized event-triggered method and feedback controller. Finally, a numerical example is constructed to demonstrate the effectiveness and advantages of the provided control scheme.</p></abstract>

Event-Triggered Fixed-Time Practical Tracking Control for Flexible-Joint Robot

This article studies the adaptive fuzzy event-triggered fixed-time practical tracking control problem for flexible-joint robot system. Since the nonlinearities of the system are difficult to obtain, fuzzy logic systems are utilized. Second-order command filters are used to avoid the “explosion of complexity” problem. Moreover, a novel compensation system is proposed. The new error compensation system cannot only compensate for the error of the filter band, but also make the error converge in fixed time. By using backstepping technique, the virtual control laws and the adaptive law are designed. Notice that compared to the reporting achievements, our proposed virtual control laws are second-order derivable by using the novel switch function, which avoids “singularity hindrance” problem. To reduce communication pressure, the event-triggered controller is designed and Zeno behavior is avoided. Our proposed control strategy ensures that the tracking error can be arbitrarily small in fixed time and all variables of the closed-loop system remain bounded. Finally, simulation results are given to show the effectiveness of our control strategy.

Event-Triggered Data-Driven Control for Nonlinear Systems Under Frequency-Duration-Constrained DoS Attacks

This paper addresses the event-triggered model free adaptive control (MFAC) problem for unknown nonlinear systems under denial-of-service (DoS) attacks, where the design and analysis are discussed under the data-driven framework. Firstly, by using the novel pseudo partial derivative, the nonlinear systems are converted into an equivalent data-relationship model. Then, the DoS attacks are described as limited by their frequency and duration, and without any more specific assumptions about the attack structure or strategy. Next, a novel event-triggered MFAC scheme is proposed. By employing the Lyapunov stability theory, the stability performance is analyzed. Furthermore, a compensation algorithm is designed to against the adverse impact brought by the DoS attacks. Finally, simulations including a numerical example and a load frequency control (LFC) example for multi-area power systems are given to demonstrate the validity and applicability of the proposed schemes.

Data-driven control of hydraulic servo actuator: An event-triggered adaptive dynamic programming approach.

Hydraulic servo actuators (HSAs) are often used in the industry in tasks that request great power, high accuracy and dynamic motion. It is well known that an HSA is a highly complex nonlinear system, and that the system parameters cannot be accurately determined due to various uncertainties, an inability to measure some parameters and disturbances. This paper considers an event-triggered learning control problem of the HSA with unknown dynamics based on adaptive dynamic programming (ADP) via output feedback. Due to increasing practical application of the control algorithm, a linear discrete model of HSA is considered and an online learning data driven controller is used, which is based on measured input and output data instead of unmeasurable states and unknown system parameters. Hence, the ADP-based data driven controller in this paper requires neither the knowledge of the HSA dynamics nor exosystem dynamics. Then, an event-based feedback strategy is introduced to the closed-loop system to save the communication resources and reduce the number of control updates. The convergence of the ADP-based control algorithm is also theoretically shown. Simulation results verify the feasibility and effectiveness of the proposed approach in solving the optimal control problem of HSAs.

Event-Triggered Sliding Mode Control for Spacecraft Reorientation With Multiple Attitude Constraints

This paper addresses the event-triggered attitude control problem for spacecraft anti-unwinding reorientation with multiple attitude constraints in the presence of external disturbance. Based on a designed novel potential function(PF), a new sliding mode controller is proposed to guarantee the completeness of anti-unwinding reorientation with multiple attitude constraints and external disturbance. Different from existing continuous attitude reorientation controller, the static and dynamic event-triggered mechanisms, related with the sliding mode variable and disturbance bound, are then constructed. With the designed event-triggered sliding mode control schemes, the attitude system asymptotic stability, the satisfaction of attitude constraints with anti-unwinding action and free of Zeno phenomenon despite on external disturbance are proved theoretically. Numerical simulations results confirm that the proposed methods can significantly reduce resource usage while guaranteeing the desired performance.

