Event-triggered Control Scheme Research Articles

Finite-time $${H_\infty }$$ synchronization of Markov jump complex dynamical networks with additive time-varying delays: an event-triggered control strategy

Finite-time $${H_\infty }$$ synchronization of Markov jump complex dynamical networks with additive time-varying delays: an event-triggered control strategy

Distributed event-triggered consensus control for a class of nonlinear multi-agent systems under switching topologies

A distributed event-triggered consensus control protocol is proposed for a class of nonlinear multi-agent systems with switching topologies. The nonlinear dynamics and switching topologies are considered simultaneously to adapt to some actual scenarios. To economize the communication resources, a distributed event-triggered mechanism is established to decrease the consumption of communication resources. The new event-triggered mechanism is constructed by the information of distributed errors, which can fully utilize communication relationships among agents to control the timing of events. A class of negative exponential functions is added to the event-triggered condition as the event-triggered mechanism is applied for the presented multi-agent systems. The design of event-triggered condition can reduce the occurring frequency of events when the adjusting time of control scheme begins at an initial small amount period of time. Finally, the simulation results are provided, which verify the feasibility and effectiveness of the designed event-triggered consensus control strategy for the presented multi-agent systems with switching topologies.

Disturbance Prediction-Based Adaptive Event-Triggered Model Predictive Control for Perturbed Nonlinear Systems

A disturbance prediction-based adaptive event-triggered model predictive control scheme is proposed for nonlinear systems in the presence of slowly varying disturbance. The optimal control problem in the model predictive control scheme is formulated by taking advantage of a proposed central path-based disturbance prediction approach, and the event-triggered mechanism is designed to be adaptive to the triggering interval. As a result, the proposed scheme improves the state prediction precision and, hence, reduces greatly the triggering frequency. Furthermore, for input-affine nonlinear systems, the disturbance separation and compensation techniques are developed to further enlarge the triggering interval. The theoretical analysis of the algorithm feasibility and closed-loop stability, as well as numerical evaluations of the effectiveness of the proposed schemes, is also given.

An Improved Co-Design Method of Dynamical Controller and Asynchronous Integral-Type Event-Triggered Mechanisms

This article addresses an improved co-design method of dynamical controller and asynchronous integral-type event-triggered mechanisms (ETMs) for a class of linear systems with external disturbances and measurement noises. First, a dynamical controller is designed for a linear disturbed plant, and two independent integral-type ETMs are synthesized to be embedded in the plant output and control input channels. Then, an augmented hybrid system is constructed, in which the integral-type ETMs in the two channels are not required to be activated synchronously and both channels are affected by their measurement noises. The proposed asynchronous integral-type event-triggered control (IT-ETC) scheme for two-fold signal transmissions can not only avoid the Zeno behavior strictly, but save more communication resources than the static event-triggered control (S-ETC) strategy. Moreover, a criterion is provided to guarantee that the hybrid system is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathcal {L}}_{2}$ </tex-math></inline-formula> stable, and an improved co-design method is further synthesized to simultaneously obtain the design parameters of ETMs and feasible solutions of the dynamical controller. As a result, a tradeoff can be achieved between the robustness of the control system and the occupancy rate of communication resources. Compared with the S-ETC strategy, the simulation results have illustrated the effectiveness and superiority of the proposed asynchronous IT-ETC scheme.

Observer-Based Dynamic Event-Triggered Adaptive Control of Distributed Networked Systems With Application to Ground Vehicles

In this paper, a distributed observer-based event-based practical consensus control for networked systems subject to adaptive nonlinear couplings is investigated. Considering that the node's state is not always measurable and the finite communication bandwidth exists in engineering systems, two distributed adaptive event-triggered control protocols subject to nonlinear couplings are proposed under observer-based dynamic output feedback. Then, distributed dynamic event-triggered control policies corresponding to two above mentioned adaptive controllers are proposed, respectively. The proposed nonlinear coupled event-triggered control schemes not only overcome continuous communication among nodes, but also relax the requirement of relying on global information for achieving a distributed practical consensus. Finally, the theoretical analysis is verified through a team of practical networked ground vehicles implemented thorough Matlab/Similink and Robot Operating Systems (ROS) environment.

선형 파라미터 변형 시스템에 대한 이벤트-트리거 외란 관측기 기반 강인 제어

In this paper, an event-triggered disturbance observer-based robust control strategy is proposed for linear parameter-varying systems. In the proposed control strategy, the disturbance observer estimates the plant’s states and external disturbances, and the robust tracking controller uses the estimated values to compensate for the external disturbance. The scenario of the transmission of the estimated values from the disturbance observer through shared networks was considered. An event-triggered observer scheme was proposed to reduce communication resources while ensuring the stability of the closed-loop system. A stability analysis was performed by considering external disturbances. The simulation results demonstrated that the output follows the desired value, compensating for external disturbances.

