Asymptotic Stability of Nonholonomic Mobile Robot Formations Using Multilayer Neural Networks

Jagannathan Sarangapani; Travis Alan Dierks

doi:10.1109/CDC.2007.4434811

Asymptotic Stability of Nonholonomic Mobile Robot Formations Using Multilayer Neural Networks

Jagannathan Sarangapani
, Travis Alan Dierks

Electrical and Computer Engineering

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

Abstract

In this paper, a combined kinematic/torque control law is developed for leader-follower based formation control using backstepping in order to accommodate the dynamics of the robots and the formation in contrast with kinematic-based formation controllers that are widely reported in the literature. A multilayer neural network (NN) is introduced along with robust integral of the sign of the error (RISE) feedback to approximate the dynamics of the follower as well as its leader using online weight tuning. It is shown using Lyapunov theory that the errors for the entire formation are asymptotically stable and the NN weights are bounded as opposed to uniformly ultimately bounded (UUB) stability which is typical with most NN controllers. Simulation results are included.

Original language	American English
Journal	Proceedings of the 46th IEEE Conference on Decision and Control (2007, New Orleans, LA)
DOIs	https://doi.org/10.1109/CDC.2007.4434811
State	Published - Jan 1 2007

Keywords

Asymptotic Stability
Formation Control
Kinematic/Dynamic Controlle
Lyapunov Methods
Multi-Robot Systems
Neural Network
Neurocontrollers
RISE

Disciplines

Operations Research, Systems Engineering and Industrial Engineering

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10.1109/CDC.2007.4434811License: Unspecified

Asymptotic Stability of Nonholonomic Mobile Robot Formations UsinFinal published version, 383 KBLicense: Unspecified

Cite this

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title = "Asymptotic Stability of Nonholonomic Mobile Robot Formations Using Multilayer Neural Networks",

abstract = " In this paper, a combined kinematic/torque control law is developed for leader-follower based formation control using backstepping in order to accommodate the dynamics of the robots and the formation in contrast with kinematic-based formation controllers that are widely reported in the literature. A multilayer neural network (NN) is introduced along with robust integral of the sign of the error (RISE) feedback to approximate the dynamics of the follower as well as its leader using online weight tuning. It is shown using Lyapunov theory that the errors for the entire formation are asymptotically stable and the NN weights are bounded as opposed to uniformly ultimately bounded (UUB) stability which is typical with most NN controllers. Simulation results are included.",

keywords = "Asymptotic Stability, Formation Control, Kinematic/Dynamic Controlle, Lyapunov Methods, Multi-Robot Systems, Neural Network, Neurocontrollers, RISE",

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N2 - In this paper, a combined kinematic/torque control law is developed for leader-follower based formation control using backstepping in order to accommodate the dynamics of the robots and the formation in contrast with kinematic-based formation controllers that are widely reported in the literature. A multilayer neural network (NN) is introduced along with robust integral of the sign of the error (RISE) feedback to approximate the dynamics of the follower as well as its leader using online weight tuning. It is shown using Lyapunov theory that the errors for the entire formation are asymptotically stable and the NN weights are bounded as opposed to uniformly ultimately bounded (UUB) stability which is typical with most NN controllers. Simulation results are included.

AB - In this paper, a combined kinematic/torque control law is developed for leader-follower based formation control using backstepping in order to accommodate the dynamics of the robots and the formation in contrast with kinematic-based formation controllers that are widely reported in the literature. A multilayer neural network (NN) is introduced along with robust integral of the sign of the error (RISE) feedback to approximate the dynamics of the follower as well as its leader using online weight tuning. It is shown using Lyapunov theory that the errors for the entire formation are asymptotically stable and the NN weights are bounded as opposed to uniformly ultimately bounded (UUB) stability which is typical with most NN controllers. Simulation results are included.

KW - Asymptotic Stability

KW - Formation Control

KW - Kinematic/Dynamic Controlle

KW - Lyapunov Methods

KW - Multi-Robot Systems

KW - Neural Network

KW - Neurocontrollers

KW - RISE

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DO - 10.1109/CDC.2007.4434811

M3 - Article

JO - Proceedings of the 46th IEEE Conference on Decision and Control (2007, New Orleans, LA)

JF - Proceedings of the 46th IEEE Conference on Decision and Control (2007, New Orleans, LA)

ER -

Asymptotic Stability of Nonholonomic Mobile Robot Formations Using Multilayer Neural Networks

Abstract

Keywords

Disciplines

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