Event-triggered Control Framework Research Articles

Relaxed Stabilization of Event-Triggered Interval Type-2 T–S Fuzzy Positive Systems With Stochastic Actuator Failure

This paper studies the membership-function-dependent (MFD) stability and positivity analysis of positive Interval Type-2 (IT2) Takagi-Sugeno (T-S) fuzzy fault-tolerant control systems under the event-triggered control framework. Considering the fact that parameter uncertainties may exist in nonlinear plants, the IT2 T-S fuzzy model as an effective mathematical tool is employed to approximately express a complex positive system with nonlinearities and uncertainties. In order to save communication resources and improve the security of system operation, an event-triggered fault-tolerant control strategy is then designed based on the Imperfectly Premise Matching (IPM) concept. In addition, for capturing positivity characteristic and matching with the linear co-positive Lyapunov function, a novel event-triggered condition in the 1-norm rather than in the 2-norm is developed to control the positive IT2 T-S fuzzy systems. Thereinto, the non-convex problem as the first obstacle is required to be cleared since feasible solutions usually cannot be obtained based on the non-convex conditions. For dealing with this problem, a valid approach is presented to conduct convexification with the help of a user-chosen constant vector. Besides, because of event-triggered action, the inherent mismatched issue of the premise variables of the fuzzy model and the ones of the fuzzy controller is encountered. Fortunately, the advanced MFD analysis, which is capable of bringing the information contained in the membership functions (MFs) into the stability and positivity conditions, provides an effective strategy to remove this barrier. Consequently, relaxed results which can accomplish the stability and positivity for wider positive systems are developed and displayed in sum-of-squares (SOS) form. Detailed simulation results are given to clarify the effectiveness of the designed event-triggered fault-tolerant control method.

Event-triggered fully distributed asymptotic consensus for leaderless multiple Euler-Lagrange systems

In this paper, the fully distributed consensus for a class of multiple Euler-Lagrange systems is investigated, where the protocol is designed under the event-triggered control framework and the dynamics of Euler-Lagrange systems are heterogeneous. Since only local information interactions at triggered instants can be used and the Euler-Lagrange systems are of relatively complex dynamics, it is challenging to achieve asymptotic consensus without using any global information (such as the Laplacian matrix information). By skillfully integrating the adaptive control, distributed control and event-triggered control techniques, a novel protocol is proposed for the investigated multiple Euler-Lagrange systems. It is proven that the asymptotic consensus can be achieved by the developed protocol. By a numerical example, the effectiveness of the developed protocol is illustrated.

Event-triggered control of leader-following and cluster consensus for MAS under directed graph with designable minimum event interval

This paper investigates the event-based consensus problems for linear multi-agent systems under directed network topology. First, a new event-triggered control method is proposed for the leader-following consensus problem of agents under directed graphs. Then this new method is applied to the cluster control problem under special topological conditions. The new event-based control scheme is better than some existing literature in the following aspects. 1) The graph only needs to contain a spanning tree instead of being required to be strongly connected graph or undirected, and the triggering function is state-dependent rather than time-dependent. 2) Some parameters are designable for the trade-off between the event interval and the performance of the controlled system. Besides, the optimization of some parameters is studied to reduce the trigger frequency. All the agents can achieve consensus with an exponential speed when communications among follower agents are intermittent, and Zeno behavior is excluded under the proposed method. 3) When applying this method to the cluster control problem, agents in the same cluster share the same form of triggering function. Cluster consensus can be achieved regardless of intra- and inter-cluster relative coupling strength under the event-triggered control framework.

Event-Triggered Control for Markov Jump Systems Subject to Mismatched Modes and Strict Dissipativity.

In order to save network resources of discrete-time Markov jump systems, an event-triggered control framework is employed in this article. The threshold parameter in the event-triggered mechanism is designed as a diagonal matrix in which all elements can be adjusted according to system performance requirements. The hidden Markov model is introduced to characterize the asynchronization between the controller and controlled system. The effect of randomly occurring gain fluctuations is taken into account during the controller design. For the purpose of guaranteeing that the closed-loop system is stochastically stable and satisfies the strictly (D₁,D₂,D₃)-ɣ-dissipative performance, sufficient conditions are constructed by employing the Lyapunov function and stochastic analysis. After linearization, the proposed controller gains are obtained by solving the linear matrix inequalities. Ultimately, a practical example of the dc motor device is used to illustrate the effectiveness of the proposed new design technique.

Distributed adaptive dynamic event-based consensus control for nonlinear uncertain multi-agent systems

In order to tackle the real network constraints such as unknown state time-varying delays and limited communication resources, this article proposes a dynamic event-triggered control framework to satisfy the distributed consensus of heterogeneous strict-feedback multi-agent systems. Each follower agent is subject to nonlinear-in-parameter uncertainties regardless of satisfying the Lipschitz condition. Having considered the follower’s uncertainties, the adaptive neural network strategy is used to approximate uncertain functions. To further simplify the controller form, the minimal learning parameter technique is introduced to evade the updated information of the neural network weight vectors. Graph-based Lyapunov–Krasovskii functionals are utilized to handle the unknown state time-delays. A command filter dynamic event-triggered control scheme is then introduced to remove the complexity explosion regardless of global information of network. Different from the existing triggering strategies for the strict-feedback multi-agent systems, the proposed triggering mechanism is proved to include a positive bounded dynamical variable which results in the exclusion of the Zeno phenomenon and reducing the communication load in the controller to actuator channels. That can lead to the save of energy and communication resources, which are meaningful for employing the proposed scheme in the real practical applications. It is eventually proved that under the proposed approach, all the closed-loop network errors converge to a small neighborhood of the origin. A simulation example is finally given to show the effectiveness of the control scheme.

