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Practical model predictive control for a class of nonlinear systems using linear parameter-varying representations
Citation Link: https://doi.org/10.15480/882.3559
Publikationstyp
Journal Article
Date Issued
2021-04-21
Sprache
English
Institut
TORE-DOI
TORE-URI
Journal
Volume
9
Start Page
62380
End Page
62393
Article Number
9410253
Citation
IEEE Access 9 : 9410253 (2021)
Publisher DOI
Scopus ID
Publisher
IEEE
In this paper, a practical model predictive control (MPC) for tracking desired reference trajectories is demonstrated for controlling a class of nonlinear systems subject to constraints, which comprises diverse mechanical applications. Owing to the linear parameter-varying (LPV) formulation of the associated nonlinear dynamics, the online MPC optimization problem is solvable as a single quadratic programming (QP) problem of complexity similar to that of LTI systems. For offset-free tracking, based on the notion of admissible reference, the controller ensures convergence to any admissible reference while its deviation from the desired reference is penalized in the stage cost of the optimization problem. This mechanism provides a safety feature under the physical limitations of the system. To guarantee stability and recursive feasibility, a terminal cost as a tracking error penalty term and a terminal constraint associated with both the terminal state and the admissible reference are included. We use tube-based concept to deal with the uncertainty of the scheduling parameter over the prediction horizon. Therefore, the online optimization problem is solved for only the nominal system corresponding to the current value of the scheduling parameter and subject to tightened constraint sets. The proposed approach has been implemented successfully in real-time onto a robotic manipulator, the experimental results illustrates its efficiency and practicality.
Subjects
Constrained systems
linear parameter-varying systems
model predictive control
robotic manipulators
robust stability
DDC Class
004: Informatik
600: Technik
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