Adaptive event-triggered control for uncalibrated visual servoing of robot manipulators

In the study, an adaptive event-triggered control strategy is proposed for image-based visual servoing of eye-to-hand robot manipulators, where the camera used does not need to be calibrated, and the dynamics behavior of manipulator is considered in design and analysis. To address the uncertainty in camera parameters and the nonlinearity in robot dynamics, and accommodate the errors between the real-time control signals and the piecewise-constant event-triggered control signals, a new and novel robust adaptive estimation approach is developed, and well fused with visual feedback control design so that the boundedness of all closed-loop signals, and the asymptotic convergence of image error towards zero are established simultaneously. Besides rigorous proof in theory, the obtained results are also confirmed by comparative simulation tests.

Attack resilient strategy for event-triggered distributed control scheme of multi-energy systems

In recent years, distributed algorithms have been increasingly used to solve the economic dispatch (ED) problem of multi-energy systems (MES) due to the advantages of high flexibility, strong robustness, and privacy. However, the MES based on the distributed optimization architecture must bear higher cyber-attack risks, so as to maintain the safe and stable operation of MES. To address this issue, an event-triggered fully distributed algorithm is proposed to solve the ED problem, which can effectively mitigate the communication burden. On this basis, an attack resilient strategy against false data injection (FDI) attacks is implemented in the proposed fully distributed algorithm, which can eliminate incorrect measurement of incremental cost and power generation data caused by cyber-attacks. In addition, a reputation value protocol embedded in the proposed attack resilient strategy is designed to effectively reduce the potential of direct isolation of the node. Finally, case studies are given in this paper to validate the effectiveness of the proposed distributed control scheme on a 9-bus MES.

Adaptive fuzzy dynamic surface control for nonstrict-feedback nonlinear state constrained systems with input dead-zone via event-triggered sampling

In this paper, a novel adaptive fuzzy dynamic surface control (DSC) via the event-triggered mechanism is developed for nonstrict-feedback nonlinear state constrained systems preceded by actuator dead-zone. The utilization of the dynamic surface technique avoids the issue of repeated differentiation of virtual control law and eliminates the drawback of the explosion of complexity with the backstepping technique. By utilizing the universal approximation property of fuzzy logic systems (FLSs), the unknown function is approximated successfully. By combining the DSC and event-triggered control (ETC) scheme, communication and computation strain are alleviated contemporaneously. An adaptive controller and an auxiliary system are cooperatively constructed to offset the effect of dead-zone nonlinear function. By employing the barrier Lyapunov functions (BLFs), all the states can remain within a prescribed interval. The tracking error can be restricted at the origin and all signals of closed-loop systems can be kept within the boundaries by the designed parameter adaptive laws and event-triggered controller. Finally, a numerical simulation result and a practical simulation result of the pendulum are proposed to guarantee the effectiveness of the presented control theory.

The optimal event-triggered impulsive control of a stochastic rumor spreading model incorporating time delay using the particle swarm optimization algorithm

In the digital age, rumor spreading is becoming more widespread and faster than ever before, and results in the more social panic and instability. Because of this, it is crucial to implement effective control strategies to prevent the continued spread of rumors, and avoid all kinds of unnecessary harm caused by rumors. In this paper, a stochastic rumor spreading model incorporating time delay within the framework of the event-triggered impulsive control (ETIC) strategies are presented. To begin with, the stability problem of this model is discussed and proved. Besides, the optimal ETIC strategies are explored by the particle swarm optimization (PSO) algorithm. Furthermore, some numerical simulations are performed to illustrate the optimal ETIC strategies of the given model. In addition, a real case is used to prove the validity of given model. Finally, the following conclusions are drawn that the stochastic model is feasible and consistent with actual rumor propagation trends, and ETIC strategies can help control rumors effectively. Meanwhile, different ETIC strategies should be used according to the different situations of rumor spreading. For instance, control strategies need to be more frequent and robust when transmission rates are higher or time delay are shorter.

Neural network-based adaptive event-triggered control for cyber–physical systems under resource constraints and hybrid cyberattacks

In this paper, the neural network (NN)-based adaptive event-triggered control (AETC) strategy is developed for cyber–physical systems (CPSs) under resource constraints and hybrid cyberattacks. First, by considering the nonperiodic denial-of-service (DoS) attacks that only constrain their frequency and duration and removing the strict assumption on the malicious information injected by deception attacks in existing literature, an unexplored hybrid cyberattack model is proposed. Second, a novel NN-based adaptive event-triggered state-feedback controller is designed, which not only flexibly solves the resource constraints of CPSs while considering the adverse effects of DoS attacks, but also adopts an on-line weight-adjusted NN to approximate and mitigate the malicious information injected by deception attacks. Then, the switched delay system model is established for the closed-loop control system with the designed novel controller. By carefully constructing a switched Lyapunov–Krasovskii functional and using the switched delay system theory and NN technology, the explicit form of the designed novel controller is obtained, which can ensure that the closed-loop control system is exponentially asymptotically stable (EAS). Finally, a numerical example is given to illustrate the effectiveness of the proposed strategy.

