research-article

Enhancing a Model-Free Adaptive Controller through Evolutionary Computation

Authors:

Anthony J. Clark,

Philip K. McKinley,

Xiaobo TanAuthors Info & Claims

GECCO '15: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation

Pages 137 - 144

https://doi.org/10.1145/2739480.2754762

Published: 11 July 2015 Publication History

Abstract

Many robotic systems experience fluctuating dynamics during their lifetime. Variations can be attributed in part to material degradation and decay of mechanical hardware. One approach to mitigating these problems is to utilize an adaptive controller. For example, in model-free adaptive control (MFAC) a controller learns how to drive a system by continually updating link weights of an artificial neural network (ANN). However, determining the optimal control parameters for MFAC, including the structure of the underlying ANN, is a challenging process. In this paper we investigate how to enhance the online adaptability of MFAC-based systems through computational evolution. We apply the proposed methods to a simulated robotic fish propelled by a flexible caudal fin. Results demonstrate that the robot is able to effectively respond to changing fin characteristics and varying control signals when using an evolved MFAC controller. Notably, the system is able to adapt to characteristics not encountered during evolution. The proposed technique is general and can be applied to improve the adaptability of other cyber-physical systems.

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Cited By

Baccouri HGuillou GBabau JPretschner AVoss SBurgueño L(2019)A model-driven approach to automate tuning of continuous controller parametersProceedings of the 22nd International Conference on Model Driven Engineering Languages and Systems Companion10.1109/MODELS-C.2019.00087(568-576)Online publication date: 15-Sep-2019
https://dl.acm.org/doi/10.1109/MODELS-C.2019.00087
Clark ACissell KMoore JLiu X(2018)Evolving Controllers for a Transformable Wheel Mobile RobotComplexity10.1155/2018/76920422018Online publication date: 1-Jan-2018
https://dl.acm.org/doi/10.1155/2018/7692042
Veenstra FJørgensen JRisi STakadama K(2018)Evolution of fin undulation on a physical knifefish-inspired soft robotProceedings of the Genetic and Evolutionary Computation Conference10.1145/3205455.3205583(157-164)Online publication date: 2-Jul-2018
https://dl.acm.org/doi/10.1145/3205455.3205583
Show More Cited By

Index Terms

Enhancing a Model-Free Adaptive Controller through Evolutionary Computation
1. Computing methodologies
  1. Artificial intelligence
    1. Control methods
    2. Search methodologies
2. Theory of computation
  1. Design and analysis of algorithms
    1. Algorithm design techniques
      1. Dynamic programming

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cover image ACM Conferences

GECCO '15: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation

July 2015

1496 pages

ISBN:9781450334723

DOI:10.1145/2739480

Editor:
Sara Silva
Universidade de Lisboa, Portugal
,
General Chair:
Anna I. Esparcia-Alcázar
Universitat Politècnica de València, Spain

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

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New York, NY, United States

Publication History

Published: 11 July 2015

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National Science Foundation

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GECCO '15

Sponsor:

SIGEVO

GECCO '15: Genetic and Evolutionary Computation Conference

July 11 - 15, 2015

Madrid, Spain

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GECCO '15 Paper Acceptance Rate 182 of 505 submissions, 36%;

Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

Baccouri HGuillou GBabau JPretschner AVoss SBurgueño L(2019)A model-driven approach to automate tuning of continuous controller parametersProceedings of the 22nd International Conference on Model Driven Engineering Languages and Systems Companion10.1109/MODELS-C.2019.00087(568-576)Online publication date: 15-Sep-2019
https://dl.acm.org/doi/10.1109/MODELS-C.2019.00087
Clark ACissell KMoore JLiu X(2018)Evolving Controllers for a Transformable Wheel Mobile RobotComplexity10.1155/2018/76920422018Online publication date: 1-Jan-2018
https://dl.acm.org/doi/10.1155/2018/7692042
Veenstra FJørgensen JRisi STakadama K(2018)Evolution of fin undulation on a physical knifefish-inspired soft robotProceedings of the Genetic and Evolutionary Computation Conference10.1145/3205455.3205583(157-164)Online publication date: 2-Jul-2018
https://dl.acm.org/doi/10.1145/3205455.3205583
Clark A(2017)Evolving adabot: A mobile robot with adjustable wheel extensions2017 IEEE Symposium Series on Computational Intelligence (SSCI)10.1109/SSCI.2017.8280979(1-8)Online publication date: Nov-2017
https://doi.org/10.1109/SSCI.2017.8280979
Clark ADeVries BMoore JCheng BMcKinley P(2016)An evolutionary approach to discovering execution mode boundaries for adaptive controllers2016 IEEE Symposium Series on Computational Intelligence (SSCI)10.1109/SSCI.2016.7850178(1-8)Online publication date: Dec-2016
https://doi.org/10.1109/SSCI.2016.7850178
Clark ATan XMcKinley P(2015)Evolutionary multiobjective design of a flexible caudal fin for robotic fishBioinspiration & Biomimetics10.1088/1748-3190/10/6/06500610:6(065006)Online publication date: 25-Nov-2015
https://doi.org/10.1088/1748-3190/10/6/065006

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