Special Issue: ICCAS 2024

International Journal of Control, Automation, and Systems 2025; 23(2): 510-519

https://doi.org/10.1007/s12555-024-0475-2

© The International Journal of Control, Automation, and Systems

Deep Neural Network-based Approximation of Nonlinear Model Predictive Control: Applications to Truck-trailer Control System

Suyong Park, Duc Giap Nguyen, Yongsik Jin, Jinrak Park, Dohee Kim, Jeong Soo Eo, and Kyoungseok Han*

Hanyang University

Abstract

In this work, we demonstrate the efficiency of approximating nonlinear model predictive control (NMPC) using deep neural networks (DNN). We design an implicit NMPC for forward and backward motions of the truck trailer (TT) to handle complexity of nonlinear system dynamics. However, the high computational load of implicit MPC poses challenges for real-time implementation. To address this issue, we employ a DNN-based NMPC approximation to estimate parametric functions. As a result, the DNN-based NMPC approximation can mimic the optimal control policy of implicit MPC. Additionally, the average computation times for implicit NMPC and the DNN-based NMPC approximation in hardware-in-the-loop (HIL) tests are 36.541 ms and 0.031 ms, respectively.

Keywords Approximation, deep neural network, hardware-in-the-loop, nonlinear model predictive control, trucktrailer system.

Article

Special Issue: ICCAS 2024

International Journal of Control, Automation, and Systems 2025; 23(2): 510-519

Published online February 1, 2025 https://doi.org/10.1007/s12555-024-0475-2

Copyright © The International Journal of Control, Automation, and Systems.

Deep Neural Network-based Approximation of Nonlinear Model Predictive Control: Applications to Truck-trailer Control System

Suyong Park, Duc Giap Nguyen, Yongsik Jin, Jinrak Park, Dohee Kim, Jeong Soo Eo, and Kyoungseok Han*

Hanyang University

Abstract

In this work, we demonstrate the efficiency of approximating nonlinear model predictive control (NMPC) using deep neural networks (DNN). We design an implicit NMPC for forward and backward motions of the truck trailer (TT) to handle complexity of nonlinear system dynamics. However, the high computational load of implicit MPC poses challenges for real-time implementation. To address this issue, we employ a DNN-based NMPC approximation to estimate parametric functions. As a result, the DNN-based NMPC approximation can mimic the optimal control policy of implicit MPC. Additionally, the average computation times for implicit NMPC and the DNN-based NMPC approximation in hardware-in-the-loop (HIL) tests are 36.541 ms and 0.031 ms, respectively.

Keywords: Approximation, deep neural network, hardware-in-the-loop, nonlinear model predictive control, trucktrailer system.

IJCAS
February 2025

Vol. 23, No. 2, pp. 359~682

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eISSN 2005-4092
pISSN 1598-6446