Robust Control Toolbox
Description
- Introduction and Key Features
- Modeling and Quantifying Plant Uncertainty
- Performing Robustness Analysis
- Synthesizing Robust Controllers
- Analyzing and Tuning Controllers in Simulink
- Reducing Plant and Controller Order
Reducing Plant and Controller Order
Detailed first-principles or finite-element plant models often have a large number of states. Similarly, H-infinity or mu-synthesis algorithms tend to produce high-order controllers with superfluous states. Robust Control Toolbox provides algorithms that let you reduce the order (number of states) of a plant or controller model while preserving its essential dynamics. As you extract lower-order models, which are more cost effective to implement, you can control the approximation error.
Bode plots comparing magnitude and phase of the original and reduced-order models for the rigid body motion dynamics of a multistory building.
The model reduction algorithms 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 focus the model reduction algorithms on specific frequency ranges.