Decentralized event-triggered sliding mode scheme for frequency regulation of multi-area power systems under deception attacks

This paper investigates the frequency regulation problem of networked multi-area power systems subject to random deception attacks. Due to the increasing integration of wireless communication devices in power systems, the frequency stability highly relies on the efficiency and security of information transmission. To alleviate the communication burden, a decentralized dynamic event-triggered (ET) scheme is proposed, in which each area transmits the measurement via its own ET rule. Moreover, the coefficients of dynamic variables are designed based on the state variations and the attack probability for different areas. Then, a decentralized ET sliding mode controller is proposed to restore the frequency. Both the mean square exponentially ultimate boundedness of the power systems and reachability of the specified sliding surface are analyzed, and the corresponding conditions are given. Compared with the widely adopted centralized schemes, the proposed decentralized ET control scheme is easier to implement and can ensure more reasonable transmission considering attack probability for different areas; meanwhile, the deception attacks on different areas are considered to occur independently. Finally, simulation results of a three-area power system are provided.

Observer-based event-triggered adaptive neural control for time-delay nonlinear systems with input saturation and external disturbances

In this article, the issue of adaptive event-triggered tracking control is investigated for time-delay nonlinear systems with input saturation and external disturbances. In the whole process of control design, the radial basis function neural networks are utilized to approximate uncertain nonlinearities. To estimate the unknown states, a neural network-based observer is constructed. Pade approximation method is adopted to eliminate the effect of input delay. A smooth non-affine function is introduced to replace input saturation, and an auxiliary variable is employed to obtain the actual control input. An event-triggered strategy is designed to reduce the utilization of communication and computation resources. Moreover, the command filtering technique is applied to handle the issue of “explosion of complexity” in the conventional backstepping method. The designed controller can assure that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded. Therefore, the proposed event-triggered neural control scheme can not only save network resources, but also improve the robustness of the system by dealing with input constraints and external disturbances. Finally, two simulation examples are given to verify the availability and feasibility of the designed controller.

Dissipative control of switched nonlinear systems with sliding mode: an event-triggered control scheme

This paper introduces the event-triggered scheme to study the problems of dissipative control and dissipativity-based sliding mode control (SMC) for switched nonlinear systems. Firstly, based on a designed event-triggered scheme, a piecewise continuous state feedback controller is constructed; sufficient conditions for $$\left( Q,S,R\right) $$ - $$\alpha $$ -dissipativity of the resulting system are derived by the approaches of a switched Lyapunov function and an average dwell time. Secondly, these two methods are extended to the study of $$H_\infty $$ performance and passive performance; the corresponding sufficient conditions are provided. Thirdly, the dissipative control problem is solved by introducing an event-triggered scheme into SMC method. Sufficient conditions for dissipativity of the resulting system are derived, and an SMC law is designed to guarantee the reachability. At the end of this paper, the correctness of the proposed methods is proved by two numerical examples.

Fuzzy finite-time consensus control for uncertain nonlinear multi-agent systems with input delay

A fuzzy finite-time consensus control method for a class of uncertain nonlinear multi-agent systems (MASs) with input delay is developed. The Pade approximation approach and Laplace transform technology are employed to eliminate the effect of input delay. Then, the fuzzy logic systems are applied to deal with the uncertain nonlinear terms. Furthermore, a relative threshold event-triggered control scheme is established to alleviate the communication burden of the control network. Meanwhile, to ensure MASs can synchronize in finite time, an adaptive consensus control method is developed. With the proposed method, even if the MASs suffer from input delay, all followers in the MASs reach finite-time synchronization and track the virtual leader. Finally, the validity of the proposed method is illustrated by the theoretical analysis and simulation examples.

