Event-triggered Control Problem Research Articles

Collision-Risk-Based Event-Triggered Optimal Formation Control for Mobile Multiagent Systems Under Incomplete Information Conditions

This article deals with collision-risk-based event-triggered optimal formation control problems for mobile multiagent systems. First, several collision-risk-related definitions, such as collision-free margin, moving direction angle, collision risk angle, and collision risk level, are proposed for the moving agents. Then, a collision-risk dependent, time-varying, event-triggered heterogeneous communication network topology is developed, where the agent starts obtaining information of the neighboring agents only when collision risks occur among them. Third, an anti-collision control law, which is composed of a switch function, a control force direction function, and a control strength function, is designed to guarantee the collision avoidance formation of multiagents. Fourth, to ensure the formation quality and save control cost of the multiagent system, an optimal formation control scheme with feedforward compensation is designed. Simulation results illustrate that: 1) by using the collision risk information of mobile agents, the proposed control scheme is effective to realize the collision avoidance optimal formation task and 2) the anti-collision formation controller can be implemented with incomplete information of the agents.

Adaptive Dynamic Programming-Based Event-Triggered Robust Control for Multiplayer Nonzero-Sum Games With Unknown Dynamics1-4mmPlease verify and confirm the term "Multi-Player" has been changed to "Multiplayer" in the title of this article.

In this article, the event-triggered robust control of unknown multiplayer nonlinear systems with constrained inputs and uncertainties is investigated by using adaptive dynamic programming. To relax the requirement of system dynamics, a neural network-based identifier is constructed by using the system input-output data. Subsequently, by designing a nonquadratic value function, which contains the bounded functions, the system states, and the control inputs of all players, the event-triggered robust stabilization problem is converted into an event-triggered constrained optimal control problem. To obtain the approximate solution of the event-triggered Hamilton-Jacobi (HJ) equation, a critic network for each player is established with a novel weight updating law to relax the persistence of excitation condition based on the experience replay technique. Furthermore, according to the Lyapunov stability theorem, the present event-triggered robust optimal control ensures the multiplayer system to be uniformly ultimately bounded. Finally, two simulation examples are employed to show the effectiveness of the present method.

Periodic Event-Triggered Dynamic Feedback Synchronization Control of Discrete-Time Neural Networks.

This article investigates the event-triggered synchronization control problem of discrete-time neural networks (DNNs) in the case of periodic sampled-data. A discrete-time periodic event-triggered mechanism is adopted to evaluate the measurements, which avoids formulating the triggering function in a continuous manner and saves energy consumption. Under this framework, an event-triggered dynamic output-feedback controller is designed to achieve the goal of synchronization. A piecewise Lyapunov functional is constructed to analyze the sawtooth-like pattern of sampled-error signals. Thereafter, the synchronization criteria are formulated for the considered DNNs. The co-designed issue is further discussed for the control gains and triggering parameter. Finally, a simulation example is presented to show the effectiveness of the proposed method.

Distributed Dynamic Event-Triggered Secon- dary Control for Islanded Microgrids With Disturbances and Communication De- lays: A Hybrid Systems Approach

This paper investigates the secondary control problem of islanded microgrid (MG) under hybrid systems framework. Owing to network-induced imperfections caused by communication and limited communication resources, we consider the event-triggered secondary control problem under time-varying delays. A hybrid dynamic event-triggering mechanism (DETM) is proposed to reduce the usage of communication resources, the event triggering interval is enforced to be lower bounded by strictly positive value. A hybrid closed-loop model is constructed, that incorporates the communication delays and event-triggering mechanism (ETM), and it facilitates stability and performance analysis. Lyapunov-based stability conditions are proposed, while the maximum allowable delay can be determined. Moreover, the proposed hybrid DETM ensures that the minimum event-triggering interval (METI) can be designable and pre-specified, even in the presence of disturbances, which is important for control implementation. Finally, the effectiveness of the proposed control strategy is illustrated by simulation examples.

Hybrid Event-Triggered Intermittent Control for Nonlinear Multi-Agent Systems

A novel hybrid event-triggered intermittent control problem for a class of nonlinear multi-agent systems is addressed. The system dynamics are reconstructed to describe the nonlinear multi-agent systems via the intermittent control method and a hybrid event-triggered mechanism. This study represents the first attempt at designing a control time sequence determined by the hybrid event-triggered mechanism to replace the predesigned control time sequence. Moreover, a consensus tracking control strategy is presented for the reconstructed multi-agent systems, and the results are extended to the case involving multiple leaders. The control strategy can solve the problems associated with the artificial selection of the control time sequence and unnecessary control input consumption. There are two simulation examples presented to demonstrate the effectiveness and feasibility of the control strategies.

