Output Consensus of Heterogeneous Linear Multi-Agent Systems by Distributed Event-Triggered/Self-Triggered Strategy

Self-Triggered Leader-Following Consensus for High-Order Nonlinear Multiagent Systems via Dynamic Output Feedback Control

This paper investigates the event-based leader-following consensus problem for high-order nonlinear multiagent systems whose dynamics are in strict feedback forms and satisfy Lipschitz condition. By using self-triggered control scheme and dynamic output feedback control method in combination, a new class of distributed self-triggered consensus protocols is proposed based only on the relative output measurements of neighboring agents. It is noted that the proposed protocols only require the output information of neighboring agents to be shared and the designed self-triggered algorithm can avoid continuous communication among neighboring agents, thus the communication cost is reduced significantly. Sufficient conditions in terms of matrix inequalities are derived to guarantee the exponential leader-following consensus. The effectiveness of the theoretical results is illustrated through a simulation example.

Quantized Consensus of Multi-Agent Networks With Sampled Data and Markovian Interaction Links

This paper investigates the joint effect of quantization, sampled data, and general Markovian interaction links on consensus networks with a leader under directed graphs. The diversity of edges formed by all the followers and the leader is also considered. Each agent in the network possesses continuous-time general linear dynamics. Each agent's state is measured only at sampling time instants, which is encoded before transmission. Subsequently, the encoded state is transmitted through noiseless digital communication links with Markovian switching rates. For this problem, a sufficient condition is derived to guarantee the convergence of the encoded states, based on which a necessary and sufficient condition is obtained to achieve consensus tracking in the mean-square sense. In addition, two sufficient conditions on coupling gain, one of which is fully distributed, are provided by proposing an optimal linear quadratic regulator-based gain matrix to ensure consensus tracking and then, the analysis of consensus region is presented. Finally, a numerical example is presented for illustrating the effectiveness of the theoretical results.

Input Time Delay Margin in Event-Triggered Consensus of Multiagent Systems

In this paper, the event-triggered consensus problem of multiagent systems with input time delay is investigated. First, the normal event-triggered control scheme containing the input time delay is introduced to reduce the number of communication. Then the following results are achieved: 1) the procedure of setting parameters is carefully formulated for the event-triggered control scheme; 2) the precise input time delay margin is calculated for the event-triggered consensus of the multiagent systems; 3) a more general condition of constructing event-triggered functions is derived to exclude the Zeno behavior; 4) the self-triggered control scheme is further applied to avoid the continuous measurement; and 5) the observer-based control scheme is also utilized to tackle the problem of unmeasurable state. Finally, the correctness and the effectiveness of these results are demonstrated by numerical simulations.

Opinion Dynamics in the Presence of Increasing Agreement Pressure

In this paper, we study a model of agent consensus in a social network in the presence increasing interagent influence, i.e., increasing peer pressure. Each agent in the social network has a distinct social stress function given by a weighted sum of internal and external behavioral pressures. We assume a weighted average update rule consistent with the classic DeGroot model and prove conditions under which a connected group of agents converge to a fixed opinion distribution, and under which conditions the group reaches consensus. We show that the update rule converges to gradient descent and explain its transient and asymptotic convergence properties. Through simulation, we study the rate of convergence on a scale-free network.

Aperiodic Robust Model Predictive Control for Constrained Continuous-Time Nonlinear Systems: An Event-Triggered Approach

The event-triggered control is a promising solution to cyber-physical systems, such as networked control systems, multiagent systems, and large-scale intelligent systems. In this paper, we propose an event-triggered model predictive control (MPC) scheme for constrained continuous-time nonlinear systems with bounded disturbances. First, a time-varying tightened state constraint is computed to achieve robust constraint satisfaction, and an event-triggered scheduling strategy is designed in the framework of dual-mode MPC. Second, the sufficient conditions for ensuring feasibility and closed-loop robust stability are developed, respectively. We show that robust stability can be ensured and communication load can be reduced with the proposed MPC algorithm. Finally, numerical simulations and comparison studies are performed to verify the theoretical results.

Distributed Consensus Optimization in Multiagent Networks With Time-Varying Directed Topologies and Quantized Communication

This paper considers solving a class of optimization problems which are modeled as the sum of all agents' convex cost functions and each agent is only accessible to its individual function. Communication between agents in multiagent networks is assumed to be limited: each agent can only interact information with its neighbors by using time-varying communication channels with limited capacities. A technique which overcomes the limitation is to implement a quantization process to the interacted information. The quantized information is first encoded as a binary sequence at the side of each agent before sending. After the binary sequence is received by the neighboring agent, corresponding decoding scheme is utilized to resume the original information with a certain degree of error which is caused by the quantization process. With the availability of each agent's encoding states (associated with its out-channels) and decoding states (associated with its in-channels), we devise a set of distributed optimization algorithms that generate two iterative sequences, one of which converges to the optimal solution and the other of which reaches to the optimal value. We prove that if the parameters satisfy some mild conditions, the quantization errors are bounded and the consensus optimization can be achieved. How to minimize the number of quantization level of each connected communication channel in fixed networks is also explored thoroughly. It is found that, by properly choosing system parameters, one bit information exchange suffices to ensure consensus optimization. Finally, we present two numerical simulation experiments to illustrate the efficacy of the algorithms as well as to validate the theoretical findings.