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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F46747885%3A24220%2F22%3A00010092" target="_blank" >RIV/46747885:24220/22:00010092 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/46747885:24620/22:00010092

Výsledek na webu

<a href="https://www.igi-global.com/chapter/autonomous-vehicle-tracking-based-on-non-linear-model-predictive-control-approach/302063" target="_blank" >https://www.igi-global.com/chapter/autonomous-vehicle-tracking-based-on-non-linear-model-predictive-control-approach/302063</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.4018/978-1-7998-9012-6.ch005" target="_blank" >10.4018/978-1-7998-9012-6.ch005</a>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

C - Kapitola v odborné knize

CEP obor

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OECD FORD obor

10200 - Computer and information sciences

Projekt

<a href="/cs/project/EF16_025%2F0007293" target="_blank" >EF16_025/0007293: Modulární platforma pro autonomní podvozky specializovaných elektrovozidel pro dopravu nákladu a zařízení</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název knihy nebo sborníku

Applications of Computational Science in Artificial Intelligence

ISBN

978-1799890140

Počet stran výsledku

58

Strana od-do

74-131

Počet stran knihy

284

Název nakladatele

IGI Global

Místo vydání

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Kód UT WoS kapitoly

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