Adaptive backstepping control of high-order fully actuated nonlinear systems with event-triggered strategy

This paper investigates the problem of adaptive event-triggered fuzzy control for nonlinear high-order fully actuated systems. In this paper, a completely unknown nonlinear function is considered, and its prior knowledge is unknown. To solve this problem, the fuzzy logic system technology is applied to approximate the unknown nonlinear function. In order to save communication resources, a novel high-order event-triggered controller is proposed under backstepping control. With the help of Lyapunov stability theory, it is proved that all signals of the closed-loop system are bounded. Finally, the theoretical results are applied to the robot system to verify their validity.

Observer-based dynamic event-triggered secure control for nonlinear networked control systems with false data injection attacks

This paper considers the problem of observer-based dynamic event-triggered secure control for nonlinear networked control systems (NCSs) under false data injection attacks (FDIA). A novel output feedback controller is developed by utilizing a sampled-data observer to relieve the impacts of unmeasured states and FDIA. To make full use of the limited network resources, a dynamic periodic event-triggered mechanism (PETM) with an auxiliary variable is designed, which can reduce the transmission of information. Then, an approximation of injected false data is implemented based on an adaptive neural network (NN) in the presence of FDIA. Based on the proposed control strategy, the boundness of the nonlinear NCSs can be ensured by selecting some proper parameters. Finally, the effectiveness and validity of the proposed control strategy are illustrated via simulation of a single link robot arm system.

Event-Triggered Consensus Control for Multirobot Systems With Cooperative Localization

In multi-robot systems, the accurate localization of each mobile robot in the team is a prerequisite to reach consensus. This paper investigates the problem of event-triggered consensus control for a group of mobile robots based on cooperative localization (CL). In our framework, each robot employs the position estimates from CL to jointly achieve consensus. An event-triggered mechanism based on a mixed-type condition is adopted in order to reduce the frequency of control updates and unnecessary transmission of information between system components. Our goal is to design an event-triggered consensus controller based on CL such that the closed-loop system achieves the prescribed consensus in spite of inaccurate sensor measurements. We provide sufficient conditions that guarantee the desired consensus using eigenvalues and eigenvectors of the Laplacian matrix. We design the controller and filter gains as well as the parameters of the event-triggering mechanism simultaneously in terms of the solution for a linear matrix inequality (LMI). Finally, simulation and experimental results are used to demonstrate the effectiveness of proposed approach.

Robust Nonlinear MPC With Variable Prediction Horizon: An Adaptive Event-Triggered Approach

This paper investigates the event-triggered model predictive control (ETMPC) problem for nonlinear systems with the bounded disturbance. First, a novel adaptive event-triggered mechanism without Zeno behaviors, in which the triggering threshold can constantly be adjusted with the change of the system state, is proposed for computational load reduction. Then, an adaptive prediction horizon update strategy (APHUS) is proposed to further reduce the computational complexity of the optimization problem at each triggering instant. Moreover, a dual-mode ETMPC algorithm is developed, and sufficient conditions on the algorithm feasibility and the system robust stability are provided. Through a simulation example, the results show that the proposed scheme can use fewer computational resources and a shorter calculation time for solving the optimization problem while ensuring satisfactory system performances than the existing ones.

Asynchronous event-triggered guaranteed cost control of two-dimensional Markov jump Roesser systems

This paper deals with the problem of asynchronous event-triggered guaranteed cost control for two-dimensional Markov jump systems in Roesser model. The hidden Markov model is introduced to characterize the asynchronous phenomenon induced by imperfect mode detection, and a hidden Markov model-based event-triggered mechanism is employed in controller design to reduce the communication burden of the network. By using the quadratic Lyapunov function technique, some sufficient criteria are developed to ensure the asymptotic stability of the addressed system with a guaranteed cost value under three different boundary conditions. Finally, an optimization algorithm with linear matrix inequality constraints is formulated to design the optimal asynchronous event-triggered guaranteed cost control law, and a simulation example is considered to illustrate its validity.

