Vehicle velocity estimation for real-time dynamic stability control

Zhang, Lipeng; Song, Jing; Kong, Liang; Huang, Qin

doi:10.1007/s12239-009-0080-7

Cited by 21 publications

(16 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The model considered for the MPC formulation is further extended including the longitudinal vehicle velocity in the state vector. This in fact becomes [23] where a cascade of observers system is employed for longitudinal and lateral velocity estimation. In the current study, longitudinal velocity is assumed to be obtained either from an estimation process or measured from a GPS unit directly, that provides an acceptable accuracy for the proposed implementation.…”

Section: Filter Designmentioning

confidence: 99%

Feedback brake distribution control for minimum pitch

Tavernini

Velenis

Longo

2017

Vehicle System Dynamics

View full text Add to dashboard Cite

The distribution of brake forces between front and rear axles of a vehicle is typically specified such that the same level of brake force coefficient is imposed at both front and rear wheels. This condition is known as ‘ideal’ distribution and it is required to deliver the maximum vehicle deceleration and minimum braking distance. For subcritical braking conditions, the deceleration demand may be delivered by different distributions between front and rear brak- ing forces. In this research we show how to obtain the optimal distribution which minimises the pitch angle of a vehicle and hence enhances driver subjective feel during braking. A vehi- cle model including suspension geometry features is adopted. The problem of the minimum pitch brake distribution for a varying deceleration level demand is solved by means of a model predictive control technique. To address the problem of the undesirable pitch rebound caused by a full-stop of the vehicle, a second controller is designed and implemented independently from the braking distribution in use. An extended Kalman filter is designed for state esti- mation and implemented in a high fidelity environment together with the model predictive control strategy. The proposed solution is compared with the reference ‘ideal’ distribution as well as another previous feed-forward solution

show abstract

Section: Filter Designmentioning

confidence: 99%

Feedback brake distribution control for minimum pitch

Tavernini

Velenis

Longo

2017

Vehicle System Dynamics

View full text Add to dashboard Cite

show abstract

“…These assumptions are reasonable because the estimation of the longitudinal velocity, lateral velocity, longitudinal slip ratio, friction coefficient and lateral slip angle of the vehicle has been done previously. [34][35][36][37][38] The actual vehicle yaw rate can be measured by inertial measurement unit (IMU).…”

Section: Sliding Mode Controller Designmentioning

confidence: 99%

Fault-tolerant control of electric vehicles with in-wheel motors using actuator-grouping sliding mode controllers

2016

Mechanical Systems and Signal Processing

View full text Add to dashboard Cite

Although electric vehicles with in-wheel motors have been regarded as one of the promising vehicle architectures in recent years, the probability of in-wheel motor fault is still a crucial issue due to the system complexity and large number of control actuators. In this study, a modified sliding mode control (SMC) is applied to achieve fault-tolerant control of electric vehicles with four-wheel-independent-steering (4WIS) and four-wheel-independent-driving (4WID). Unlike in traditional SMC, in this approach the steering geometry is re-arranged according to the location of faulty wheels in the modified SMC. Three SMC control laws for longitudinal velocity control, lateral velocity control and yaw rate control are designed based on specific vehicle motion scenarios. In addition the actuator-grouping SMC method is proposed so that driving actuators are grouped and each group of actuators can be used to achieve the specific control target, which avoids the strong coupling effect between each control target. Simulation results prove that the proposed modified SMC can achieve good vehicle dynamics control performance in normal driving and large steering angle turning scenarios. In addition, the proposed actuator-grouping SMC can solve the coupling effect of different control targets and the control performance is improved. Abstract:Although electric vehicles with in-wheel motors have been regarded as one of the promising vehicle architectures in recent years, the probability of in-wheel motor fault is still a crucial issue due to the system complexity and large number of control actuators. In this study, a modified sliding mode control (SMC) is applied to achieve fault-tolerant control of electric vehicles with four-wheel-independent-steering (4WIS) and four-wheel-independent-driving (4WID). Unlike in traditional SMC, in this approach the steering geometry is re-arranged according to the location of faulty wheels in the modified SMC. Three SMC control laws for longitudinal velocity control, lateral velocity control and yaw rate control are designed based on specific vehicle motion scenarios. In addition the actuator-grouping SMC method is proposed so that driving actuators are grouped and each group of actuators can be used to achieve the specific control target, which avoids the strong coupling effect between each control target. Simulation results prove that the proposed modified SMC can achieve good vehicle dynamics control performance in normal driving and large steering angle turning scenarios. In addition, the proposed actuator-grouping SMC can solve the coupling effect of different control targets and the control performance is improved.

show abstract

“…Hence the reference velocity could be obtained using the wheel speed sensors [28,29] in the flow chat shown in Fig. 5.…”

Section: Establishment Of Reference Velocitymentioning

confidence: 99%