A Real-Time Nonlinear Model Predictive Control Strategy for Stabilization of an Electric Vehicle at the Limits of Handling

Siampis, Efstathios; Velenis, Efstathios; Gariuolo, Salvatore; Longo, Stefano

doi:10.1109/tcst.2017.2753169

Cited by 90 publications

(32 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They are incorporated into the lateral dynamics to estimate vehicle states and analyze the vehicle stability on different roads (see refs. ). Tire curves are represented by three regions including linear, transient, and nonlinear defined by road friction coefficient, normal forces, and cornering stiffness.…”

Section: Tire Models and Force Estimationmentioning

confidence: 97%

Tire Condition Monitoring and Intelligent Tires Using Nanogenerators Based on Piezoelectric, Electromagnetic, and Triboelectric Effects

Argani

Hashemi

Khajepour

et al. 2018

Adv Materials Technologies

View full text Add to dashboard Cite

With the prospect of autonomous driving on roads in near future, it is paramount to make the vehicles safe on any driving and road condition. This is only possible by additional sensors to make up for the driver's cognitive and sensory system. Measuring road condition and tire forces especially in autonomous vehicles are vital in their safety, reliability, and public confidence in automated driving. Real time measurement of road condition and tire forces in buses and trucks can significantly improve the safety of road transportation system, and in miming/construction and off-road vehicles can improve performance, tire life, and reduce operational costs.There are three particular branches of research in the area of tire condition monitoring systems (TCMS) including estimation, sensing, and power harvesting. The main focus of this review article would be on progress in estimation techniques, energy harvesting, and also sensing approaches for TCMS based on piezoelectric, electromagnetic, and triboelectric nanogenerators (TENGs). Indeed, the fusion of these three areas in a singular package leads to designing future intelligent tires. The implementation of intelligent tires is highly advantageous for reduction of disastrous accidents,CO 2 , and noise emissions, and also fuel consumption. Figure 1 depicts an intelligent tire with its basic parts, its crucial applications, and advantages. In Figure 1, NGs, PEGs, and EMGs refer to nanogenerators, piezoelectric nanogenerators, and electromagnetic generators, respectively.Advances in applications of sensor technologies, sensor fusion, and cooperative estimation in intelligent transportation systems facilitate having reliable tire forces. Consequently, developing a flexible estimation structure to operate the available sensor data in production vehicles is a preeminent objective of the car manufacturers. In this direction, reliable estimation of tire forces at a reasonable cost is necessary for proper functioning of active safety systems in current vehicles. Longitudinal, lateral, and vertical tire forces make major contributions to velocity estimation, [3] traction and stability control, [4,5] and obstacle avoidance systems. [6,7] This article provides a comprehensive review on intelligent tire and TCMS. The first part of the article describes tire models, techniques for tire force, and tire-road friction The quest to utilize intelligent tires has prompted substantial multidisciplinary research including vehicle dynamics, control, estimation, energy harvesting, and even nanotechnology. This review article presents the progress in the area of tire condition monitoring systems (TCMS) and intelligent tires using devices fabricated based on piezoelectric, electromagnetic, and triboelectric effects. Three main branches of the research in this area are presented, including estimation techniques, sensing, and energy harvesting approaches. The authors delineate the importance of TCMS for vehicle active safety systems, its importance for transportation safety, and also its remarka...

show abstract

Section: Tire Models and Force Estimationmentioning

confidence: 97%

Tire Condition Monitoring and Intelligent Tires Using Nanogenerators Based on Piezoelectric, Electromagnetic, and Triboelectric Effects

Argani

Hashemi

Khajepour

et al. 2018

Adv Materials Technologies

View full text Add to dashboard Cite

show abstract

“…The authors claimed that simulation was time-consuming and tuning was difficult. A recent study [12] claimed to have achieved real-time NMPC control of torque-vectoring using a commercial toolbox. The numerical quality is not satisfactory, though, as the final cost could be 28% higher than the actual optimum in some cases.…”