Event-triggered formation control of n-link networked stochastic robotic manipulators

This article proposes an event-triggered control framework to satisfy the tracking formation performance for a group of uncertain non-linear n-link robotic manipulators. The robotic manipulators are configured as a multi-agent system and they communicate over a directed graph (digraph). Furthermore, the non-linear robotic manipulator-multi-agent systems are subject to stochastic environmental loads. By introducing extra virtual controllers in the final step of the backstepping design, a total number of n event-triggering mechanisms are introduced independently for each link of all the robotic manipulator agents to update the control inputs in a fully distributed manner. More precisely, the actuator of each link of a particular agent is capable of being updated independent of other link actuator updates. A rigorous proof of the convergence of all the closed-loop signals in probability is then given and the Zeno phenomenon is excluded for the control event-triggered architectures. The simulation experiments finally quantify the effectiveness of proposed approach in terms of reducing the number of control updates and handling the stochastic environmental loads.

Quantization level based event-triggered control with measurement uncertainties

In this paper, the quantization level based event-triggered control algorithm is novelly proposed and stability of the considered system could be guaranteed on account of hysteresis quantizer under measurement uncertainties. By carefully exploring the characteristics of nonlinear networked control systems, hysteresis quantizer is selected to avoid potential chatterings caused by frequent quantization level transitions. Two kinds of situations, one is networked control system without considering measurement uncertainties, and the other takes measurement uncertainties into consideration, are separately discussed under the quantization level based event-triggered control framework. By thoroughly digging out the relationship between quantizer structure and event-triggered control mechanism, two quantization level based event-triggered control mechanisms are put forward under different situations. Moreover, no matter whether measurement uncertainties are taken into account, system stability could always be guaranteed by our proposed criteria and Zeno behavior would never happen with our method. Finally, simulations show the feasibility and superiority of our proposed algorithms.

Event-triggered resilient control for cyber-physical systems under periodic DoS jamming attacks

This paper considers the event-triggered resilient control problem for cyber-physical systems against periodic denial-of-service (DoS) attacks. Firstly, a novel event-triggered scheme without Zeno behaviors is presented to save network resources and eliminate the occurrence of invalid events during periodic DoS attacks. Subsequently, by constructing a predictor-based event-triggered control framework, the upper bound of the prediction error related to periodic DoS attack parameters is given. Furthermore, sufficient conditions related to periodic DoS attack parameters are established to ensure the input-to-state stability. It is shown that the proposed design method can achieve better system performances than the existing ones when the system suffers the same degree of periodic DoS attacks. Finally, the theoretical research results are validated through a batch reactor system model.

Robust efficient cruise control for high-speed train movement based on the self-triggered mechanism

This paper investigates the robust efficient self-triggered cruise control problem for high-speed trains to reduce the consumptions of the computational resources and wireless communication bandwidth. By capturing the dynamic evolution of high-speed trains in the actual environment, an error dynamic state-space model is established considering uncertain parameters, input constraints and disturbances to the train motion. According to the event-triggered control framework, a co-design method is introduced to design the cruise controller and the event-triggered condition jointly. Based on the Lyapunov stability method, a sufficient condition for the existence of the robust state-feedback control law and the event-triggered condition parameter are presented in terms of linear matrix inequalities (LMIs), which ensures that the high-speed train tracks the desired speed, and the coupler deviations are stable at the equilibrium point under external disturbances. With the fact that the event-triggered controller often requires a dedicated detector to continuously monitor the system state, a self-triggered implementation is further developed to dynamically calculate the next triggered instant at each update time based on recently collected information. In the end, numerical experiments are provided to illustrate the effectiveness of the proposed approach in tracking the desired movement profiles, and thereby demonstrates the advantages of the proposed self-triggered controller over the traditional periodic triggered control method.

Formula omitted] control of uncertain linear systems with a triggering threshold dependent approach

This paper investigates the H∞ control of uncertain linear systems under the event-triggered control framework. The uncertainty is a mismatched type without known upper bounds. To reduce the redundant transmission of control signals, three triggering strategies are developed, namely, the absolute threshold triggering manner is built firstly to deal with the small control input variation. By revisiting the first one, the large control input is monitored by the second triggering scheme. Lastly, a trade-off between the above two triggering measures is made to cover all possible inputs without sacrificing system performance largely. Based on the proposed triggering strategies, different adaptive controllers containing the triggering threshold bound are constructed to eliminate the unknown uncertainty and ensure uniformly bounded with the required H∞ performance level, which is verified by simulation studies.

