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Volume 10
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Article

Performance Analysis of Deep Neural Network Controller for Autonomous Driving Learning from a Nonlinear Model Predictive Control Method

by
Taekgyu Lee
and
Yeonsik Kang
*
Graduate School of Automotive Engineering, Kookmin University, Seoul 02707, Korea
*
Author to whom correspondence should be addressed.
Electronics 2021, 10(7), 767; https://doi.org/10.3390/electronics10070767
Submission received: 10 February 2021 / Revised: 11 March 2021 / Accepted: 18 March 2021 / Published: 24 March 2021
(This article belongs to the Special Issue Real-Time Control of Embedded Systems)
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Abstract

Nonlinear model predictive control (NMPC) is based on a numerical optimization method considering the target system dynamics as constraints. This optimization process requires large amount of computation power and the computation time is often unpredictable which may cause the control update rate to overrun. Therefore, the performance must be carefully balanced against the computational time. To solve the computation problem, we propose a data-based control technique based on a deep neural network (DNN). The DNN is trained with closed-loop driving data of an NMPC. The proposed "DNN control technique based on NMPC driving data" achieves control characteristics comparable to those of a well-tuned NMPC within a reasonable computation period, which is verified with an experimental scaled-car platform and realistic numerical simulations.
Keywords: data-driven control; model predictive control; artificial neural network; autonomous driving; deep neural network control; artificial intelligence data-driven control; model predictive control; artificial neural network; autonomous driving; deep neural network control; artificial intelligence

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MDPI and ACS Style

Lee, T.; Kang, Y. Performance Analysis of Deep Neural Network Controller for Autonomous Driving Learning from a Nonlinear Model Predictive Control Method. Electronics 2021, 10, 767. https://doi.org/10.3390/electronics10070767

AMA Style

Lee T, Kang Y. Performance Analysis of Deep Neural Network Controller for Autonomous Driving Learning from a Nonlinear Model Predictive Control Method. Electronics. 2021; 10(7):767. https://doi.org/10.3390/electronics10070767

Chicago/Turabian Style

Lee, Taekgyu, and Yeonsik Kang. 2021. "Performance Analysis of Deep Neural Network Controller for Autonomous Driving Learning from a Nonlinear Model Predictive Control Method" Electronics 10, no. 7: 767. https://doi.org/10.3390/electronics10070767

APA Style

Lee, T., & Kang, Y. (2021). Performance Analysis of Deep Neural Network Controller for Autonomous Driving Learning from a Nonlinear Model Predictive Control Method. Electronics, 10(7), 767. https://doi.org/10.3390/electronics10070767

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