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Regularized Filtered Basis Functions Approach for Accurate Tracking of Discrete-Time Linear Time Invariant Systems With Bounded Random Uncertainties

[+] Author Affiliations
Keval S. Ramani, Chinedum E. Okwudire

University of Michigan, Ann Arbor, MI

Paper No. DSCC2016-9885, pp. V002T28A004; 6 pages
  • ASME 2016 Dynamic Systems and Control Conference
  • Volume 2: Mechatronics; Mechatronics and Controls in Advanced Manufacturing; Modeling and Control of Automotive Systems and Combustion Engines; Modeling and Validation; Motion and Vibration Control Applications; Multi-Agent and Networked Systems; Path Planning and Motion Control; Robot Manipulators; Sensors and Actuators; Tracking Control Systems; Uncertain Systems and Robustness; Unmanned, Ground and Surface Robotics; Vehicle Dynamic Controls; Vehicle Dynamics and Traffic Control
  • Minneapolis, Minnesota, USA, October 12–14, 2016
  • Conference Sponsors: Dynamic Systems and Control Division
  • ISBN: 978-0-7918-5070-1
  • Copyright © 2016 by ASME


This paper proposes a regularized filtered basis functions (RFBF) approach for robust tracking of discrete-time linear time invariant systems with bounded random (unstructured) uncertainties. Identical to the filtered basis functions (FBF) approach, studied in prior work by the authors, the RFBF approach expresses the control trajectory as a linear combination of user-defined basis functions with unknown coefficients. The basis functions are forward filtered using a model of the system and their coefficients are selected to fulfill the tracking control objective. The two approaches differ in the coefficient selection process. The FBF approach selects the coefficients such that the tracking error is minimized in the absence of uncertainties, whereas, the proposed RFBF approach formulates the coefficient selection problem as a constrained game-type problem where the coefficients are selected to minimize the worst case tracking error in the presence of model uncertainty. Illustrative examples are used to demonstrate significantly more accurate tracking of uncertain systems using RFBF compared with FBF.

Copyright © 2016 by ASME



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