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Telerobotic control of a mobile coordinated robotic server, executive summaryThis interim report continues with the research effort on advanced adaptive controls for space robotics systems. In particular, previous results developed by the principle investigator and his research team centered around fuzzy logic control (FLC) in which the lack of knowledge of the robotic system as well as the uncertainties of the environment are compensated for by a rule base structure which interacts with varying degrees of belief of control action using system measurements. An on-line adaptive algorithm was developed using a single parameter tuning scheme. In the effort presented, the methodology is further developed to include on-line scaling factor tuning and self-learning control as well as extended to the multi-input, multi-output (MIMO) case. Classical fuzzy logic control requires tuning input scale factors off-line through trial and error techniques. This is time-consuming and cannot adapt to new changes in the process. The new adaptive FLC includes a self-tuning scheme for choosing the scaling factors on-line. Further the rule base in classical FLC is usually produced by soliciting knowledge from human operators as to what is good control action for given circumstances. This usually requires full knowledge and experience of the process and operating conditions, which limits applicability. A self-learning scheme is developed which adaptively forms the rule base with very limited knowledge of the process. Finally, a MIMO method is presented employing optimization techniques. This is required for application to space robotics in which several degrees-of-freedom links are commonly used. Simulation examples are presented for terminal control - typical of robotic problems in which a desired terminal point is to be reached for each link. Future activities will be to implement the MIMO adaptive FLC on an INTEL microcontroller-based circuit and to test the algorithm on a robotic system at the Mars Mission Research Center at North Carolina State University.
Document ID
19940015571
Acquisition Source
Legacy CDMS
Document Type
Contractor Report (CR)
Authors
Lee, Gordon
(North Carolina State Univ. Raleigh, NC, United States)
Date Acquired
September 6, 2013
Publication Date
December 7, 1993
Subject Category
Mechanical Engineering
Report/Patent Number
NASA-CR-194685
NAS 1.26:194685
Report Number: NASA-CR-194685
Report Number: NAS 1.26:194685
Accession Number
94N20044
Funding Number(s)
CONTRACT_GRANT: NAG1-1283
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.
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