车辆侧倾预测和主动控制综述.docx

A Study on Vehicle Roll and Rollover1. Vehicle Roll Dynamics An introduction to static and dynamic roll stability of a heavy commercial vehicle can be found in 1, which introduces different factors influencing the static and dynamic roll stability of a heavy commercial vehicle.2. Review of Vehicle Rollover Goldman R.W et al. 2 presents a review of literature pertaining to vehicular rollover. This review is limited to papers covering rollover of road vehicles, such as passenger cars, utility vehicles and heavy commercial trucks-both articulated and non-articulated, i.e. the review excludes papers regarding off-road vehicles. In addition, this review focuses mainly on cases of manoeuvre induced rollover such as rollover in cornering, lane-change manoeuvres, etc., though rollover by tripping is discussed to a certain degree. It begins with a general introduction to the rollover phenomenon that may be applied to both articulated and non-articulated vehicles. Non-articulated vehicles are then examined in more detail and a review of some research into stability metrics and the prediction of rollover for these vehicles is presented. Likewise, the stability metrics and prediction of rollover for articulated heavy trucks carrying rigid and liquid cargo is reviewed along with work into active suspensions, braking control and rollover warning devices. (This is a review, not too much point for you.)3. Prevention and Control to Vehicle RolloverSince traditional rollover prevention, load transfer ratio (LTR), relies on a lateral acceleration signal to calculate rollover propensity which cant predict the rollover of the vehicle correctly. Larish C et al. 3 proposes a new real-time rollover prevention systems, which utilizes a drivers steering input and several other sensor signals available from the vehicles electronic stability control system according to the conclusion that factors associated with steering had the greatest capability of providing the earliest rollover warning. More vehicle information (steering angle, yaw rate, and roll rate) is used in the index. At the same time, all of the required feedback variables can be easily measured with inexpensive sensors available in electronic stability control (ESC) systems.By building up the lateral and roll motion dynamics, the author gives the traditional LTR and the new PLTR defined which is shown to provide a time advance in the detection of rollover threats compared with the LTR index when simulated in CarSim.In paper 4, a rollover prevention controller is designed based on a linear vehicle model under the assumption that a drivers steering input is previewable. A linear optimal preview controller is designed. To avoid the full-state measurement of a linear quadratic regulator (LQR), linear quadratic static output feedback (LQ SOF) control is adopted. The author designed a preview controller through a 3-DOF vehicle model with a 2-DOF bicycle and 1-DOF roll model to describe the yaw and the lateral motion, and the roll motion in which control yaw moment by differential braking and active suspension force are used as actuators. It is concluded that the preview controller reduces the roll angle and the lateral acceleration by making the controlled vehicle exhibit understeer characteristics. Imine Hocine et al. 5 propose an original method about heavy-vehicle rollover risk prediction which is presented and validated experimentally. It is based on the calculation of the load transfer ratio (LTR), which depends on the estimated vertical forces using high-order sliding-mode (HOSM) observers.Two tests are carried out, the zigzag test and the braking test, the former for rollover study and the latter to show the rapidity and the robustness of the proposed method. But in the test, the LTR only reaches to 0.2, which is quite different from actual situation. Besides, the proposed method is tested on an instrumented tractor instead of a semi-trailer. Generally, two variables are difficult to measure in the rollover index, roll angle and the height of the center gravity. Sensors for measuring roll angle are expensive while sensor for c.g. height of vehicle in real-time doesnt exit. Rajamani Rajesh et al. 6 developed algorithms for real-time estimation of these variables. Experimental data have confirmed that the developed algorithms reliably performed in a number of different maneuvers that include constant steering, ramp steering, and sine with dwell steering tests.A driving control algorithm for 4WD electric vehicle is developed in 7 to improve vehicle maneuverability and lateral stability and, at the same time, prevent vehicle rollover. In this control algorithm, the rollover constraint for rollover prevention is obtained from the