Prescribed performance event-triggered fault-tolerant control of uncertain pure-feedback nonlinear systems

The prescribed performance event-triggered fault-tolerant control (FTC) problem is considered for a class of uncertain pure-feedback nonlinear systems with actuator failures. Contrary to the existing results, the novel decoupling condition and the universal construction mode of error transformation are first given. Based on this, a new radical constrained function is used to low-complexity controller design without involving the Nussbaum gain scheme or adaptation parameter updated structure, in spite of nonlinear coupling and event triggering input. It is proved that the desired prescribed accuracy of the closed-loop output tracking can be achieved and the number of event-trigger showed a significant reduction. The effectiveness of theory results is verified by simulation on a numerical example.

Dynamic event-triggered formation maneuver in cooperative marine surface vehicles control over directed communication networks

This article focuses on the dynamic event-triggered formation control problem for multiple marine surface vehicles (MSVs) considering model uncertainties and external disturbances under a unidirectional communication topology. Firstly, a model-parameter-free control strategy is developed to compensate for the adverse effect of parameter uncertainty and time-varying external disturbances by resorting to the sliding mode philosophy and the adaptive law. In contrast to backstepping-based formation control schemes, the proposed control scheme circumvents the complexity explosion, and there is no complex calculation process. Thus, it has a relatively simple structure. After that, to reduce the communication frequency among inter-MSVs, a dynamic event-triggered control (DETC) mechanism is developed. Compared with the existing event-triggered mechanism, the proposed controller ensures fewer triggering instants among vehicles. Finally, theoretical analysis and numerical simulations have shown the effectiveness and superiorities of the proposed methods.

Event-triggered tracking control for nonlinear time-varying delay systems with full state constraints and unknown control coefficients

This paper addresses the problem of event-triggered control for a class of nonlinear systems with unknown control coefficients, time-varying delays, full state constraints and external disturbances, simultaneously. Firstly, based on integral Barrier-Lyapunov Functionals, an adaptive event-triggered controller is designed to account for the impacts of complete state constraints, unknown time-varying delays and external disturbances. Using fuzzy logic systems, the unknown functions of systems are then roughly estimated. The system redundancy can be significantly reduced by implementing the event-triggered control technique in the meanwhile. By introducing the separation technique and Lyapunov–Krasovskii functionals, it is shown that the strategy can ensure tracking performance greatly and all the closed-loop signals of the system are bounded. Compared with the existing results, the proposed design scheme is less conservative and has a wilder application range. Finally, the simulation results show the effectiveness of the proposed approach.

Dynamic event-triggered synchronisation control for complex dynamical networks with stochastic attacks and actuator faults

ABSTRACT This paper studies the problem of dynamic adaptive event-triggered synchronisation control for a class of complex dynamical networks (CDNs) with parameter uncertainty and stochastic network attacks. In order to save communication resources, reduce the driving burden and overcome the conservatism of fixed parameters, an improved event-triggered control strategy is designed. Firstly, a new synchronisation error model with uncertain of internal coupling weights between nodes and stochastic network attacks on actuator is modelled. Secondly, the Bernoulli stochastic distribution process is used to describe the probability of stochastic attacks, and the actuator failure model is adopted to depict actuator failure. By establishing an appropriate Lyapunov function, some sufficient conditions to ensure the asymptotic stability of the synchronisation error system is derived, which reduces conservatism. In addition, the cooperative design of controller and adaptive event-triggered strategy is solved by solving linear matrix inequality. Eventually, two examples are given to illustrate the effectiveness and potential of the proposed design method.

Sliding-mode surface-based decentralized event-triggered control of partially unknown interconnected nonlinear systems via reinforcement learning

This paper studies a sliding-mode surface (SMS)-based decentralized event-triggered control problem for a class of partially unknown interconnected nonlinear systems within an identifier-critic neural networks framework. By assigning an especial cost function associated with SMS for each nominal subsystem, the original control issue is equivalently converted into designing a number of optimal controllers updating under an event-triggered manner, which considerably saves the communication resources. To derive these optimal event-based control policies, the corresponding Hamilton-Jacobi-Bellman equations are solved via the reinforcement learning algorithm. Different from traditional reinforcement learning algorithms, the identifier-critic network framework can obviate the requirement for the knowledge of system internal dynamics, and remove the errors generated by approximating actor networks. The weight vector in the critic network is updated through the gradient descent method and the experience replay technology, such that the persistence of excitation condition is relaxed. Under the proposed SMS-based decentralized control scheme, the considered system has a faster control response while minimizing the cost function. After that, according to the Lyapunov stability theory, all signals of the interconnected nonlinear systems are strictly proved to be bounded. Finally, the validity of the proposed control scheme is demonstrated via a simulation example.

