Note: Some of the graphical elements of this site are only visible to browsers that support accepted web standards. The content of this site is, however, accessible to any browser or Internet device.
Control and Power Portfolio Partnership
ROBUST CONTROL, MODEL REDUCTION AND CONTROLLER APPROXIMATION
ACTIVE RESEARCH AREAS
- Model Reduction of Large Scale Systems: The aim of the project is to develop efficient model reduction techniques for complex systems of many thousands of differential equations. Two methods currently under investigation that are based on Krylov subspaces: Arnoldi and Lanczos approximations. Novel reduction techniques have been developed that mimic Lyapunov-based reduction techniques and use Linear Fractional Transformation (LFT) to simplify and improve the reduction.
- Controller Approximation for Linear Time-invariant (LTI) Systems: The aim is to integrate the controller reduction procedure into the controller synthesis techniques for model-based methods. Methods are developed to obtain a priori error bounds for the controller approximation and to estimate the performance and stability margin deteriorations. Riccati equations play a major role in these reduction procedures.
- Model and Controller Reduction for Linear Parameter Varying (LPV) Systems: Methods that are developed for LTI systems can be generalized to LPV through the Linear Matrix Inequalities tools. Many of the LTI robust control synthesis methods seem to carry naturally to LPV systems with quadratic stability and performance used as the measure.
- Structured Singular Values (µ) Analysis: A relation between µ-analysis and super optimization is established. This relation is used to improve the calculations of upper bounds on µ which is critical in many of the robust analysis results. New improved results are found and conditions are derived to further investigate different classes of µ.