A Sensitivity-Based Data Augmentation Framework for Model Predictive Control Policy Approximation

Approximating model predictive control (MPC) policy using expert-based supervised learning techniques requires labeled training datasets sampled from the MPC policy. This is typically obtained by sampling the feasible state space and evaluating the control law by solving the numerical optimization p...

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Bibliographic Details
Published in:IEEE transactions on automatic control 2022-11, Vol.67 (11), p.6090-6097
Main Author: Krishnamoorthy, Dinesh
Format: Article
Language:English
Subjects:
Approximation
Approximation algorithms
Control theory
Data augmentation
Deep learning
learning-based control
Mathematical analysis
Optimal control
Optimization
Parameter sensitivity
parametric optimization
Predictive control
Sensitivity
Training
Training data
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Summary:Approximating model predictive control (MPC) policy using expert-based supervised learning techniques requires labeled training datasets sampled from the MPC policy. This is typically obtained by sampling the feasible state space and evaluating the control law by solving the numerical optimization problem offline for each sample. Although the resulting approximate policy can be cheaply evaluated online, generating large training samples to learn the MPC policy can be time-consuming and prohibitively expensive. This is one of the fundamental bottlenecks that limit the design and implementation of MPC policy approximation. This technical article aims to address this challenge, and proposes a novel sensitivity-based data augmentation scheme for direct policy approximation. The proposed approach is based on exploiting the parametric sensitivities to cheaply generate additional training samples in the neighborhood of the existing samples.
ISSN:0018-9286
1558-2523
DOI:10.1109/TAC.2021.3124983