Section: -2 Model Predictive Control Of Vehicle Dynamicsmentioning

confidence: 99%

A Real-Time Nonlinear MPC for Extreme Lateral Stabilization of Passenger Vehicles

Zheng

Shyrokau

2019

2019 IEEE International Conference on Mechatronics (ICM)

View full text Add to dashboard Cite

Loss of lateral stability is still a major cause of road accidents in recent years. Nonlinear model predictive control (NMPC) is regarded as a powerful tool to improve vehicle safety by fully utilizing the tire-road friction. However, the computational load is excessive for on-board hardware, thus NMPC is yet distant from real-time implementation in vehicle control. To tackle the problem, this study proposes a method to improve the computational efficiency in NMPC. First, a lookup table of initial guess of the NMPC solution is calculated based on a piecewise-affine (PWA) approximation of tire's nonlinear behavior. Then, a local optimization process starting from the initial guess searches for the optimal control input, using perpendicular search plus line search method. The proposed method was examined through a set of numerical tests and in real-world scenarios using experimentally calibrated multibody model. The code was also tested on dSPACE for proof of real-time implementation. Results show a significant reduction in computational time, thus real-time implementation has been achieved with a huge margin. The loss in the accuracy of the optimal solution is negligible. The performance in improving vehicle safety is promising too, as the vehicle can be recovered from unstable motion with body slip angle up to 30 degrees.

show abstract

“…The experimental results in [10] show stabilization only up to even lower speeds, due to a lack of convergence in the implementation on a rapid control prototyping (RCP) unit. Siampis et al [11] applied two real-time optimization methods to an NMPC for stability control while assessing computational complexity on an RCP unit. The NMPC outperforms a linear time-varying (LTV) MPC, both of them employing a double-track model considering load transfer effects.…”

Section: Introductionmentioning

confidence: 99%

On Prediction Model Fidelity in Explicit Nonlinear Model Predictive Vehicle Stability Control

Metzler¹,

Tavernini

Gruber

et al. 2021

IEEE Trans. Contr. Syst. Technol.

View full text Add to dashboard Cite

This study discusses vehicle stability control based on explicit nonlinear model predictive control (NMPC) and investigates the influence of prediction model fidelity on controller performance. The explicit solutions are generated through an algorithm using multiparametric quadratic programming (mp-QP) approximations of the multiparametric nonlinear programming (mp-NLP) problems. Controllers with different prediction models are assessed through objective indicators in sine-with-dwell tests. The analysis considers the following prediction model features: 1) nonlinear lateral tire forces as functions of slip angles, which are essential for the operation of the stability controller at the limit of handling. Moreover, a simple nonlinear tire force model with saturation is shown to be an effective alternative to a more complex model based on a simplified version of the Magic Formula; 2) longitudinal and lateral load transfers, playing a crucial role in the accurate prediction of the lateral tire forces and their yaw moment contributions; 3) coupling between longitudinal and lateral tire forces, which has a significant influence on the front-to-rear distribution of the braking forces generated by the controller; and 4) nonlinear peak and stiffness factors of the tire model, with visible yet negligible effects on the results.

show abstract

A Real-Time Nonlinear Model Predictive Control Strategy for Stabilization of an Electric Vehicle at the Limits of Handling

Cited by 90 publications

References 29 publications

Tire Condition Monitoring and Intelligent Tires Using Nanogenerators Based on Piezoelectric, Electromagnetic, and Triboelectric Effects

Tire Condition Monitoring and Intelligent Tires Using Nanogenerators Based on Piezoelectric, Electromagnetic, and Triboelectric Effects

A Real-Time Nonlinear MPC for Extreme Lateral Stabilization of Passenger Vehicles

On Prediction Model Fidelity in Explicit Nonlinear Model Predictive Vehicle Stability Control

Contact Info

Product

Resources

About