A Soft Constrained MPC Formulation Enabling Learning From Trajectories With Constraint Violations
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Date
2021
Publication Type
Journal Article
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yes
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Abstract
In practical model predictive control (MPC) implementations, constraints on the states are typically softened to ensure feasibility despite unmodeled disturbances. In this work, we propose a soft constrained MPC formulation supporting polytopic terminal sets in half-space and vertex representation, which significantly increases the feasible set while maintaining asymptotic stability in case of constraint violations. The proposed formulation allows for leveraging system trajectories that violate state constraints to iteratively improve the MPC controller’s performance. To this end, we apply convex optimization techniques to obtain a data-driven terminal cost and set, which result in a quadratic MPC problem.
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published
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Journal / series
Volume
6
Pages / Article No.
980 - 985
Publisher
IEEE
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Software
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Subject
Predictive control for linear systems; Constrained control; Iterative learning control
Organisational unit
09563 - Zeilinger, Melanie / Zeilinger, Melanie
Notes
Funding
157601 - Safety and Performance for Human in the Loop Control (SNF)