Fully distributed optimization of second‐order systems with disturbances based on event‐triggered control

Abstract

This paper studies the distributed optimization problem of second‐order multiagent systems containing external disturbances. To reject the external disturbances and lead agents' states to converge to the optimal consensus point, an adaptive event‐triggered controller is proposed based on the internal model principle. With the adaptive mechanism, both the controller and the event‐triggering condition do not contain the parameters related to global information, such as the maximum Lipschitz constant and the minimum strongly convex constant of local cost functions, and hence the event‐triggered controller is fully distributed. By utilizing the event‐triggered scheme, the consumption of communication among neighbors and the computing resources are saved. Furthermore, with the Lyapunov analysis framework, the optimal consensus can be proved to achieve and Zeno behavior is excluded from the event‐triggering condition. Finally, the effectiveness of the proposed protocol is verified by numerical simulations.

Resilient algorithm for distributed resource allocation under false data injection attacks

“…Suppose that Assumptions 1-3 hold. The resilient distributed resource allocation algorithm (14) with linear ESO ( 13) can converge to a small neighborhood of optimal allocation X * of problem (1). That is,…”

“…Generally speaking, the computation and the communication are two important components in the distributed optimization algorithms for solving the resource allocation problem. In recent years, the results of asymptotic and exponential convergence of distributed resource allocation algorithms have been obtained for agents communicating on undirected graphs [9][10][11][12][13][14][15]. And the related results were also extended to weight-(un)balanced directed graphs (digraphs) and switching networks [16][17][18][19][20].…”

“…To reduce the communication load, distributed event-triggered algorithms were proposed in the work of Deng and Wang [28] and Li et al [29]. In addition, Deng et al studied the distributed resource allocation problem with the double-integrator and the multi-integrator agents and designed distributed algorithms based on the state feedback [14,24,28,30].…”

Output feedback distributed optimization algorithms of second‐order Lipschitz nonlinear multi‐agent systems

“…In many practical cooperative tasks, nevertheless, the distributed optimization assignments are implemented on continuous-time multi-agent systems, for instance, multi-mobile robots and multi-manipulators. Therefore, continuous-time distributed optimization problem has been considered and a lot of distributed optimization algorithms have been proposed in continuous-time domain for first-order, second-order, and higher order dynamic multi-agent systems [3,[18][19][20][21][22][23][24][25][26][27][28][29][30][31][32].…”

Event‐triggered sampled‐data synchronization of complex networks with uncertain inner coupling and time‐varying delays

“…In the process of data transmission, because there is a large amount of transmission data in the complex network, some of which do not need to be transmitted, an increasing number of scholars have studied event-triggered sampled-data (ETSD) control [28][29][30][31][32][33][34]. The ETSD control mechanism can only transmit the sampled signal to the controller if the predefined conditions are satisfied.…”