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Robust Control Toolbox 3.4
Product Description
- Introduction and Key Features
- Working with the Robust Control Toolbox
- Modeling and Quantifying Plant Uncertainty
- Performing Robustness Analysis
- Synthesizing Robust Multivariable Controllers
- Reducing Controller and Plant Model Order
Reducing Controller and Plant Model Order
The Robust Control Toolbox provides tools for reducing the order (number of states) of a plant or controller model while preserving its essential dynamics. Detailed first-principles or finite-element plant models may have a high order. Similarly, H
or µ-synthesis algorithms tend to produce high-order controllers with superfluous states. In both cases, model reduction lets you develop approximate plant and controller models that are reliable and cost-effective to implement.
A comparison of the original and reduced-order models. Click on image to see enlarged view.
The toolbox offers state-of-the-art algorithms for extracting reduced-order models while controlling the approximation error. The reduction techniques and error-bound calculations are based on Hankel singular values of the system, which measure the energy of the states. By retaining high-energy states and ignoring low-energy states, the reduced model preserves the essential features of the original model. You can use the absolute or relative approximation error to select the order, and use frequency-dependent weights to determine the level of accuracy appropriate to the frequency ranges.
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