TY - GEN
T1 - Nonlinear Model Predictive Control Approximation
T2 - 24th International Conference on Control, Automation and Systems, ICCAS 2024
AU - Park, Suyong
AU - Nguyen, Duc Giap
AU - Jin, Yongsik
AU - Park, Jinrak
AU - Kim, Dohee
AU - Eo, Jeong Soo
AU - Han, Kyoungseok
N1 - Publisher Copyright:
© 2024 ICROS.
PY - 2024
Y1 - 2024
N2 - In this work, we demonstrate the effectiveness of nonlinear model predictive control (NMPC) approximation based on deep neural network (DNN). MPC has been widely adopted in autonomous driving control problems to handle multiple objectives and constraints. We first design the implicit NMPC for the forward and backward motions of a truck-trailer (TT) system, which follows the reference path while maintaining safety between the head truck (HT) and the trailer (TR). However, the computational load in implicit MPC makes it a challenge for real-time implementations. To alleviate the computational burden in implicit NMPC online, an NMPC approximation approach based on DNN is adopted in this study to achieve a parametric function approximation. We conduct a comparative study on the proposed approach and a baseline controller for control performance analysis, and the computational load is evaluated on a hardware-in-the-loop (HIL) experimental system.
AB - In this work, we demonstrate the effectiveness of nonlinear model predictive control (NMPC) approximation based on deep neural network (DNN). MPC has been widely adopted in autonomous driving control problems to handle multiple objectives and constraints. We first design the implicit NMPC for the forward and backward motions of a truck-trailer (TT) system, which follows the reference path while maintaining safety between the head truck (HT) and the trailer (TR). However, the computational load in implicit MPC makes it a challenge for real-time implementations. To alleviate the computational burden in implicit NMPC online, an NMPC approximation approach based on DNN is adopted in this study to achieve a parametric function approximation. We conduct a comparative study on the proposed approach and a baseline controller for control performance analysis, and the computational load is evaluated on a hardware-in-the-loop (HIL) experimental system.
KW - control policy approximation
KW - Deep neural network
KW - hardware-in-the-loop systems
KW - nonlinear model predictive control
KW - truck-trailer system
UR - http://www.scopus.com/inward/record.url?scp=85214406784&partnerID=8YFLogxK
U2 - 10.23919/ICCAS63016.2024.10773199
DO - 10.23919/ICCAS63016.2024.10773199
M3 - Conference contribution
AN - SCOPUS:85214406784
T3 - International Conference on Control, Automation and Systems
SP - 933
EP - 938
BT - 2024 24th International Conference on Control, Automation and Systems, ICCAS 2024
PB - IEEE Computer Society
Y2 - 29 October 2024 through 1 November 2024
ER -