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DYNAMIC MODELING AND MODEL BASED CONTROL OF AN INDUCTION MACHINE
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| Author(s) |
L. Krüger; D. Naunin |
| Abstract |
The proposed approach - without using of analytical system knowledge - seems to be a useful instrument to
model and control nonlinear dynamic systems. Therefore a representations of non-linear discrete time systems and two model-based control structures [Internal Model Control (IMC) and Model Predictive Control (MPC)] of an induction
motor. are discussed. To acquire the system data for estimation the drive was stimulated by a random ternary speed reference signal sequence. Different nonlinear model structures- the stochastic NARMAX-model- and different kinds of artificial Neural Networks - the Multilayer Perceptron Network (MLP) and the Radial Basis· Function Network (RBF) - have been used to model the real process dynamics. These structures are compared with regard to the
modeling validity and the computational expense on a parallel processor system. Furthermore the control performance of both control structures are discussed. |
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| Filename: | Unnamed file |
| Filesize: | 634.4 KB |
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| Type |
Members Only |
| Date |
Last modified 2016-04-04 by System |
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