Membership-Function-Dependent Stabilization of Event-Triggered Interval Type-2 Polynomial Fuzzy-Model-Based Networked Control Systems

In this article, the stability analysis and control synthesis of interval type-2 (IT2) polynomial-fuzzy-model-based networked control systems are investigated under the event-triggered control framework. The nonlinear dynamics in the plant is efficiently represented by an IT2 polynomial fuzzy model that the IT2 membership functions are utilized to capture the uncertainties in the plant. An event-triggered IT2 polynomial fuzzy controller is then designed to stabilize the nonlinear model subject to uncertainties. The stability conditions of the closed-loop control system are summarized in the form of sum-of-squares. Under the imperfectly premise matching (IPM) concept, the membership-function-dependent (MFD) approach is applied to endow the polynomial fuzzy controllers with more flexibility in terms of number of rules and premise membership functions. In the MFD approach under the IPM concept, both the number of rules and the shape of membership functions in the fuzzy models and controllers can be different. Also, the information of IT2 membership functions of the polynomial fuzzy model and controller is considered and adopted to further relax the stability conditions. Furthermore, the intrinsic mismatched issue of the premise variables of the fuzzy model and controllers due to the event-triggering mechanism is handled by the MFD approach. A detailed simulation example is provided to verify the effectiveness of the proposed event-based control strategy.

Learning-Based Event-Triggered Control for Synchronization of Passive Multiagent Systems Under Attack

In this article, we study the synchronization of a group of output passive agents that communicate with each other according to an underlying communication graph. A distributed event-triggered control framework that guarantees synchronization and reduces the required communication rate is introduced. A general Byzantine attack on a multiagent system is defined and its negative effects on synchronization are characterized. The Byzantine agents are able to intelligently falsify their data and manipulate the underlying communication graph by altering their control feedback weights. Next, a decentralized decision-making and detection framework is introduced and its steady-state and transient performances are analyzed. Furthermore, a method of identifying Byzantine neighbors and a learning-based procedure for estimating the attack parameters are introduced. Finally, learning-based control frameworks to mitigate the effects of the attack are proposed.

Integral reinforcement learning-based online adaptive event-triggered control for non-zero-sum games of partially unknown nonlinear systems

This paper develops an integral reinforcement learning (IRL)-based adaptive control method for the multi-player non-zero-sum (NZS) games of the nonlinear continuous-time systems with partially unknown dynamics, in the context of event-triggered mechanism. With the principle of IRL method, the requirement for the system drift dynamics is relaxed in the controller design. Moreover, different from the conventional iteration computation methods, the algorithm developed in this work is implemented in an online adaptive fashion, which provides a new way to combine the IRL algorithm and the event-triggered control framework in solving the NZS game issues. In the event-based algorithm, a state-dependent triggering condition is presented, which not only guarantees the closed-loop system stability, but also reduces the computation and communication loads of the controlled plant. By means of Lyapunov theorem, the uniform ultimate boundedness (UUB) properties of the system states and the critic weight estimation errors have been proved. Finally, two numerical examples are utilized to demonstrate the efficacy of the proposed method.

Dual-stage periodic event-triggered output-feedback control for linear systems

This paper proposes an event-triggered control framework, called dual-stage periodic event-triggered control (DSPETC), which unifies periodic event-triggered control (PETC) and switching event-triggered control (SETC). Specifically, two period parameters h1 and h2 are introduced to characterize the new event-triggering rule, where h1 denotes the sampling period, while h2 denotes the monitoring period. By choosing some specified values of h2, the proposed control scheme can reduce to PETC or SETC scheme. In the DSPETC framework, the controlled system is represented as a switched system model and its stability is analyzed via a switching-time-dependent Lyapunov functional. Both the cases with/without network-induced delays are investigated. Simulation and experimental results show that the DSPETC scheme is superior to the PETC scheme and the SETC scheme.

A multi-class decentralised event-triggered control framework for congestion and emission reduction in freeway networks

The present work deals with the problem of regulating traffic in freeway networks via ramp metering with the twofold objective of reducing congestion and traffic emissions. In this paper, an answer to this problem is given by proposing a multi-class decentralised control scheme that acts according to an event-triggered logic. Local control algorithms receive local measurements from the freeway network and make a periodic prediction on the local system evolution. On the basis of this information, the local control algorithms communicate suitable parameters to the local feedback controllers, which produce control actions able to jointly reduce the congestion and the traffic emissions in a neighbourhood of the controlled on-ramp. Since these local controllers are of the multi-class type, the resulting ramp metering control actions are distinguished for different classes of vehicles. The results obtained by applying such a decentralised scheme are compared in the paper with those achieved by using a centralised control framework.

Adaptive Control for Linear Uncertain Discrete-Time Systems with Event-Triggered Mechanisms

An adaptive event-triggered control framework for uncertain linear time-invariant discrete-time systems is developed. The approach is Lyapunov-based and the event-triggered mechanism checks if certain condition is satisfied at every time instant in order to reduce the communication rate between the controller and the actuator. Finally, we present two numerical examples to show efficacy of the proposed approach.