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Neural Generalized Predictive Control: A Newton-Raphson ImplementationAn efficient implementation of Generalized Predictive Control using a multi-layer feedforward neural network as the plant's nonlinear model is presented. In using Newton-Raphson as the optimization algorithm, the number of iterations needed for convergence is significantly reduced from other techniques. The main cost of the Newton-Raphson algorithm is in the calculation of the Hessian, but even with this overhead the low iteration numbers make Newton-Raphson faster than other techniques and a viable algorithm for real-time control. This paper presents a detailed derivation of the Neural Generalized Predictive Control algorithm with Newton-Raphson as the minimization algorithm. Simulation results show convergence to a good solution within two iterations and timing data show that real-time control is possible. Comments about the algorithm's implementation are also included.
Document ID
19970015094
Acquisition Source
Langley Research Center
Document Type
Technical Memorandum (TM)
Authors
Soloway, Donald (NASA Langley Research Center Hampton, VA United States)
Haley, Pamela J. (NASA Langley Research Center Hampton, VA United States)
Date Acquired
September 6, 2013
Publication Date
February 1, 1997
Subject Category
Cybernetics
Report/Patent Number
NASA-TM-110244
NAS 1.15:110244
Report Number: NASA-TM-110244
Report Number: NAS 1.15:110244
Accession Number
97N18201
Funding Number(s)
PROJECT: RTOP 274-00-96-20
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.

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