“…By algebraic Riccati equationbased method and low-gain output-feedback mechanism, the observer-based protocol is designed to solve the issue of edge-consensus in [32]. e distributed observer-based tracking and containment control algorithms by using the event-triggered control method are, respectively, proposed for general linear systems with time delays in [33,34]. In [35], a novel distributed tracking control algorithm with input time delays is designed for leader-follower linear systems.…”
This paper considers the tracking and containment consensus for the general linear systems with input time delays under directed communication networks. The distributed observer-based algorithm on the basis of event-triggering mechanism will be designed by using only neighboring agents information. In this way, we can save network resource effectively. The event-based protocol with input time delays will be proposed for the leader-follower systems. Appropriate feedback gain matrices and trigger parameters can be designed by using Lyapunov stability theory. Based on the designed control algorithm, if the feedback gain matrices and the event trigger are designed appropriately, the leader-follower general linear system can eventually reach tracking and containment consensus. Then, two simulation results are provided to demonstrate the practicability of the theoretical analysis.
“…By algebraic Riccati equationbased method and low-gain output-feedback mechanism, the observer-based protocol is designed to solve the issue of edge-consensus in [32]. e distributed observer-based tracking and containment control algorithms by using the event-triggered control method are, respectively, proposed for general linear systems with time delays in [33,34]. In [35], a novel distributed tracking control algorithm with input time delays is designed for leader-follower linear systems.…”
This paper considers the tracking and containment consensus for the general linear systems with input time delays under directed communication networks. The distributed observer-based algorithm on the basis of event-triggering mechanism will be designed by using only neighboring agents information. In this way, we can save network resource effectively. The event-based protocol with input time delays will be proposed for the leader-follower systems. Appropriate feedback gain matrices and trigger parameters can be designed by using Lyapunov stability theory. Based on the designed control algorithm, if the feedback gain matrices and the event trigger are designed appropriately, the leader-follower general linear system can eventually reach tracking and containment consensus. Then, two simulation results are provided to demonstrate the practicability of the theoretical analysis.
“…Zhang et al studied the consensus problem of general linear multi-agent systems based on integral-type event-driven control strategy [30]. Observer-based event-triggered consensus protocols were designed according to the availability of output information in [31], [32].…”
Section: Introductionmentioning
confidence: 99%
“…Motivated by the aforementioned work, we further investigate the event-triggered consensus problem of linear multi-agent systems. Note that in [26], [29], [31], and [32], continuous information is still needed in event checking. In order to reduce the communication frequency between agents, we propose a new event-triggered control scheme, which does not use continuous communication information in the event-triggering condition.…”
For the consensus problem of multi-agent systems with linear dynamics, distributed eventtriggered control strategies are put forward, which can decrease the frequency of information transmissions between agents and the number of control inputs of each agent. In the case that the agent states can be obtained or outputs can be obtained, distributed consensus protocols are designed based on event-triggered state information and event-triggered observer information, respectively. The consensus problem is converted into the stability problem by model transformation. Sufficient conditions for multi-agent systems to achieve consensus are obtained. Meanwhile, it is theoretically proved that the event-triggering conditions will exclude Zeno behavior. Simulation examples verify the effectiveness of obtained results.INDEX TERMS Consensus, multi-agent systems, event-triggering, distributed control, linear dynamics.
“…30 In the works of Eqtami et al 26 and Sahoo et al, 31 event-triggered control methods for discrete-time systems were given. Analysis of event-triggered control methods for linear systems was presented in the work of Heemels et al 32 In 2012, an event-triggered control algorithm was studied for multiagent systems in the work of Dimarogonas et al 33 Besides, the event-triggered scheme was used for tracking control systems in the works of Tallapragada and Chopra 34 and Liu et al 35 With extensive research on ADP, many event-triggered ADP algorithms have been generated. Zhong and He 36 proposed an event-triggered control method based on ADP algorithms with an observer, which only used input and output data.…”
Section: Introductionmentioning
confidence: 99%
“…In the works of Eqtami et al and Sahoo et al, event‐triggered control methods for discrete‐time systems were given. Analysis of event‐triggered control methods for linear systems was presented in the work of Heemels et al In 2012, an event‐triggered control algorithm was studied for multiagent systems in the work of Dimarogonas et al Besides, the event‐triggered scheme was used for tracking control systems in the works of Tallapragada and Chopra and Liu et al…”
Summary
In this paper, an event‐triggered heuristic dynamic programming algorithm for discrete‐time nonlinear systems with a novel triggering condition is studied. Different from traditional heuristic dynamic programming algorithms, the control law in this algorithm will only be updated when the triggering condition is satisfied to reduce the computational burden. Three neural networks are employed, which are model network, action network, and critic network. Model functions, control laws, and value functions are estimated using neural networks, respectively. The main contribution of this algorithm is the novel triggering condition with simpler form and fewer assumptions. Additionally, a proof of the stability for discrete‐time systems using Lyapunov technique is given. Finally, two simulations are shown to verify the effectiveness of the developed algorithm.
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