Predictive Active Steering Control for Autonomous Vehicle Systems

Falcone, Paolo; Borrelli, Francesco; Asgari, Jahan; Tseng, H. Eric; Hrovat, Davor

doi:10.1109/tcst.2007.894653

Cited by 1,141 publications

(627 citation statements)

References 22 publications

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“…Since input and output variables, δ , δ , , and are available, it is possible to identify all the models in (1) and (2). The steering wheel actuator model ( ) a G s is a feedback system with a servo-motor controller, which will be explained in the subsection 3.3.1.…”

Section: Linear Model Structures Of Steering Systemmentioning

confidence: 99%

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Empirical Modeling of Steering System for Autonomous Vehicles

Kim¹,

Min²,

Kim³

2017

Journal of Electrical Engineering and Technology

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-To design an automatic steering controller with high performance for autonomous vehicle, it is necessary to have a precise model of the lateral dynamics with respect to the steering command input. This paper presents an empirical modeling of the steering system for an autonomous vehicle. The steering system here is represented by three individual transfer function models: a steering wheel actuator model from the steering command input to the steering angle of the shaft, a dynamic model between the steering angle and the yaw rate of the vehicle, and a dynamic model between the steering command and the lateral deviation of vehicle. These models are identified using frequency response data. Experiments were performed using a real vehicle. It is shown that the resulting identified models have been well fitted to the experimental data.

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Section: Linear Model Structures Of Steering Systemmentioning

confidence: 99%

“…Automatic steering control of vehicles has been investigated for both autonomous vehicles (AV) [1][2][3] and driver steering assistance [4,5]. Driver steering assistance systems under development include collision avoidance systems, adaptive cruise control, and lane departure avoidance systems.…”

Section: Introductionmentioning

confidence: 99%

Empirical Modeling of Steering System for Autonomous Vehicles

Kim¹,

Min²,

Kim³

2017

Journal of Electrical Engineering and Technology

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“…In the presence of obstacles, the controller deviates from the reference trajectory by incorporating obstacle-distance information from range sensors into the optimization problem. Falcone et al [27] presented two approaches with different computational complexities for controlling an active front steering system in an autonomous vehicle. In the first approach, the MPC problem is formulated by using a nonlinear vehicle model.…”

Section: The Virtual Structure Strategy With Mpcmentioning

confidence: 99%

Coordinated Path Following for Mobile Robots Using a Virtual Structure Strategy with Model Predictive Control

Kanjanawanishkul

2014

Automatika

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In this paper, we propose a novel coordinated path following controller based on model predictive control (MPC) for mobile robots. The strategy is based on a virtual structure approach where the entire formation is considered as a rigid body and the control laws for a virtual leader vehicle and for actual follower robots are optimized by considering the dynamics of the virtual structure and the desired motion of each vehicle. Besides, we also fulfill time convergence for trajectory tracking by integrating an additional penalty term into our model predictive control scheme. However, the major concern in the use of model predictive control is whether such an open-loop control scheme can guarantee system stability. In this case, we apply the idea of a contractive constraint to guarantee the stability of our MPC framework. Although our approach is centralized, numerous simulation scenarios have been conducted to illustrate its effectiveness and its superior performance for a small group of mobile robots. Furthermore, we show that path following control can offer a number of advantages over its trajectory tracking counterpart. Koordinirano slije enje putanje za mobilne robote zasnovano na strategiji virtualne strukture i modelskom prediktivnom upravljanju. U ovomečlanku predlaže se novi algoritam upravljanja koordiniranim slije enjem putanje za mobilne robote zasnovan na modelskom prediktivnom upravljanju. Strategija se zasniva na pristupu s virtualnom strukturom gdje se cijela formacija robota smatra krutim tijelom, dok se za virtualno vodeće vozilo i za slijedeće robote optimiziraju zakoni upravljanja vodeći računa o dinamici virtualne strukture i željenom gibanju svakog od vozila. Tako er, algoritam ispunjava uvjet vremena konvergencije radi praćenja trajektorija na način da integrira dodataničlan unutar algoritma modelskog prediktivnog upravljanja. Me utim, koristeći modelsko prediktivno upravljanje postavlja se pitanje može li ovakav pristup u otvorenom upravljačkom krugu jamčiti stabilnost. Radi toga, primijenjuje se ideja sužavajućeg ograničenja radi jamčenja stabilnosti predloženog riješenja. Iako je predloženi pristup centraliziran, provedeni su brojni simulacijski eksperimenti kako bi se ilustrirala učinkovitost i superiorno vladanje na primjeru male grupe mobilnih robota. Nadalje, pokazuje se da upravljanje slije enjem putanje pruža brojne prednosti u usporedbi s praćenjem trajektorije.Ključne riječi: upravljanje koordiniranim slije enjem putanje, modelsko prediktivno upravljanje, mobilni roboti, virtualna struktura, upravljanje gibanjem robota

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“…Most control techniques used in the previous studies fall into the first category. Examples include nonlinear predictive control (Falcone et al, 2007), random sub-optimal control (Chen et al, 2006), robust H  (Hirano et al, 1993), sliding mode , and artificial neural networks (Nwagboso et al, 2002), etc. In contrast, hierarchical control has not yet been applied extensively to integrated vehicle control system.…”

Section: Introductionmentioning

confidence: 99%