Model-free predictive control: an algorithmic approach

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Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.3/78573

Title: Model-free predictive control: an algorithmic approach
Author(s): Barry, Tim
Abstract: This book presents a data-driven approach to constrained control in the form of a subspace-based state-space system identification algorithm integrated into a model predictive controller. Previous research into this area focused on the system identification aspects resulting in an omission of many of the features that would make such a control strategy attractive to industry. These features include constraint handling, zero-offset set-point tracking and non-stationary disturbance rejection. Parameterisation with Laguerre orthonormal functions was proposed for the reduction in computational load of the controller. Simulation studies were performed using three real-world systems demonstrating: identification capabilities in the presence of white noise and non-stationary disturbances; unconstrained and constrained control; and the benefits and costs of parameterisation with Laguerre polynomials. The discussed algorithms have also been presented in Matlab code.
Publication type: Book
Research centre: Swinburne University of Technology. Faculty of Engineering and Industrial Sciences
Publication year: 2010
Keyword(s): Laguerre orthonormal function; Model-free predictive controller
Publisher: VDM Verlag Dr. Müller
ISBN: 9783639227406, 3639227409
Publisher URL: http://www.vdm-publishing.com/