# Deep Model Predictive Control

## TL;DR

This paper introduces a deep learning-based model predictive control method for nonlinear systems with unknown uncertainties, using neural networks for disturbance approximation and a tube-based controller for stability and constraint satisfaction.

## Contribution

It proposes a novel integration of deep neural networks with tube-based MPC to handle unknown, state-dependent uncertainties in nonlinear systems.

## Key findings

- Neural networks effectively approximate unknown disturbances.
- The combined approach guarantees constraint satisfaction.
- Closed-loop stability is maintained during learning.

## Abstract

This paper presents a deep learning based model predictive control algorithm for control affine nonlinear discrete time systems with matched and bounded state-dependent uncertainties of unknown structure. Since the structure of uncertainties is not known, a deep neural network (DNN) is employed to approximate the disturbances. In order to avoid any unwanted behavior during the learning phase, a tube based model predictive controller is employed, which ensures satisfaction of constraints and input-to-state stability of the closed-loop states.