On Building Myopic MPC Policies using Supervised Learning

TL;DR

This paper proposes a novel approach to approximate explicit MPC by learning the value function offline and using a short-horizon myopic MPC, reducing online computation while maintaining performance.

Contribution

It introduces a method to learn the value function offline with data augmentation, enabling efficient myopic MPC with reduced online complexity.

Findings

Reduced online computation in MPC using learned value functions

Effective offline training with sensitivity-based data augmentation

Maintained control performance with short-horizon MPC

Abstract

The application of supervised learning techniques in combination with model predictive control (MPC) has recently generated significant interest, particularly in the area of approximate explicit MPC, where function approximators like deep neural networks are used to learn the MPC policy via optimal state-action pairs generated offline. While the aim of approximate explicit MPC is to closely replicate the MPC policy, substituting online optimization with a trained neural network, the performance guarantees that come with solving the online optimization problem are typically lost. This paper considers an alternative strategy, where supervised learning is used to learn the optimal value function offline instead of learning the optimal policy. This can then be used as the cost-to-go function in a myopic MPC with a very short prediction horizon, such that the online computation burden reduces significantly without affecting the controller performance. This approach differs from existing work on value function approximations in the sense that it learns the cost-to-go function by using offline-collected state-value pairs, rather than closed-loop performance data. The cost of generating the state-value pairs used for training is addressed using a sensitivity-based data augmentation scheme.