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Real-time adaptive control using neural generalized predictive control
| Content Provider | NASA Technical Reports Server (NTRS) |
|---|---|
| Author | Haley, Pam Soloway, Don Gold, Brian |
| Copyright Year | 1999 |
| Description | The objective of this paper is to demonstrate the feasibility of a Nonlinear Generalized Predictive Control algorithm by showing real-time adaptive control on a plant with relatively fast time-constants. Generalized Predictive Control has classically been used in process control where linear control laws were formulated for plants with relatively slow time-constants. The plant of interest for this paper is a magnetic levitation device that is nonlinear and open-loop unstable. In this application, the reference model of the plant is a neural network that has an embedded nominal linear model in the network weights. The control based on the linear model provides initial stability at the beginning of network training. In using a neural network the control laws are nonlinear and online adaptation of the model is possible to capture unmodeled or time-varying dynamics. Newton-Raphson is the minimization algorithm. Newton-Raphson requires the calculation of the Hessian, but even with this computational expense the low iteration rate make this a viable algorithm for real-time control. |
| File Size | 147802 |
| Page Count | 5 |
| File Format | |
| Alternate Webpage(s) | http://archive.org/details/NASA_NTRS_Archive_20040086718 |
| Archival Resource Key | ark:/13960/t4xh4jc9v |
| Language | English |
| Publisher Date | 1999-01-01 |
| Access Restriction | Open |
| Subject Keyword | Numerical Analysis Stability Algorithms Predictions Variations Network Control Nonlinearity Feasibility Analysis Newton-raphson Method Time Constant Adaptive Control Magnetic Suspension 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 |
