Generalized fuzzy environment models learned with genetic algorithms for a robotic force control

Fusaomi Nagata, Keigo Watanabe, Kazuya Sato, Kiyotaka Izumi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Impedance control allows the manipulator to change the mechanical impedance such as inertia, damping and stiffness, acting between the end-effector and its environment. However, to achieve stable force control under unknown stiff environments, complicated tuning of desired impedance parameters is needed. Among the parameters, the desired damping is the most significant to suppress overshoots and oscillations. In this paper, the generalized fuzzy environment models with anisotropy is proposed to systematically determine the desired damping against to unknown environments. The models learned with genetic algorithms, can estimate the each directional stiffness of environment and yield the desired damping, considering the critical damping condition of the control system. Position and force control simulations are shown to demonstrate the effectiveness and promise of the models.

Original languageEnglish
Title of host publicationIEEE International Conference on Intelligent Robots and Systems
PublisherIEEE
Pages590-596
Number of pages7
Volume1
Publication statusPublished - 1999
Externally publishedYes
Event1999 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS'99): Human and Environment Friendly Robots whith High Intelligence and Emotional Quotients' - Kyongju, South Korea
Duration: Oct 17 1999Oct 21 1999

Other

Other1999 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS'99): Human and Environment Friendly Robots whith High Intelligence and Emotional Quotients'
CityKyongju, South Korea
Period10/17/9910/21/99

Fingerprint

Force control
Robotics
Damping
Genetic algorithms
Stiffness
Position control
End effectors
Manipulators
Anisotropy
Tuning
Control systems

ASJC Scopus subject areas

  • Control and Systems Engineering

Cite this

Nagata, F., Watanabe, K., Sato, K., & Izumi, K. (1999). Generalized fuzzy environment models learned with genetic algorithms for a robotic force control. In IEEE International Conference on Intelligent Robots and Systems (Vol. 1, pp. 590-596). IEEE.

Generalized fuzzy environment models learned with genetic algorithms for a robotic force control. / Nagata, Fusaomi; Watanabe, Keigo; Sato, Kazuya; Izumi, Kiyotaka.

IEEE International Conference on Intelligent Robots and Systems. Vol. 1 IEEE, 1999. p. 590-596.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Nagata, F, Watanabe, K, Sato, K & Izumi, K 1999, Generalized fuzzy environment models learned with genetic algorithms for a robotic force control. in IEEE International Conference on Intelligent Robots and Systems. vol. 1, IEEE, pp. 590-596, 1999 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS'99): Human and Environment Friendly Robots whith High Intelligence and Emotional Quotients', Kyongju, South Korea, 10/17/99.
Nagata F, Watanabe K, Sato K, Izumi K. Generalized fuzzy environment models learned with genetic algorithms for a robotic force control. In IEEE International Conference on Intelligent Robots and Systems. Vol. 1. IEEE. 1999. p. 590-596
Nagata, Fusaomi ; Watanabe, Keigo ; Sato, Kazuya ; Izumi, Kiyotaka. / Generalized fuzzy environment models learned with genetic algorithms for a robotic force control. IEEE International Conference on Intelligent Robots and Systems. Vol. 1 IEEE, 1999. pp. 590-596
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