In this paper, the distributed control problem of nonlinear strict-feedback multiagent systems is addressed under directed and time-invariant communication graphs. With the utilization of the prescribed performance control methodology, a control algorithm is proposed to ensure predefined bounds of overshoot, convergence rates, and steady-state values of the neighborhood synchronization errors in the presence of unknown dead-zone inputs. The algorithm is fully distributed in the sense that the control input for each agent is independent of any global information of the communication graph and is solely based on local relative output information from its neighborhood set. The approximating structures, e.g., neural networks or fuzzy systems, and the command filters that are typically incorporated to avoid the need for analytical derivatives in the backstepping design are not employed here, resulting in a low-complexity design. Simulation results are included to verify the algorithm.

This brief addresses the distributed consensus problem of nonlinear multi-agent systems under a general directed communication topology. Each agent is governed by higher-order dynamics with mismatched uncertainties, multiple completely unknown high-frequency gains, and external disturbances. The main contribution of this brief is to present a new distributed consensus algorithm, enabling the control input of each agent to require minimal information from its neighboring agents, that is, only their output information. To this end, a dynamic system is explicitly constructed for each agent to generate a reference output. Theoretical and simulation verifications of the proposed algorithm are rigorously studied to ensure that asymptotic consensus can be achieved and that all closed-loop signals remain bounded.

This paper presents new distributed consensus algorithms for higher-order multi-agent systems with mismatched uncertainties on general directed graphs. The developed algorithm enjoys the intriguing attribute of accommodating nonuniform communication delays and relies on minimal information from the neighboring agents, namely, their output states. Specifically, by exploiting the interplay of tools from the Laplace transformation and algebraic graph theory, we demonstrate that with the developed distributed algorithm, all agents reach consensus, while the boundedness of all closed-loop signals remains unchanged. In addition, we apply the developed algorithm to tackle the rendezvous control problem for networked nonholonomic mobile robots with unknown dynamic parameters. We also show that by properly tuning the design parameters, the rendezvous errors of the entire system can be made as small as desired. Comparative simulations are performed to illustrate and validate the algorithms.

This paper addresses the leader-following consensus problem of networked Lagrangian systems with unknown control directions and uncertain dynamics. For undirected graphs and directed graphs, two types of distributed control protocols are proposed without assuming that the leader's position information is linearly parameterised. It is proven that all signals in the closed-loop system are bounded, and a leader-following consensus can be achieved with the proposed corresponding protocols. These protocols are distributed in the sense that the control input for each Lagrangian system is solely based on local relative position and velocity information from its neighbourhood set and does not require additional information, e.g. acceleration or observer information of its neighbours, thus avoiding the dead-loop problem and reducing the communication burden. Simulations on networked two-link revolute joint arms are given to validate the theoretical findings.