rollover index (RI).Hocine Imine et al. 8 developed an active steering assistance system to avoid the rollover of heavy vehicles (HV) with an estimator based on the high-order sliding mode observer to estimate the vehicle dynamics. The observer developed can estimate infinite time states and identify parameters. The aim of the active steering control is to ensure the convergence of the lateral acceleration of the vehicle to its limit.In order not to reduce the drive performance of vehicles, Mehmet Akar et al. 9 propose a switching controller (SC) which is coupled with identification schemes to determine vehicle lateral and roll parameters in speed varying situations. The SC is a differential braking force that is adaptive with the estimated CG height value. The simulation results work well but a theoretical analysis of the proposed methods and verification on a real vehicle are future research directions for this paper.In critical situations, the vehicle behavior undergoes considerable variations, such as lateral forces saturations, road friction variations, and so on. Thus, Hamid Dahmani et al. 10 design a TS-observer-based controller using H-infinite approach with sufficiently robust to ensure the vehicle stability. A more complex vehicle models should be considered in order to take into account the vehicle behavior after the wheel liftoff and the longitudinal speed variation in future work.Sangoh Han et al. 11 developed three monitoring systems to improve the robustness in estimating the lateral velocity and sideslip angle based on the sliding mode observer. Considering the fact that roll motion is closely related to the lateral tire forces in cornering, the first monitoring system is designed based on the 2-degree-of-freedon (2-DOF) bicycle model, including the rolling effect. The other two monitoring systems are designed based on the 3-DOF vehicle model, including the longitudinal dynamics. But improvements need to be done in the robustness of the estimation algorithms.A traditional rollover index utilizes lateral acceleration measurements and can detect only ontripped rollovers that happen due to high lateral acceleration from a sharp turn. But it fails to detect tripped rollovers. So a new rollover index for the detection of tripped and untripped rollovers is proposed in 12. The authors build up an untripped and tripped rollover model, which introduces unknown road input and lateral forces input considering the tripped rollover. Those unknown inputs can be replaced by relative outputs in algebraic equations, which are easy to measure. By comparing the traditional and the new rollover index in CARSIM simulation, it is concluded that both indexes can detect rollovers when a vehicle experiences an untripped rollover, but the new index works well when a vehicle suffers a tripped rollover. Besides, a sensitivity analysis is made between the new index and the mass change, which shows that the rollover index is roughly independent of the vehicle mass.Finally, due to the high cost of developing a full-size instrumented vehicle for rollover testing, a scaled vehicle is used to study the rollover index after a dynamic similitude analysis to a full-size vehicle. The experiment results show that the new rollover index can reliably detect the tripped and untripped rollover. (就我个人而言，我觉得这篇文章不错。作者在最后总结中自认为也不错，文章观点在发表刊物中属于首例)4. Reference 1 C. B. Winkler and R. D. Ervin,Rollover of Heavy Commercial Vehicles.2 Goldman R.W, El-Gindy M and Kulakowski B.T, Rollover dynamics of road vehicles: Literature survey, Heavy Vehicle Systems, vol. 8, no. 2, p. 103-141, 2001.3 Larish C, Piyabongkarn D, Tsourapas V and Rajamani R, A New Predictive Lateral Load Transfer Ratio for Rollover Prevention Systems, IEEE Transactions on Vehicular Technology, vol. 62, no, 7, p. 2928-2936, 2013.4 Yim, Seongjin, Design of a preview controller for vehicle rollover prevention, IEEE Transactions on Vehicular Technology, vol. 60, no. 9, p. 4217-4226, November 2011.5 Imine Hocine, Benallegue Abdelaziz, Madani Tarek and Srairi Salim, Rollover risk prediction of heavy vehicle using high-order sliding-mode observer: Experimental results, IEEE Transactions on Vehicular Technology, vol. 63, no. 6, p. 2533-2543, July 2014.6 Rajamani Rajesh, Piyabongkarn Damrongrit, Tsourapas Vasilis and Lew Jae Y, Parameter and state estimation in vehicle roll dynamics, IEEE Transactions on Intelligent Transportation Systems, vol. 12, no. 4, p. 1558-1567, December 2011.7 Kang Juyong , Yoo Jinho and Yi Kyongsu, Driving control algorithm for maneuverability, lateral stability, and rollover prevention of 4WD electric vehicles with independently driven front and rear wheels, IEEE Transactions on Vehicular Technology, vol. 60, no. 7, p. 2987-3001, September