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Autonomous Vehicle Tracking Based on Non-Linear Model Predictive Control Approach

Autonomous Vehicle Tracking Based on Non-Linear Model Predictive Control Approach
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Author(s): Trieu Minh Vu (Institute for Nanomaterials, Advanced Technologies and Innovation, Technical University of Liberec, Czech Republic), Reza Moezzi (Institute for Nanomaterials, Advanced Technologies and Innovation, Technical University of Liberec, Czech Republic), Jindrich Cyrus (Institute for Nanomaterials, Advanced Technologies and Innovation, Technical University of Liberec, Czech Republic), Jaroslav Hlava (Faculty of Mechatronics, Informatics and Interdisciplinary Studies, Technical University of Liberec, Czech Republic) and Michal Petru (Institute for Nanomaterials, Advanced Technologies and Innovation, Technical University of Liberec, Czech Republic)
Copyright: 2022
Pages: 58
Source title: Applications of Computational Science in Artificial Intelligence
Source Author(s)/Editor(s): Anand Nayyar (Duy Tan University, Da Nang, Vietnam), Sandeep Kumar (CHRIST University (Deemed), Bangalore, India) and Akshat Agrawal (Amity University, Guragon, India)
DOI: 10.4018/978-1-7998-9012-6.ch005

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Abstract

Autonomous driving vehicles are developing rapidly; however, the control systems for autonomous driving vehicles tracking smoothly in high speed are still challenging. This chapter develops non-linear model predictive control (NMPC) schemes for controlling autonomous driving vehicles tracking on feasible trajectories. The optimal control action for vehicle speed and steering velocity is generated online using NMPC optimizer subject to vehicle dynamic and physical constraints as well as the surrounding obstacles and the environmental side-slipping conditions. NMPC subject to softened state constraints provides a better possibility for the optimizer to generate a feasible solution as real-time subject to online dynamic constraints and to maintain the vehicle stability. Different parameters of NMPC are simulated and analysed to see the relationships between the NMPC horizon prediction length and the weighting values. Results show that the NMPC can control the vehicle tracking exactly on different trajectories with minimum tracking errors and with high comfortability.

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