This paper studies the leader-following consensus problem for a class of strict-feedback multiagent systems with unknown nonlinearities and state time-delays under directed topology. By using the backstepping technique, an adaptive consensus control protocol is proposed, where neural networks are employed to neutralize uncertain nonlinearities. To eliminate the effects of time-delays, Lyapunov-Krasovskii functionals, and Young's inequalities are used in the design process. It is notable that the computation burden is dramatically alleviated by proposing a novel adaptive mechanism. For communication topology containing a spanning tree, the proposed controller guarantees that the consensus tracking error will converge to an adjustable neighborhood of the origin. Finally, a numerical example is provided to validate the effectiveness of our result.

As the control tasks become complex, fulfilling such tasks cooperatively is the first choice in practice. In this article, the output consensus problem of heterogeneous multiagent systems is studied by deploying distributed observers in follower agents. Each observer in the follower only measures part of the leader’s output, which relieves the burden of a simple agent when the leader’s output is of large-scale dimensions. Then, all followers in the system work cooperatively to estimate the full state of the leader. By using parameterized Riccati equation and output regulation theory, sufficient conditions are given to design the distributed observers and the output consensus protocol. Finally, a numerical example is conducted to verify the obtained result.