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Distributed adaptive leaderāfollower and leaderless consensus control of a class of strict-feedback nonlinear systems : a unified approach
Author's accepted manuscript.Available from 16/05/2022.acceptedVersio
Distributed Control of Multi-agent Systems with Unknown Time-varying Gains: A Novel Indirect Framework for Prescribed Performance
In this paper, a new yet indirect performance guaranteed framework is
established to address the distributed tracking control problem for networked
uncertain nonlinear strict-feedback systems with unknown time-varying gains
under a directed interaction topology. The proposed framework involves two
steps: In the first one, a fully distributed robust filter is constructed to
estimate the desired trajectory for each agent with guaranteed observation
performance that allows the directions among the agents to be non-identical. In
the second one, by establishing a novel lemma regarding Nussbaum function, a
new adaptive control protocol is developed for each agent based on backstepping
technique, which not only steers the output to asymptotically track the
corresponding estimated signal with arbitrarily prescribed transient
performance, but also largely extends the scope of application since the
unknown control gains are allowed to be time-varying and even state-dependent.
In such an indirect way, the underlying problem is tackled with the output
tracking error converging into an arbitrarily pre-assigned residual set
exhibiting an arbitrarily pre-defined convergence rate. Besides, all the
internal signals are ensured to be semi-globally ultimately uniformly bounded
(SGUUB). Finally, simulation results are provided to illustrate the
effectiveness of the co-designed scheme
Distributed Adaptive Control for a Class of Heterogeneous Nonlinear Multi-Agent Systems with Nonidentical Dimensions
A novel feedback distributed adaptive control strategy based on radial basis neural network (RBFNN) is proposed for the consensus control of a class of leaderless heterogeneous nonlinear multi-agent systems with the same and different dimensions. The distributed control, which consists of a sequence of comparable matrices or vectors, can make that all the states of each agent to attain consensus dynamic behaviors are defined with similar parameters of each agent with nonidentical dimensions. The coupling weight adaptation laws and the feedback management of neural network weights ensure that all signals in the closed-loop system are uniformly ultimately bounded. Finally, two simulation examples are carried out to validate the effectiveness of the suggested control design strategy
Distributed Adaptive Consensus Control of Nonlinear Output-Feedback Systems on Directed Graphs
This paper deals with consensus control in leader-follower format of a class of network-connected uncertain nonlinear systems by output feedback. Each subsystem is in the nonlinear output feedback form with unknown parameters, and the connection graph among the subsystems is directed. Distributed adaptive control inputs are designed to achieve the consensus control in the sense that the subsystem states asymptotically follow the subsystem at node 0 with no input, which is also known as the leader. The proposed adaptive control only uses relative output measurements and the local information of the connection to each subsystem, and hence the proposed adaptive control is fully distributed. The proposed scheme is different from the consensus output regulation schemes literature, and the leader plays a similar role as a reference model in the classic model reference adaptive control. (C) 2016 Elsevier Ltd. All rights reserved.National Natural Science Foundation of China [61473005, 11332001]; 111 Project [B08015]SCI(E)[email protected]; [email protected]
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