Neuro-adaptive cooperative tracking control of unknown higher-order affine nonlinear systems

Sami El-Ferik; Aminuddin Qureshi; Frank L. Lewis

doi:10.1016/j.automatica.2013.12.033

Neuro-adaptive cooperative tracking control of unknown higher-order affine nonlinear systems

Sami El-Ferik
, Aminuddin Qureshi
, Frank L. Lewis

Research output: Contribution to journal › Article › peer-review

142 Scopus citations

Abstract

In this paper we propose a practical design method for distributed cooperative tracking control of a class of higher-order nonlinear multi-agent systems. Dynamics of the agents (also called the nodes) are assumed to be unknown to the controller and are estimated using Neural Networks. Linearization-based robust neuro-adaptive controller driving the follower nodes to track the trajectory of the leader node is proposed. The nodes are connected through a weighted directed graph with a time-invariant topology. In addition to the fact that only few nodes have access to the leader, communication among the follower nodes is limited with some nodes having access to the information of their neighbor nodes only. Command generated by the leader node is ultimately followed by the followers with bounded synchronization error. The proposed controller is well-defined in the sense that control effort is restrained to practical limits. The closed-loop system dynamics are proved to be stable and simulation results demonstrate the effectiveness of the proposed control scheme.

Original language	English
Pages (from-to)	798-808
Number of pages	11
Journal	Automatica
Volume	50
Issue number	3
DOIs	https://doi.org/10.1016/j.automatica.2013.12.033
State	Published - Mar 2014

Bibliographical note

Funding Information:
The authors would like to acknowledge the support provided by King Abdulaziz City for Science and Technology (KACST) through the Science and Technology Unit at King Fahd University of Petroleum and Minerals (KFUPM) for funding this work through project No. 09-SPA783-04 as part of the National Science, Technology and Innovation Plan. The material in this paper was not presented at any conference. This paper was recommended for publication in revised form by Associate Editor Warren E. Dixon under the direction of Editor Andrew R. Teel.

Keywords

Cooperative tracking control
Feedback linearization
Neural network adaptive control

ASJC Scopus subject areas

Control and Systems Engineering
Electrical and Electronic Engineering

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10.1016/j.automatica.2013.12.033

Cite this

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title = "Neuro-adaptive cooperative tracking control of unknown higher-order affine nonlinear systems",

abstract = "In this paper we propose a practical design method for distributed cooperative tracking control of a class of higher-order nonlinear multi-agent systems. Dynamics of the agents (also called the nodes) are assumed to be unknown to the controller and are estimated using Neural Networks. Linearization-based robust neuro-adaptive controller driving the follower nodes to track the trajectory of the leader node is proposed. The nodes are connected through a weighted directed graph with a time-invariant topology. In addition to the fact that only few nodes have access to the leader, communication among the follower nodes is limited with some nodes having access to the information of their neighbor nodes only. Command generated by the leader node is ultimately followed by the followers with bounded synchronization error. The proposed controller is well-defined in the sense that control effort is restrained to practical limits. The closed-loop system dynamics are proved to be stable and simulation results demonstrate the effectiveness of the proposed control scheme.",

keywords = "Cooperative tracking control, Feedback linearization, Neural network adaptive control",

author = "Sami El-Ferik and Aminuddin Qureshi and Lewis, \{Frank L.\}",

note = "Funding Information: The authors would like to acknowledge the support provided by King Abdulaziz City for Science and Technology (KACST) through the Science and Technology Unit at King Fahd University of Petroleum and Minerals (KFUPM) for funding this work through project No. 09-SPA783-04 as part of the National Science, Technology and Innovation Plan. The material in this paper was not presented at any conference. This paper was recommended for publication in revised form by Associate Editor Warren E. Dixon under the direction of Editor Andrew R. Teel. ",

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AU - Lewis, Frank L.

N1 - Funding Information: The authors would like to acknowledge the support provided by King Abdulaziz City for Science and Technology (KACST) through the Science and Technology Unit at King Fahd University of Petroleum and Minerals (KFUPM) for funding this work through project No. 09-SPA783-04 as part of the National Science, Technology and Innovation Plan. The material in this paper was not presented at any conference. This paper was recommended for publication in revised form by Associate Editor Warren E. Dixon under the direction of Editor Andrew R. Teel.

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N2 - In this paper we propose a practical design method for distributed cooperative tracking control of a class of higher-order nonlinear multi-agent systems. Dynamics of the agents (also called the nodes) are assumed to be unknown to the controller and are estimated using Neural Networks. Linearization-based robust neuro-adaptive controller driving the follower nodes to track the trajectory of the leader node is proposed. The nodes are connected through a weighted directed graph with a time-invariant topology. In addition to the fact that only few nodes have access to the leader, communication among the follower nodes is limited with some nodes having access to the information of their neighbor nodes only. Command generated by the leader node is ultimately followed by the followers with bounded synchronization error. The proposed controller is well-defined in the sense that control effort is restrained to practical limits. The closed-loop system dynamics are proved to be stable and simulation results demonstrate the effectiveness of the proposed control scheme.

AB - In this paper we propose a practical design method for distributed cooperative tracking control of a class of higher-order nonlinear multi-agent systems. Dynamics of the agents (also called the nodes) are assumed to be unknown to the controller and are estimated using Neural Networks. Linearization-based robust neuro-adaptive controller driving the follower nodes to track the trajectory of the leader node is proposed. The nodes are connected through a weighted directed graph with a time-invariant topology. In addition to the fact that only few nodes have access to the leader, communication among the follower nodes is limited with some nodes having access to the information of their neighbor nodes only. Command generated by the leader node is ultimately followed by the followers with bounded synchronization error. The proposed controller is well-defined in the sense that control effort is restrained to practical limits. The closed-loop system dynamics are proved to be stable and simulation results demonstrate the effectiveness of the proposed control scheme.

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KW - Neural network adaptive control

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Neuro-adaptive cooperative tracking control of unknown higher-order affine nonlinear systems

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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