An Improved Data Augmentation Scheme for Model Predictive Control Policy Approximation
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Abstract (arXiv)
This paper considers the problem of data generation for MPC policy approximation. Learning an approximate MPC policy from expert demonstrations requires a large data set consisting of optimal state-action pairs, sampled across the feasible state space. Yet, the key challenge of efficiently generating the training samples has not been studied widely. Recently, a sensitivity-based data augmentation framework for MPC policy approximation was proposed, where the parametric sensitivities are exploited to cheaply generate several additional samples from a single offline MPC computation. The error due to augmenting the training data set with inexact samples was shown to increase with the size of the neighborhood around each sample used for data augmentation. Building upon this work, this letter paper presents an improved data augmentation scheme based on predictor-corrector steps that enforces a user-defined level of accuracy, and shows that the error bound of the augmented samples are independent of the size of the neighborhood used for data augmentation.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 12 of its 44 references that are in the map. First posted on arXiv 9 Mar 2023.
Builds on
12 of 44 references.
References · 12
- 2018Efficient representation and approximation of model predictive control laws via deep learningcited by 58
- 2018Learning an Approximate Model Predictive Controller with Guaranteescited by 57
- 2017A Bayesian Data Augmentation Approach for Learning Deep Modelscited by 50
- 2017Improving Deep Learning using Generic Data Augmentationcited by 36
- 2014Interactive Policy Learning through Confidence-Based Autonomycited by 29
- 2019Large Scale Model Predictive Control with Neural Networks and Primal Active Setscited by 29
- 2019Near-Optimal Rapid MPC using Neural Networks: A Primal-Dual Policy Learning Frameworkcited by 16
- 2019Safe and Near-Optimal Policy Learning for Model Predictive Control using Primal-Dual Neural Networkscited by 13
- 2019Probabilistic performance validation of deep learning-based robust NMPC controllerscited by 11
- 2020Interactive Imitation Learning in State-Spacecited by 10
- 2020Fitting a Linear Control Policy to Demonstrations with a Kalman Constraintcited by 9
- 2020A Sensitivity-based Data Augmentation Framework for Model Predictive Control Policy Approximationcited by 6
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