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| Title: | Asymptotic stability of nonholonomic mobile robot formations using multilayer neural networks | |
| Author (s): | Dierks, Travis A. Sarangapani, Jagannathan | |
| Department/Lab Affiliations: | Engineering Management & Systems Engineering | |
| Keywords: | Asymptotic stability Formation control Kinematic/dynamic controlle Lyapunov methods Multi-robot systems Neural network Neurocontrollers RISE | |
| Issue Date: | 2007 | |
| Publisher: | Institute of Electrical and Electronics Engineers IEEE | |
| Citation: | Dierks, T., and Jagannathan, S. "Asymptotic stability of nonholonomic mobile robot formations using neural networks." 46th IEEE Conference on Decision and Control, 2007 | |
| 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. | |
| Type: | Article text | |
| In Title: | 46th IEEE Conference on Decision and Control | |
| Copyright Notice: | This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. allows publisher's final version to be uploaded FULL COPYRIGHT INFORMATION: | |
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| title | Asymptotic stability of nonholonomic mobile robot formations using multilayer neural networks | |
| contributor.author | Dierks, Travis A. | |
| contributor.author | Sarangapani, Jagannathan | |
| contributor.deptlab | Engineering Management & Systems Engineering | |
| contributor.sponsor | GAANN Program | |
| contributor.sponsor | U.S. Department of Education | |
| subject | Asymptotic stability | |
| subject | Formation control | |
| subject | Kinematic/dynamic controlle | |
| subject | Lyapunov methods | |
| subject | Multi-robot systems | |
| subject | Neural network | |
| subject | Neurocontrollers | |
| subject | RISE | |
| date.issued | 2007 | |
| publisher | Institute of Electrical and Electronics Engineers IEEE | |
| identifier.citation | Dierks, T., and Jagannathan, S. "Asymptotic stability of nonholonomic mobile robot formations using neural networks." 46th IEEE Conference on Decision and Control, 2007 | |
| identifier.pub.URI | ||
| description.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. | |
| type | Article | |
| type.DCMIType | text | |
| type.status | Final version | |
| rights | This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. | |
| rights | allows publisher's final version to be uploaded | |
| rights.URI | ||
| rights.URI | ||
| rights.URI | ||
| relation.isPartOf | 46th IEEE Conference on Decision and Control | |
| date.accessioned | 2008-07-23T17:15:00Z | |
| date.available | 2008-08-04T18:42:33Z | |
| identifier.persist.URI | ||
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