Event-Triggered Optimal Control for Temperature Field of Roller Kiln Based on Adaptive Dynamic Programming.

Temperature field control is crucial for the comprehensive performance of Ni-Co-Mn layered cathode material that is the most important part of lithium-ion batteries. Starting from the aspect of a class of distributed parameter systems described by highly dissipative partial differential equations (PDEs), an event-triggered optimal control (ETOC) method based on adaptive dynamic programming (ADP) for the roller kiln temperature field is proposed. First, we formulate the event-triggered control problem of the temperature field under the general framework of PDE systems. Then, an event-triggered condition is designed based on the stability of the closed-loop PDE system, which also guarantees the upper bound of the performance index. Subsequently, ADP technology is adopted to realize the ETOC, where the critic network is employed to approximate the optimal value function. Since the studied system can be regarded as an impulsive dynamic system with flow dynamics and jump dynamics simultaneously, the stability of the impulsive dynamic system combined with the ADP-based closed-loop PDE system is proved. Finally, simulation results on the temperature field verify the effectiveness of the proposed method.

Delayed impulsive stabilisation of discrete-time systems: a periodic event-triggering algorithm

This paper studies the problem of event-triggered impulsive control for discrete-time systems. A novel periodic event-triggering scheme with two tunable parameters is presented to determine the moments of updating impulsive control signals which are called event times. Sufficient conditions are established to guarantee asymptotic stability of the resulting impulsive systems. It is worth mentioning that the event times are different from the impulse times, that is, the control signals are updated at each event time but the actuator performs the impulsive control tasks at a later time due to time delays. The effectiveness of our theoretical result with the proposed scheme is illustrated by three examples.

Event-triggered model-free adaptive predictive control for multi-area power systems under deception attacks

This paper focuses on the event-triggered model-free adaptive predictive load frequency control (LFC) problem for multi-area power systems under false data injection (FDI) attacks. First, the power system model is assumed to be unknown, and the nonlinear power system is converted into an equivalent linear data model using the input and output data of the powerline system. The FDI attacks on the power system is also modeled, and a Bernoulli stochastic process is used to represent whether the data is transmitted successfully or not. Second, the equivalent linear data model to predicts upcoming events, and a new event-triggered model-free adaptive predictive control scheme is designed via the predictions. And a RBF neural network disturbance estimator is designed to estimate and compensate for the effects caused by disturbances, and then power system stability is analyzed. An event-triggered scheme is also developed in the design of the LFC in order to save communication and computational burden. The results show that the designed control algorithm is data-driven independent of the power system structure and does not require the measurement of any system state signals. Finally, the effectiveness and correctness of the scheme is verified by a three-area numerical example and simulation results.

Dynamic-based event-triggered neural network control for p-normal interconnected time-delay systems with asymmetric constraints

A decentralized dynamic event-triggered tracking control problem is investigated for a class of p-normal interconnected time-delay nonlinear systems with asymmetric constraints. First, a new event-triggered mechanism (ETM) is constructed by designing a dynamic variable law, which effectively reduces the number of controller updates. The introduced constants provide an obvious solution for the proof of Zeno phenomenon. Second, a barrier Lyapunov function (BLF) is constructed with the help of sign functions to achieve time-varying asymmetric constraints. Meanwhile, we improve the previous neural networks (NNs) approximation scheme and dynamic gain technique in order to handle unknown interconnection functions and time-delay signals, respectively. Then, it is rigorously proved that all signals in the system are bounded, and the error signals are constrained within the time-varying asymmetric bounds. Finally, the feasibility of the scheme is verified by simulation examples.

Command filter-based adaptive fuzzy switching event-triggered control for non-affine nonlinear systems with actuator faults

This paper considers a problem of command filter-based adaptive fuzzy switching event-triggered control for non-affine nonlinear systems with actuator faults. First, a state observer is given to estimate unmeasurable state. Secondly, an error transformation equation is designed to constrain output tracking error, and the feasibility condition of tracking error is not required. Then, a finite time fault-tolerant control scheme is constructed based on command filter and backstepping technologies, which can compensate completely the effects of actuator faults and trigger errors, and there is no differential explosion, singularity phenomenon and Zeno behavior. Besides, a switching event-triggered algorithm is designed to reduce the triggering number under the premise of ensuring the tracking performance. Finally, the proposed algorithm is applied to an one-link manipulator to verify the availability.