An Improved Data Augmentation Scheme for Model Predictive Control Policy Approximation

TL;DR

This paper introduces an enhanced data augmentation method for MPC policy approximation that uses predictor-corrector steps to control accuracy, ensuring error bounds are unaffected by the augmentation neighborhood size.

Contribution

It proposes a novel data augmentation scheme based on predictor-corrector steps that maintains bounded errors regardless of neighborhood size, improving upon previous sensitivity-based methods.

Findings

Error bounds are independent of neighborhood size.

The proposed method enforces a user-defined accuracy level.

Augmented samples improve MPC policy approximation quality.

Abstract

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.