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This article solves the leaderless consensus problem of a class of uncertain nonlinear multiagent systems with unknown control directions and unknown system parameters. Without using the Nussbaum function approach, a novel control scheme is proposed by means of the switching mechanism. The control algorithm guarantees that consensus errors converge to the origin asymptotically, and the amplitude of the control signals is much smaller compared with those using Nussbaum functions. The simulation results illustrate the effectiveness of the proposed algorithm. K E Y W O R D S adaptive control, directed graph, multiagent system, nonlinear systems, unknown control directions 1 INTRODUCTION The consensus control of multiagent system has attracted widespread attentions and many results have been obtained due to the broad applications in many areas, such as cooperative control of unmanned air vehicles, formation and flocking control of mobile robots, distributed sensor networks, attitude alignment for a clusters of satellites, congestion control in communication networks, and so on. For example, in Reference 1, an adaptive neural network control scheme is proposed to solve the leaderless consensus of multiagent systems. In Reference 2, an adaptive control scheme is proposed for multiagent systems with second-order integrator dynamics under directed graphs. In References 3 and 4, distributed adaptive control schemes are developed for multiagent systems with more generalized nonlinearities. In Reference 5, the distributed adaptive leader-following consensus control for high-order nonlinear multiagent systems with a time varying reference under directed topology subjected to mismatched unknown parameters is considered. In References 6 and 7, leader-following distributed consensus control schemes are proposed, in which the NN-based method is applied and global information of Laplacian matrix is not required. In Reference 8, a fully distributed leader-following consensus

Most autonomous systems can be described by Euler-Lagrange (EL) model. This article investigates the problem of prescribed time tracking control for EL systems in the presence of modeling uncertainties, prescribed performance requirements and time-varying state constraints. Two proportional-integral (PI)-like control schemes with time-varying gains are developed with several favorable features: (1) achieving prescribed tracking precision within finite time in the presence of modeling uncertainties; (2) state constraints being obeyed all the time; (3) both the final tracking accuracy and the setting time being preassigned irrespective of initial condition or any other constraining parameter; and (4) bearing simple PI structure with analytical formula for robust-adaptive tuning gains, and demanding inexpensive online computation. The benefits and effectiveness of the proposed control are also validated via numerical simulation.

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