Hamid Khatibi, Marc Kunze, Mark Butcher, Gorka Galdos, Klaske Vanheusden
Overview
Data-Driven Controller Tuning (Time-Domain Approaches)
Data-driven model-free controller-tuning approaches try to lump the identification, controller design and controller reduction together and present a direct “data-to-control” algorithm. This is very attractive, particularly when a mathematical model of the plant is not available and/or the nonlinear behavior of the plant cannot be identified easily and considered for controller design.
- Iterative Correlation-based Controller Tuning (ICbT)
- Data-Driven Controller Tuning with Guaranteed Stability
- Data-Driven Methods for Tracking Improvement
Data-Driven Controller Tuning (Frequency-Domain Approaches)
For LTI systems, frequency-domain nonparametric models (spectral models) can be obtained by spectral analysis from time-domain data. In this type of models the information is not condensed into a small set of parameters thus avoiding errors of unmodeled dynamics that appear in parametric models. Moreover, identification of spectral models need less a priori knowledge than the parametric identification methods (sampling period, time-delay, model structure, order of polynomials, etc.). On the other hand, several time-domain performance measures can be obtained from the frequency response of the system. The Nyquist stability criterion and model uncertainty can be represented in the frequency-domain. It is well known that major advances were obtained only when the control problem was regarded as a loop shaping problem in the frequency domain.
- Iterative Controller Tuning Based on the Closed-loop Relay Test
- Controller Design by Linear Programming
- H infinity Controller Design Using Spectral Models
Robust Control Design Using the Polynomial Approach
There are many well-developed control strategies based on an appropriate dynamic plant model. In many cases, dynamic systems can be expressed in terms of a linear time-invariant transfer function with parameter uncertainty or multiple models. This type of uncertainty can be taken directly into account using the polynomial approach for robust controller design. The following research projects are considered in this area: Related projects
- Modeling and Robust Control of Linear Direct-Drive Motors
- Main Axes Control of the Overwhelmingly Large Telescope
- Multi-model Robust Control Design
- Robust Control of Plants with Parametric Uncertainties
- Robust Controller Synthesis for Linear Systems with Parameter Uncertainty Using Convex Optimization
- Synthesis of Optimal Preview-based Tracking Compensators