No Arabic abstract
This paper deals with the H2 suboptimal output synchronization problem for heterogeneous linear multi-agent systems. Given a multi-agent system with possibly distinct agents and an associated H2 cost functional, the aim is to design output feedback based protocols that guarantee the associated cost to be smaller than a given upper bound while the controlled network achieves output synchronization. A design method is provided to compute such protocols. For each agent, the computation of its two local control gains involves two Riccati inequalities, each of dimension equal to the state space dimension of the agent. A simulation example is provided to illustrate the performance of the proposed protocols.
This paper deals with data-driven output synchronization for heterogeneous leader-follower linear multi-agent systems. Given a multi-agent system that consists of one autonomous leader and a number of heterogeneous followers with external disturbances, we provide necessary and sufficient data-based conditions for output synchronization. We also provide a design method for obtaining such output synchronizing protocols directly from data. The results are then extended to the special case that the followers are disturbance-free. Finally, a simulation example is provided to illustrate our results.
In this paper, we consider scalable output and regulated output synchronization problems for heterogeneous networks of right-invertible linear agents based on localized information exchange where in the case of regulated output synchronization, the reference trajectory is generated by a so-called exosystem. We assume that all the agents are introspective, meaning that they have access to their own local measurements. We propose a scale-free linear protocol for each agent to achieve output and regulated output synchronizations. These protocols are designed solely based on agent models and they need no information about communication graph and the number of agents or other agent models information.
This paper investigates the H2 and H-infinity suboptimal distributed filtering problems for continuous time linear systems. Consider a linear system monitored by a number of filters, where each of the filters receives only part of the measured output of the system. Each filter can communicate with the other filters according to an a priori given strongly connected weighted directed graph. The aim is to design filter gains that guarantee the H2 or H-infinity norm of the transfer matrix from the disturbance input to the output estimation error to be smaller than an a priori given upper bound, while all local filters reconstruct the full system state asymptotically. We provide a centralized design method for obtaining such H2 and H-infinity suboptimal distributed filters. The proposed design method is illustrated by a simulation example.
This paper studies scale-free protocol design for H_infty almost output and regulated output synchronization of heterogeneous multi-agent systems with linear, right-invertible, and introspective agents in presence of external disturbances. The collaborative linear protocol designs are based on localized information exchange over the same communication network, which do not require any knowledge of the directed network topology and spectrum of the associated Laplacian matrix. Moreover, the proposed scale-free protocols achieve H_infty almost synchronization with a given arbitrary degree of accuracy for any size of the network.
In this paper, we investigate a constrained optimal coordination problem for a class of heterogeneous nonlinear multi-agent systems described by high-order dynamics subject to both unknown nonlinearities and external disturbances. Each agent has a private objective function and a constraint about its output. A neural network-based distributed controller is developed for each agent such that all agent outputs can reach the constrained minimal point of the aggregate objective function with bounded residual errors. Two examples are finally given to demonstrate the effectiveness of the algorithm.