Blending based multiple-model adaptive control of multivariable systems with application to lateral vehicle motion control

• Published in: European journal of control Vol. 58; pp. 1 - 10
• Main Authors: Zengin, H., Zengin, N., Fidan, B., Khajepour, A.
• Format: Journal Article
• Language: English
• Published: Philadelphia Elsevier Ltd 01.03.2021; Elsevier Limited
• Subjects: Adaptation; Adaptive control; Asymptotic properties; Blending; Controllers; Design; Mathematical models; MIMO; MIMO (control systems); Motion control; Multiple-model; Multivariable control; Parameter estimation; Parameter identification; Parameter uncertainty; Polytopic uncertainty; Supervisory control; Tracking control; Vehicle motion control; Multiple-model; Supervisory control; MIMO; Vehicle motion control; Adaptive control; Polytopic uncertainty
• ISSN: 0947-3580; 1435-5671
• DOI: 10.1016/j.ejcon.2020.12.007

This paper develops multiple fixed model blending based adaptive parameter identification schemes for multi-input multi-output (MIMO) systems with polytopic parameter uncertainty. The developed identification schemes are proven to be asymptotically stable for uncertain linear time-invariant (LTI) MIMO systems, and is shown to provide fast adaptation for even uncertain linear time-varying (LTV) systems. Furthermore, utilizing the proposed parameter identification schemes, a linear-quadratic (LQ) optimal multiple-model adaptive control (MMAC) scheme is developed for linear MIMO systems with polytopic uncertainties. The proposed MMAC scheme is proven to be asymptotically stable for LTI MIMO systems and applied to tracking control of uncertain lateral vehicle dynamics. A set of simulation test results are presented to verify the stability, effectiveness, and comparison of the proposed MMAC scheme.