Soft computing system for motion control

C. Li*, C. Y. Lee

*Corresponding author for this work

Research output: Contribution to conferenceConference Paperpeer-review

3 Scopus citations

Abstract

The novel concept of pseudo-errors is proposed for self-organizing neuro-fuzzy system (SO-NFS) in this paper. The SO-NFS is viewed as a soft computing system. Based on the concept of pseudo-errors and a clustering algorithm, a neuro-fuzzy system possessing self-organizing ability and learning ability is presented. Pseudo-errors are viewed as the prior knowledge of a given unknown plant, and they are used as important information for the SO-NFS to self-organize its initial knowledge base. Pseudo-errors are potential errors that could be occurred in application. They can be used to indicate where the fuzzy regions are of interest in the input space for application, so to avoid the waste of computational resource. With the information of pseudo-errors, a more concise structure of neuro-fuzzy system can be self-organized. The well-known random optimization (RO) algorithm is used to search for a near-optimal set of parameters for the SO-NFS. The proposed approach is applied to motion control of an auto-warehousing crane system.

Original languageEnglish
Pages364-369
Number of pages6
StatePublished - 2001
Event2001 IEEE Intelligent Transportation Systems Proceedings - Oakland, CA, United States
Duration: 25 08 200129 08 2001

Conference

Conference2001 IEEE Intelligent Transportation Systems Proceedings
Country/TerritoryUnited States
CityOakland, CA
Period25/08/0129/08/01

Keywords

  • Cluster
  • Crane system
  • Motion control
  • Neuro-fuzzy system
  • Parameter learning
  • Pseudo-error
  • Random optimization
  • Self-organization
  • Soft-computing

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