Dual-Condition event-Triggered distributed adaptive neural control for multi-Agent systems with nonlinear fault

Distributed robust event-triggered adaptive control for a class of uncertain nonlinear multi-agent systems with actuator saturation

An adaptive neural network (NN) event-triggered control scheme is proposed for nonlinear nonstrict-feedback multiagent systems (MASs) against input saturation, unknown disturbance, and sensor faults. Mean-value theorem and Nussbaum-type function are invoked to transform the structure of the input saturation and overcome the difficulty of unknown control directions, respectively. On the basis of the universal approximation property of NNs, a nonlinear disturbance observer is designed to estimate the unknown compounded disturbance composed of external disturbance and the residual term of input saturation. According to the measurement error defined by control signal, an event-triggered mechanism is developed to save network transmission resource and reduce the number of controller update. Then, an adaptive NN compensation control approach is proposed to tackle the problem of sensor faults via the dynamic surface control (DSC) technique. It is proved that all signals in the closed-loop system are semi-globally uniformly ultimately bounded. Finally, simulation results demonstrate the effectiveness of the presented control strategy.

This paper investigates the event-triggered train formation control problem for multiple autonomous surface vehicles (ASVs) formation system in polar communication interference environment. Firstly, a distributed resilient guidance algorithm is introduced to generate the reference route based on waypoints. In the guidance algorithm, the distributed resilient leader predictor (RLP) is applied to obtain the states of ice-breaking ship when communication fails, and the resilient train formation scheme is designed to compute the reference signals for ASVs. Subsequently, an adaptive neural event-triggered train formation control algorithm is developed. In the control algorithm, the neural networks (NNs) are conducted to approximate model uncertainties, and event-triggered control (ETC) is employed to minimize controller updates. Furthermore, the threshold of the event-triggered mechanism (ETM) can be dynamically adjusted by states of system. It is proved that the formulated algorithm can ensure the prediction errors converge and multiple ASVs system is stable in polar communication interference environment. Finally, two simulation experiments are adopted to illustrate the effectiveness of the proposed algorithm.

This article investigates the adaptive fuzzy distributed optimal event-triggered (ET) control problem for high-order nonlinear multiagent (NMA) system under weight-unbalanced directed graph. An optimal signal generator with ET mechanism is first established to estimate optimization point and optimal network channel utilization between agents and neighbors. Second, since the ET mechanism is introduced in optimal signal generator, the first-order derivative of optimal virtual signals is not continuous at the trigger instant, a high-order filter is established to estimate high-order derivatives of optimal virtual signals. Based on the optimal signal generator with ET mechanism and high-order filter, an adaptive fuzzy distributed optimal ET control scheme is proposed by backstepping control design technology. It is shown that controlled NMA system is asymptotically stable and the global cost function is minimized. Finally, we apply the proposed distributed optimal ET control approach to marine surface vehicle systems, the simulation and comparison results verify its effectiveness.

Most event-triggered controllers available nowadays are based on static state-feedback controllers. As in many control applications the full state is not available for feedback, it is the objective of this paper to propose event-triggered dynamical output-based controllers. The fact that the controller is based on output feedback instead of state feedback does not allow for straightforward extensions of existing event-triggering mechanisms if a minimum time between two subsequent events, the so-called `minimum inter-event time', has to be guaranteed. Therefore, we will propose an event-triggering mechanism that invokes execution of the control task when the difference between the measured output or the control input of the plant or controller, respectively, and its previously sampled value becomes `large' compared to its current value and an additional threshold. For such event-triggering mechanisms, we will study closed-loop stability and L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> -performance and provide bounds on the minimum inter-event time. In addition, we will model the event-triggered control system using impulsive systems, which truly describe the behaviour of the event-triggered control system. As a result, we can guarantee stability and performance for improved event-triggered controllers with larger minimum inter-event times than existing results in literature.

This paper investigates the periodic event-triggered asymmetric bipartite consensus problem of multi-agent systems (MASs) under structurally balanced undirected signed graphs. In order to reduce the consumption of communication resources and continuous detection in the control process of MASs, a novel periodic detection event-triggered asymmetric bipartite consensus control protocol is proposed. In particular, each agent samples only in a fixed period. For each agent, two event conditions are given, and only when state error satisfies this event condition can the event be triggered. A new type of Laplacian matrix is constructed to achieve asymmetric bipartite consensus. The control method can guarantee the performance of MASs and have less communication resources. Based on the Lyapunov stability theorem and algebraic graph theory, the proposed periodic event-triggered control strategy is able to implement the asymmetric bipartite consensus when the topology is structurally balanced undirected signed graphs. The proposed method in this paper can reduce the restriction on the sampling period and automatically exclude Zeno behavior. Finally, the validity of the conclusion is verified by numerical simulation.

