A Simple Distributed Adaptive Consensus Tracking Control of High Order Nonlinear Multi-Agent Systems

Zamani, Najmeh; Askari, Javad; Kamali, Marzieh

doi:10.1109/iraniancee.2019.8786402

Cited by 2 publications

(1 citation statement)

References 18 publications

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“…To demonstrate the effectiveness of the proposed design scheme, two examples are presented. First, a dynamic system consisting of three one‐link manipulators [22, 45] is considered, and then a numerical example is given. Example 1 Let the dynamic model of each manipulator in the three one‐link manipulator system noted above be given by the following non‐linear equations [22]:

\begin{matrix} right left right left right left right left right left right left0.278em 2em 0.278em 2em 0.278em 2em 0.278em 2em 0.278em 2em 0.278em3pt \\ {\dot{x}}_{i, 1} & = x_{i, 2}, \\ {\dot{x}}_{i, 2} & = x_{i, 3} - [\sin (x_{i, 1}), x_{i, 2}] θ_{i, 1} + \frac{I_{d, i}}{D_{i}}, \\ {\dot{x}}_{i, 3} & = b_{i} u_{i} - [x_{i, 2}, x_{i, 3}] θ_{i, 2} + \sin (x_{i, 3} (t - τ (t))), \end{matrix}

where

τ false(t false) = false(0.05 + 0.001 \sin false(0.1 t false) false)

\begin{matrix} right left right left right left right left right left right left0.278em 2em 0.278em 2em 0.278em 2em 0.278em 2em 0.278em 2em 0.278em3pt \\ \begin{array}{l} θ_{i, 1} & = {(][, \frac{N_{i}}{D_{i}}, \frac{B_{i}}{D_{i}})}^{normalT}, \end{array} \end{matrix}

…”

Section: Simulationsmentioning

confidence: 99%

Distributed adaptive consensus tracking control for non‐linear multi‐agent systems with time‐varying delays

Zamani

Askari

Kamali

et al. 2020

IET Control Theory & Appl

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In this study, a novel distributed adaptive controller is provided for consensus control of high‐order non‐linear multi‐agent systems with unknown time‐varying delays. The system is subject to uncertain disturbances, and the agents' dynamics are not known. Unlike the existing literature, the proposed method does not require time‐delay terms in system dynamics to be bounded. A neural network is used to model the unknown non‐linear dynamics. Then, despite the destabilising effect of the unknown delays, some adaptive rules based on the dynamic surface control are designed to achieve the consensus objective. The semi‐global uniform boundedness of the resultant closed‐loop signals and the convergence of the tracking errors to a neighbourhood of the origin are shown mathematically. Simulations verify the effectiveness of the results.

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