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Nonlinear Performance Seeking Control using Fuzzy Model Reference Learning Control and the Method of Steepest DescentPerformance Seeking Control (PSC) attempts to find and control the process at the operating condition that will generate maximum performance. In this paper a nonlinear multivariable PSC methodology will be developed, utilizing the Fuzzy Model Reference Learning Control (FMRLC) and the method of Steepest Descent or Gradient (SDG). This PSC control methodology employs the SDG method to find the operating condition that will generate maximum performance. This operating condition is in turn passed to the FMRLC controller as a set point for the control of the process. The conventional SDG algorithm is modified in this paper in order for convergence to occur monotonically. For the FMRLC control, the conventional fuzzy model reference learning control methodology is utilized, with guidelines generated here for effective tuning of the FMRLC controller.
Document ID
Acquisition Source
Legacy CDMS
Document Type
Conference Paper
Kopasakis, George
(NASA Lewis Research Center Cleveland, OH United States)
Date Acquired
September 6, 2013
Publication Date
May 1, 1997
Subject Category
Aircraft Propulsion And Power
Report/Patent Number
NAS 1.15:107454
AIAA Paper 97-3362
Meeting Information
Meeting: Jet Propulsion
Location: Seattle, WA
Country: United States
Start Date: July 6, 1997
End Date: July 9, 1997
Sponsors: American Society for Electrical Engineers, Society of Automotive Engineers, Inc., American Society of Mechanical Engineers, American Inst. of Aeronautics and Astronautics
Accession Number
Funding Number(s)
Distribution Limits
Work of the US Gov. Public Use Permitted.
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