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Lyapunov function is proposed to study the convergence of the consensus algo rithms. In [6] and [20], necessary and sufficient conditions for an appropriate decentralized linear stabilizing feedback are established. In [16], the problem of flocking with obstacles is addressed, where flocking is defined as achieving both structural and navigational stability. The sys tems considered are restricted to have integrator dynamics, and stability results are not presented. A feedback control strategy that achieves con vergence of a MAS, for single-integrator dynamics, with a desired formation and avoiding collisions is proposed in [1]. A connection between formation infeasibility and a sort of fiocking is established. Consensus of multiple autonomous vehicles is ad dressed in [7], by using virtual leaders and artificial potential fields among neighboring vehicles. Re sults are also restricted to vehicles with integra tor dynamics. A decentralized dynamic controller dealing with the problem of cooperation among a collection of vehicles is presented in [3] and [9]. The problems of consensus (synchronization), model-reference, and regulation for a network of identical multi-input, multi-output linear MAS are considered in [22]. That work proposes a dis tributed protocol to solve such problems for net work Laplacian topologies and asymmetric topolo gies. In [2], consensus output regulation of network connected MAS is addressed. Every agent is rep resented by a nonlinear system, and has identical Abstra£t. Th‫؛‬s paper presents the des‫؛‬gn of a distributed control ‫؛‬aw for the output regulation and output consen sus of a set of N agents. In this approach, each agent dynamics is represented by a switched linear system. The representation of the agents is neither constrained to be the same nor to have the same state dimension, and communication among agents is considered to be switching. It is also considered that some agents get the reference to be followed from the output of a virtual agent, and every agent gets the output information of its neighbors. Using this information, every agent computes the exosystem state to solve its individual regulation problem. The approach herein proposed employs a local switched stabilizing feedback for each agent based on a common Lyapunov function. A numerical example is provided in order to illustrate the proposed control law.