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Neural generalized predictive control: a newton-raphson implementation
| Content Provider | NASA Technical Reports Server (NTRS) |
|---|---|
| Author | Soloway, Donald Haley, Pamela J. |
| Copyright Year | 1997 |
| Description | An 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. |
| File Size | 1001741 |
| Page Count | 20 |
| File Format | |
| Alternate Webpage(s) | http://archive.org/details/NASA_NTRS_Archive_19970015094 |
| Archival Resource Key | ark:/13960/t56f0sd69 |
| Language | English |
| Publisher Date | 1997-02-01 |
| Access Restriction | Open |
| Subject Keyword | Cybernetics Algorithms Neural Nets Digital Simulation Predictions Feedforward Control Nonlinearity Time Measurement Newton-raphson Method Optimization Real Time Operation Ntrs Nasa Technical Reports Server (ntrs) Nasa Technical Reports Server Aerodynamics Aircraft Aerospace Engineering Aerospace Aeronautic Space Science |
| Content Type | Text |
| Resource Type | Technical Report |