Event-triggered constrained neural critic control of nonlinear continuous-time multiplayer nonzero-sum games

In this article, by exploiting the event-triggered neural critic control scheme, the optimal control question of nonlinear continuous-time multiplayer nonzero-sum games with asymmetric control constraints is investigated. First, to satisfy the asymmetric constraints, we propose an appropriate nonquadratic function. Also, the time-based coupled Hamilton-Jacobi (HJ) equations and the optimal control policies are deduced. To save communication resources and improve control efficiency, the event-driven mechanism is innovated. Further, the event-triggered optimal control strategies and the event-triggered coupled HJ equations are given. Afterwards, a novel triggering condition is established, and the critic neural networks are built to evaluate the optimal cost functions, thereby obtaining the event-triggered near-optimal controls. During neural critic learning, a new weight adjustment rule is proposed. Moreover, by exploiting the Lyapunov approach, the stability of the system state and the weight estimation errors is guaranteed. Finally, two examples are given to verify the efficacy of the established event-triggered neural critic control scheme.

Sampling-Position-Based Event-Triggering Consensus of Nonlinear Second-Order Agents

In this brief, we study the consensus seeking problems for second-order nonlinear multi-agent systems with unavailable velocity measurements. An event-triggered control strategy is developed based on causally sampled position data only, which can reduce the controller update frequencies significantly and avoid continuous communications effectively. Some sufficient conditions for achieving consensus are obtained in light of Lyapunov-Krasovskii stability theory and linear matrix inequality technique.

Event-Triggered Adaptive Control of Coupled Hyperbolic PDEs With Piecewise-Constant Inputs and Identification

In this article, we present an event-triggered boundary control scheme with a, likewise, event-triggered batch least-squares parameter identification for a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$2\times 2$</tex-math></inline-formula> hyperbolic PDE-ODE system, where two coefficients of the in-domain couplings between two transport PDEs, and the system parameter of a scalar ODE, are unknown. The triggering condition is designed based on evaluating both the actuation deviation caused by the difference between the plant states and their sampled values, and the growth of the plant norm. When either condition is met, the piecewise-constant control input and parameter estimates are updated simultaneously. For the closed-loop system, the following results are proved: first, the absence of a Zeno phenomenon; second, finite-time exact identification of the unknown parameters in most situations; third, exponential regulation of the plant states to zero. In the numerical simulation, the design is verified in an application of axial vibration control of a mining cable elevator, where the damping coefficients of the cable and the cage are unknown.

Finite-time adaptive neural network event-triggered output feedback control for PMSMs

This article investigates the finite-time adaptive neural network event-triggered output feedback control for the permanent magnet synchronous motor (PMSM) systems. The addressed PMSM systems include unknown nonlinear dynamics and immeasurable states. The neural networks are utilized to approximate the unknown nonlinear dynamics and an equivalent control design model is established, by which a neural network state observer is given to estimate the immeasurable states. By constructing an event-triggered mechanism and under the framework of adaptive backstepping control design technique and finite-time stability theory, a finite-time adaptive event-triggered output feedback control scheme is developed. It is proved that the proposed control scheme ensures the closed-loop system to be stable and the angular velocity, stator current and other state variables remain bounded in a finite time. Finally, the computer simulation is provided to confirm the effectiveness of the presented controllers.

ESO-based adaptive event-triggered load following control design for a pressurized water reactor with samarium–promethium dynamics

Conventional control systems used in nuclear reactors rely on real-time information transmission between the controllers and the actuators, which is unnecessary and imposes a heavy communication burden. To this end, this paper proposes an extended state observer-based adaptive event-triggered control (ESO-AETC) scheme for the load following problem of a pressurized water reactor (PWR) in the presence of uncertainties and limited communication resources. Different from most of the previous controllers presented in the literature, the samarium–promethium dynamics are considered in the constructed PWR model in this paper. Based on the PWR model with the load-dependent parameters and the samarium–promethium dynamics, an ESO, which can estimate lumped uncertainties and unmeasured system states including the relative density of the delayed neutron precursor, the average fuel temperature, the xenon concentration, the iodine concentration, the samarium concentration, the promethium concentration, and the total reactivity, is designed. With the help of the ESO and the event-triggered techniques, an AETC scheme is developed to avoid the real-time communication in the controller-to-actuator channel of the PWR system. It is rigorously proved that the control error can converge to any prescribed residual set in a finite time according to Lyapunov stability theory, excluding the Zeno behavior. Finally, simulation studies involving comparisons with existing works, estimation performance verification, and sensitivity analysis are conducted to assess the proposed ESO-AETC scheme. Simulation results reveal that (1) relative to the pre-existing model-free controller (MFC) and the disturbance observer-based sliding mode controller (DO-SMC), the proposed ESO-AETC scheme is found to provide better load following performance with at least 56.12% lower maximum absolute error (MAE), at least 15.05% lower integral absolute error (IAE), and 87.58% reduced communication resources in the controller-to-actuator channel, (2) the ESO possesses strong estimation capability for system states and lumped uncertainties, and (3) the proposed ESO-AETC scheme yields a low sensitivity with respect to the change of the controller parameters.