Composite Lyapunov based vehicle longitudinal control assistance

Enache, Nicoleta Minoiu; Mammar, Saïd; Glaser, Sébastien; Lusetti, Benoît; Nouvelière, Lydie

doi:10.23919/ecc.2009.7075067

Cited by 5 publications

(4 citation statements)

References 13 publications

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“…This requires a second zone model. The second zone model is inspired on algorithms in control theory [EMGL09], in Equation 3.5. Variables without superscript relate to the subject, variables with superscript O to the object being followed.…”

Section: Trajectoriesmentioning

confidence: 99%

“…Longitudinal control for Cruise Control (CC) and Adaptive Cruise Control (ACC) applications is based on a variety of control strategies, e.g. PID control[HGCV11], sliding mode control[NM07] and Lyapunov functions based control[EMGL09].Table 2.8 presents the trajectory description. For longitudinal control, the speed profile specifies speed v[i] in function of time t[i].…”

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confidence: 99%

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Highly Automated Driving on Highways Based on Legal Safety

Vanholme¹,

Gruyer²,

Lusetti³

et al. 2013

IEEE Trans. Intell. Transport. Syst.

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122

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Vehicle automation is proposed as one of the solutions to make transport safer, more comfortable and more environmentally friendly. It is gradually being introduced through Advanced Driver Assistance Systems (ADAS). ADAS started as Anti-lock Braking Systems (ABS) and have now extended to Adaptive Cruise Control (ACC) and Lane Keeping Assist Systems (LKAS). This work aims to contribute to this evolution, by discussing how driving systems can share the road with human drivers. It presents the legal safety concept for the design of a highly automated driving system for highways. The legal safety concept proposes to base driving system design on traffic rules. This allows fully automated driving in traffic with human drivers, without necessarily changing equipment on other vehicles or infrastructure. The driving system uses traffic rules to predict legal or nonlegal trajectories of objects in its perception zone and worst-case objects outside its perception zone. If all objects respect traffic rules, accidents will be avoided. If not, driving defensively will avoid most accidents. Today, international law allows highly automated driving, but not yet fully automated driving. The driving system interacts with the human driver, via human rules. The HAVEit project (European Seventh Framework Programme) and ABV project (French National Research Agency) propose human rules based on the horse-rider metaphor (Hmetaphor). If needed, the driving system takes over control in order to avoid accidents. If the human driver cannot continue driving, the driving system brings the vehicle to a safe standstill on the emergency lane. The consequences of these human rules on driving system design are explored. System rules form the third set of rules of the legal safety concept. With system rules, system components respect the limitations of other system components. The requirements on perception, control and Human-Machine Interface (HMI) components of the legal safety system are discussed. The decision component, which is the central component of the legal safety system, is completely worked out from requirements to design. The legal safety system has been implemented on PC and automotive Electronic Control Units (ECUs). The integration and validation of legal safety components on LIVIC, HAVEit and ABV demonstrators are presented. The work concludes that, for highway environments, legal safety decision, control and HMI can be achieved with state-of-the-art technology, and legal safety perception could be available in medium term.

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Section: Trajectoriesmentioning

confidence: 99%

mentioning

confidence: 99%

Highly Automated Driving on Highways Based on Legal Safety

Vanholme¹,

Gruyer²,

Lusetti³

et al. 2013

IEEE Trans. Intell. Transport. Syst.

Self Cite

122

View full text Add to dashboard Cite

show abstract

“…The closedloop system compensates for the unmodeled dynamics and external disturbances. Enache et al [2009] have chosen to use a specific double integrator vehicle following model for that purpose of cruise control. For that, a composite Lyapunov function-based vehicle following control law using Bilinear Matrix Inequalities (BMI) optimization, has been elaborated.…”

Section: Introductionmentioning

confidence: 99%

LPV-based Control Design of an Adaptive Cruise Control System for Road Vehicles

et al. 2015

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The paper presents a robust cruise control design method for a test vehicle, which is able to guarantee velocity tracking performance. The longitudinal dynamics and the consideration of model uncertainties such as parameters variation and actuator dynamics, are formulated in the vehicle model. The proposed controller has a feedforward term, which reduces the conservativeness of the control system during the compensation of the measured disturbances. The feedback term is designed using robust Linear Parameter-Varying methods, in which the inaccuracy of the feedforward control is considered. The purpose of the feedback control is to guarantee the robust performance and to handle the actuator saturation. The efficiency of the method is presented through simulation scenarios.

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“…Afterwards, the designed control law has to be tested on its robustness against speed variations. The lateral vehicle control has been investigated by (Li et al, 2005), (Minoiu et al, 2009), the longitudinal control makes the object of the works (Mammar and Yacine, 2012), (Enache et al, 2009) and the yaw rate control is treated in (Minoiu et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Trajectory Tracking Control by LMI-based Approach for Car-like Robots

Enache¹

2012

Proceedings of the 9th International Conference on Informatics in Control, Automation and Robotics

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A lot of research has been done concerning motion planning and trajectory tracking control for robots, including car-like robots. Nevertheless, most of the methods require very well defined trajectories, continuous and several times derivable. Frequently, the system has to be written in a specific form, like the chained form, and the path obtained in the original space may not satisfy additional constraints in the original space or singularities can occur in the control law. The goal of this work is to investigate a control method to do the trajectory tracking control of car-like robots in the original space, which does not require trajectories several times derivable, but with good robustness and pursuit properties in order to implement it on a passenger vehicle. The control law for trajectory tracking presented here is derived from a method developed for unicycles. This is based on a real time combination of static linear feedbacks that are obtained by an off line LMI (Linear Matrix Inequalities) approach.

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Composite Lyapunov based vehicle longitudinal control assistance

Cited by 5 publications

References 13 publications

Highly Automated Driving on Highways Based on Legal Safety

Highly Automated Driving on Highways Based on Legal Safety

LPV-based Control Design of an Adaptive Cruise Control System for Road Vehicles

Trajectory Tracking Control by LMI-based Approach for Car-like Robots

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