This paper investigates the event-triggered predictive control problem of networked control systems (NCSs) with the event-triggered mechanism adopted both in the forward and feedback channels to save the network resources. The transmition time instants of states are determined by an event-triggered mechanism, which contains a dynamic reference state, in the feedback channel. Combination of the event-triggered mechanism and the networked predictive control approach is employed to estimate states as well as future control input sequences of the system in the forward channel. The derived estimate state error system of the networked event-triggered predictive control system is proven to be uniformly globally asymptotic stable. Simulation results are provided to demonstrate the effectiveness and applicability of the proposed event-triggered predictive control scheme.

Event-triggered adaptive optimal tracking control for nonlinear stochastic systems with dynamic state constraints

Derivative-based event-triggered control for networked systems with quantization

Finite-time stabilization of linear systems by bounded event-triggered and self-triggered control

This paper investigates the problem of event-triggered networked predictive control for systems with data loss. An event-triggered networked predictive control system is proposed. Based on predictive control model, a data loss compensation strategy is presented and an extended event-triggered transmission mechanism is developed. The closed-loop event-triggered predictive control system is described as a switched system and sufficient closed-loop stability conditions related to event-triggered mechanism are established. Under the event-triggered networked predictive control scheme, the consumption of the communication resources is reduced. Finally, an example is provided to illustrate the effectiveness of the proposed method.

This article investigates the event-triggered impulsive control (ETIC) problem for a class of nonlinear time-delay systems subject to exogenous disturbances. An original event-triggered mechanism (ETM) which utilizes the information of system state and external input is constructed based on Lyapunov function approach. To achieve the input-to-state stability (ISS) of the considered system, some sufficient conditions are presented, in which the underlying relationship among ETM, exogenous input, and impulse action is established. Furthermore, the possible Zeno behavior induced by the proposed ETM is excluded simultaneously. As an application, the design criterion of ETM and impulse gain is put forward for a class of impulsive control systems with delay according to the feasibility of some linear matrix inequalities (LMIs). Finally, two examples with numerical simulations are provided to confirm the effectiveness of the developed theoretical results, where the synchronization issue of delayed Chua's circuit is considered.

This article investigates adaptive control problems for unknown second-order nonlinear multiagent systems (MASs) via an event-triggered approach. An adaptive event-triggered consensus controller is given to second-order MAS with unknown nonlinear dynamics. We prove that the proposed consensus controller is free from Zeno behavior. Next, an adaptive event-triggered tracking controller is developed for leader-follower MAS with the leader having bounded nonzero control input. Both consensus and tracking controllers are fully distributed, which means that event-triggered controllers only use local cooperative information. Finally, an unknown second-order nonlinear MAS is used to verify the given event-triggered controllers.

In this paper, we propose an event-triggered formation control algorithm for a multi-agent system consists of linear discrete agents in order to reduce energy consumption due to unnecessary calculation and communication of agents. By using the event-triggered protocol, which updates the control input aperiodically only when certain triggering condition is satisfied, calculation and communication frequency are reduced. Also the multi-agent system is guaranteed an almost sure stability by determining the control input update timing by the triggering condition which is based on Lyapunov's stability theorem. Finally we verify the effectiveness of the proposed algorithm by numerical simulation.

This paper addresses the problem of event-triggered control for infinite-dimensional systems. We employ event-triggering mechanisms that compare the plant state and the error of the control input induced by the event-triggered implementation. Under the assumption that feedback operators are compact, a strictly positive lower bound on the interevent times can be guaranteed. We show that if the threshold of the event-triggering mechanisms is sufficiently small, then the event-triggered control system with a bounded control operator and a compact feedback operator is exponentially stable. For infinite-dimensional systems with unbounded control operators, we employ two event-triggering mechanisms that are based on system decomposition and periodic event-triggering, respectively, and then analyze the exponential stability of the closed-loop system under each event-triggering mechanism.