Event-Triggered Neural Network Control for LTI Systems

This paper addresses the problem of event-triggered control (ETC) of discrete-time linear time-invariant (LTI) systems stabilized by neural network controllers. The event-triggering mechanisms (ETMs) is proposed to update only a portion of the layers required to maintain stability and satisfactory performance of the feedback system, thus reducing the computational cost associated with the evaluation of the neural network. Sufficient convex conditions in the form of linear matrix inequalities (LMIs) are provided to compute the triggering parameters and characterize an estimate of the domain of attraction for the feedback system. The formulation is based on the use of Lyapunov theory and a set of generalized sector constraints that deal with the nonlinear activation functions, represented in this case by the saturation function. Optimization procedures are also formulated to effectively reduce the amount of computation in the neural network. An example borrowed from the literature is used to illustrate the effectiveness of the proposal.

Passive Fault-Tolerant Control for NCSs Using Event-Triggered Approach

In this paper, the passive event-triggered control problem for networked control systems with actuator faults is of concern. A sufficient condition is proposed by a Lyapunov-Krasovskii functional, under which such type continuous-time Networked control systems with actuator faults are asymptotically stable and passive. The designing method of controller is developed by using Wirtinger inequality and linear matrix inequalities (LMIs). The given strategy can reduce the number of transmissions, thus saving the communication resources. Finally, two numerical examples are provided to demonstrate the effectiveness of the proposed method.

Dynamic event-triggered adaptive control for uncertain stochastic nonlinear systems

In this paper, the dynamic event-triggered adaptive tracking control problem is investigated via backstepping technology for uncertain stochastic nonlinear systems. First, the stochastic nonlinear system with unknown parameter is considered. By introducing an additional dynamic variable, a dynamic event-triggered adaptive controller is designed such that the closed-loop signals are uniformly ultimately bounded in the sense of the fourth moment. Then, a more general partially unknown stochastic nonlinear system is further considered, and the designed adaptive neural network control scheme ensures that the closed-loop signals are fourth moment semi-globally uniformly ultimately bounded (SGUUB). The proposed dynamic event-triggering mechanism (DETM) guarantees that the lengths of time intervals between each two consecutive events are lower-bounded by a positive constant. It is necessary to point out that the DETM is better at saving resources than the static event-triggering mechanism (SETM). Finally, two simulations are conducted to show the validity of the control strategies for these two systems, respectively.

Finite-time event-triggered containment control of multiple Euler–Lagrange systems with unknown control coefficients

This paper researches the finite-time event-triggered containment control problem of multiple Euler–Lagrange systems (ELSs) with unknown control coefficients. To realize an accurate convergence time, the settling-time performance function is employed to ensures the steady-state and dynamic properties of the containment errors in the resulting system. Meanwhile, to handle unknown control coefficients, adaptive neural networks (ANNs) with an additional saturated term are designed, which removes the requirement of full rank control coefficients in traditional control methods. By establishing an event-triggered mechanism, a novel finite-time event-triggered containment control law is designed, which yields the semi-global practical finite-time stable (SGPFS) of the resulting closed-loop system without Zeno phenomenon according to the finite-time stability criterion. The effectiveness of the designed methodology is verified by simulation.

Finite-Time Adaptive Fuzzy Switching Event-Triggered Control for Nonaffine Stochastic Systems

This article considers the problem of finite-time adaptive fuzzy switching event-triggered control for a class of nonaffine stochastic systems with periodic actuator faults and asymmetric error constraints. First, a framework of semiglobally finite-time stability in probability is established for stochastic systems. Then, unlike the existing observers, an adaptive state observer with faults compensation mechanism is designed to estimate unknown state. In order to balance the systems tracking performance and communication burdens, a switching event-triggered strategy is given to regulate trigger signal and avoids the Zeno behavior effectively. In backstepping process, a nonlinear tracking error-dependent function is constructed to constrain the tracking errors within asymmetric boundaries, and the effects of faults and trigger errors can be compensated completely by the designed finite-time fault-tolerant control strategy. Finally, the effectiveness of presented strategy is further verified by simulation results.