1,200 research outputs found
Output consensus of nonlinear multi-agent systems with unknown control directions
In this paper, we consider an output consensus problem for a general class of
nonlinear multi-agent systems without a prior knowledge of the agents' control
directions. Two distributed Nussbaumtype control laws are proposed to solve the
leaderless and leader-following adaptive consensus for heterogeneous multiple
agents. Examples and simulations are given to verify their effectivenessComment: 10 pages;2 figure
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
Adaptive Fuzzy Tracking Control with Global Prescribed-Time Prescribed Performance for Uncertain Strict-Feedback Nonlinear Systems
Adaptive fuzzy control strategies are established to achieve global
prescribed performance with prescribed-time convergence for strict-feedback
systems with mismatched uncertainties and unknown nonlinearities. Firstly, to
quantify the transient and steady performance constraints of the tracking
error, a class of prescribed-time prescribed performance functions are
designed, and a novel error transformation function is introduced to remove the
initial value constraints and solve the singularity problem in existing works.
Secondly, based on dynamic surface control methods, controllers with or without
approximating structures are established to guarantee that the tracking error
achieves prescribed transient performance and converges into a prescribed
bounded set within prescribed time. In particular, the settling time and
initial value of the prescribed performance function are completely independent
of initial conditions of the tracking error and system parameters, which
improves existing results. Moreover, with a novel Lyapunov-like energy
function, not only the differential explosion problem frequently occurring in
backstepping techniques is solved, but the drawback of the semi-global
boundedness of tracking error induced by dynamic surface control can be
overcome. The validity and effectiveness of the main results are verified by
numerical simulations on practical examples
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