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Generation of an optimal architecture of neuro force controllers for robot manipulators in unknown environments using genetic programming with fuzzy fitness evaluation

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Abstract

 In this paper, we have applied genetic programming to generate an optimal architecture of neuro force controllers for robot manipulators in any environment. In order to perform precise force control in unknown environments, the optimal structured neuro force controller is generated using genetic programming with fuzzy fitness evaluation. After the architecture of the neuro controller has been optimized for any kinds of environments, it can be applied for a robot contact task with an unknown environment in on-line manner using its own adaptation ability. An effective crossover operation is proposed for the efficient evolution of the controllers. The simulation has been carried out to evaluate the effectiveness of the proposed robot force controller.

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Authors and Affiliations

  1. Department of Advanced Systems Control Engineering, Graduate School of Science and Engineering, Saga University, 1 Honjomachi, Saga 840-8502, Japan E-mail: kiguchi@ieee.org, , , , , , JP

    K. Kiguchi, H. Miyaji, K. Watanabe & K. Izumi

  2. Center of Cooperative Research in Advanced Science and Technology, Nagoya University, 1 Furo-cho, Chikusa-ku, Nagoya 464-8603, Japan, , , , , , JP

    T. Fukuda

Authors
  1. K. Kiguchi
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  2. H. Miyaji
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  3. K. Watanabe
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  4. K. Izumi
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  5. T. Fukuda
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Kiguchi, K., Miyaji, H., Watanabe, K. et al. Generation of an optimal architecture of neuro force controllers for robot manipulators in unknown environments using genetic programming with fuzzy fitness evaluation. Soft Computing 5, 237–242 (2001). https://doi.org/10.1007/s005000100087

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  • DOI: https://doi.org/10.1007/s005000100087

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