Data-driven Methods for Tracking Improvement of Systems subject to Stochastic Disturbances

Mark Butcher, Alireza Karimi, Roland Longchamp

Overview

In this project data-driven approaches, which by-pass the system modelling step and so do not suffer from unmodelled dynamics, are being investigated to improve the tracking performance of systems subject to stochastic disturbances.

For the general tracking problem, a precompensator controller is used to filter the desired output signal before it is applied as an input to the system. The precompensator’s parameters are tuned directly using measured data. This data is affected by stochastic disturbances, such as measurement noise. The effect of these disturbances on the calculated parameters is being studied and the correlation approach is used to reduce it.

For the specific problem where the tracking task is repetitive, a situation frequently encountered in industrial applications, Iterative Learning Control (ILC) is proposed. ILC uses measurements from previous repetitions to adjust the system’s input for the current repetition in a manner so as to improve the tracking. As measurements are used, the calculated input is sensitive to the stochastic disturbances affecting them. The effect of these disturbances on the learning procedure is being examined and algorithms that are less sensitive to their presence are being developed.

Additionally, the proposed methods are being extended for use on Linear Parameter Varying (LPV) systems, in which the system’s dynamics change as a function of a scheduling parameter.

Keywords : Data-driven controller tuning; Precompensator; Tracking Improvement; Correlation approach; Iterative Learning Control; Stochastic disturbances;

 

 

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Related publications

Journal Articles

2008

A. Karimi; M. Butcher; R. Longchamp : Model-Free Precompensator Tuning Based on the Correlation Approach; IEEE Transactions on Control Systems Technology. 2008. DOI : 10.1109/TCST.2007.916315.
M. Butcher; A. Karimi; R. Longchamp : A Statistical Analysis of Certain Iterative Learning Control Algorithms; International Journal of Control. 2008. DOI : 10.1080/00207170701484851.

Conference Papers

2008

M. Butcher; A. Karimi; R. Longchamp : Data-driven Precompensator Tuning for Linear Parameter Varying Systems. 2008. 47th IEEE Conference on Decision and Control, Cancun, Mexico, December 9-11, 2008. p. 3854-3859.
M. Butcher; A. Karimi; R. Longchamp : On the Consistency of Certain Identification Methods for Linear Parameter Varying Systems. 2008. IFAC World Congress 2008, Seoul, July 6-11, 2008.
M. Butcher; A. Karimi; R. Longchamp : Iterative Learning Control based on Stochastic Approximation. 2008. IFAC World Congress 2008, Seoul, July 6-11, 2008.

2006

M. Butcher; A. Karimi; R. Longchamp : A Comparison of Iterative Learning Control Algorithms with application to a Linear Motor System. 2006. IEEE IECON'06, Paris, France, November 7-10, 2006.

2005

A. Karimi; M. Butcher; R. Longchamp : Model-Free Precompensator and Feedforward Tuning Based on the Correlation Approach. 2005. Joint IEEE CDC and ECC, Seville, Spain, December, 2005.