Adaptive Automotive Speed Control

Liubakka, M.K.; Winkelman, J.; Kokotović, P.V.

doi:10.23919/acc.1991.4791405

Cited by 8 publications

(9 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Certain vehicle or road parameters, like rolling resistance, road inclination or friction coefficient, could change during a single trip. Examples of adaptive controller for vehicle control applications can be found in works by [Liubakka et al, 1993], [Ioannou and Xu, 1994], [Oda et al, 1991] or [Youcef-Toumi et al, 1992].…”

Section: Generalitiesmentioning

confidence: 99%

Robust stop-and-go control strategy: an algebraic approach for non-linear estimation and control

Villagrá

d’Andréa-Novel

Choi

et al. 2009

IJVAS

View full text Add to dashboard Cite

This paper describes a robust stop-and-go control strategy for vehicles. Since sensors used in a real automotive context are generally low cost, measurements are quite noisy. Furthermore, many vehicle/road interaction factors (road slope, rolling resistance, aerodynamic forces) are very poorly known. Hence, a robust strategy to noise and parameters is proposed within the same theoretical framework: algebraic nonlinear estimation and control techniques. On the one hand, noisy signals will be processed in order to obtain accurate derivatives, and thereafter, variable estimates. On the other hand, a grey-box closedloop control will be implemented to reject all kind of disturbances caused by exogenous parameter uncertainties.

show abstract

Section: Generalitiesmentioning

confidence: 99%

Robust stop-and-go control strategy: an algebraic approach for non-linear estimation and control

Villagrá

d’Andréa-Novel

Choi

et al. 2009

IJVAS

View full text Add to dashboard Cite

show abstract

“…In order to design the switching logic, set the throttle opening to 0 at different speeds to get the maximum deceleration value of vehicle on this condition, then draw the maximum deceleration curve. In order to ensure comfort and reduce frequent switching, the switching logic curve is shifted up and down by 0.01 m/s 2 to form a transition zone. As shown in Figure 3, the driving control area is above the transition area, and the braking control area is below the transition area.…”

Section: Mfasmc Control Scheme For Vehicle Longitudinal Dynamicsmentioning

confidence: 99%

Model-free adaptive sliding mode control for intelligent vehicle longitudinal dynamics

Zhang-qi

Jiang

Wei

et al. 2022

Advances in Mechanical Engineering

View full text Add to dashboard Cite

Vehicle longitudinal dynamics system has the characteristics of being strongly non-linear, time-varying, and multiple-perturbed, so, it is difficult to build the mathematical model accurately. The control algorithms, based on accurate mathematical model, can hardly achieve the ideal effect, but control methods, which merely adopt input/output data (I/O) of a system, provides a solution. In this paper, by means of combing model-free adaptive control (MFAC) and sliding-mode control (SMC), the model-free adaptive sliding mode control (MFASMC) method is proposed. By comparison with feedback-feedforward control method, the MFASMC method can better improve the control effect and anti-disturbance performance. Meanwhile, the stability of MFASMC method was proven mathematically. Besides, the parameters of MFASMC method were optimized using genetic algorithm. Results of simulation and HiL test shows that the MFASMC method has fast response, strong robustness and smooth output. It would be better to apply it to the longitudinal dynamics control of intelligent vehicles.

show abstract

“…Therefore it is necessary to use an adaptive control approach with an implicit or explicit online estimation scheme for better control performance. Examples of adaptive controllers for vehicle control applications can be found in the work by Liubakka et al [57], Ioannou et al [58], Oda et al [59]. For longitudinal control of HDV, Yanakiev et al [60], [61] have proposed an adaptive controller with direct adaptation of PIQ controller gains.…”

Section: B Vehicle-level Controllersmentioning

confidence: 99%

Research advances in intelligent collision avoidance and adaptive cruise control

Vahidi

Eskandarian

2003

IEEE Trans. Intell. Transport. Syst.

717

315

View full text Add to dashboard Cite

This paper looks into recent developments and research trends in collision avoidance/warning systems and automation of vehicle longitudinal/lateral control tasks. It is an attempt to provide a bigger picture of the very diverse, detailed and highly multidisciplinary research in this area. Based on diversely selected research, this paper explains the initiatives for automation in different levels of transportation system with a specific emphasis on the vehicle-level automation. Human factor studies and legal issues are analyzed as well as control algorithms. Drivers' comfort and well being, increased safety, and increased highway capacity are among the most important initiatives counted for automation. However, sometimes these are contradictory requirements. Relying on an analytical survey of the published research, we will try to provide a more clear understanding of the impact of automation/warning systems on each of the above-mentioned factors. The discussion of sensory issues requires a dedicated paper due to its broad range and is not addressed in this paper.

show abstract

Adaptive Automotive Speed Control

Cited by 8 publications

References 2 publications

Robust stop-and-go control strategy: an algebraic approach for non-linear estimation and control

Robust stop-and-go control strategy: an algebraic approach for non-linear estimation and control

Model-free adaptive sliding mode control for intelligent vehicle longitudinal dynamics

Research advances in intelligent collision avoidance and adaptive cruise control

Contact Info

Product

Resources

About