基于参数估计的车辆稳定性控制策略研究
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摘要
车辆的稳定性控制(Vehicle Stability Control,简称VSC)主要是为了防止车辆在弯道行驶或制动时出现失稳的现象。近年来,尽管在VSC的研究上取得了长足的进步,但是它的进一步推广和应用却受到了两方面的制约：一方面,随着控制系统的不断复杂化,控制精度越来越高,所需要的传感器数量也越来越多,这使得设计成本不断加大；另一方面,控制器很难实时获得轮-地交互作用信息,这使得控制策略不能根据不同的路面附着条件和轮-地接触状况进行实时调整。
基于模型的估计方法能充分利用现有的传感器信息,估计出一些不易测量的车辆状态或轮-地交互作用参数,是减少设计和生产成本的有效方法。但是这种估计算法的复杂程度和计算精度在很大程度上取决于所使用的车辆模型。所以,本文首先建立了一个完整的整车动力学模型,包括：轮胎模型、转向系统模型、制动系统模型和单轮模型。其中,为了更好地描述轮-地交互作用,本文基于车轮侧偏特性建立了三种不同精度的轮胎模型,以期为不同的估计算法提供更合理的选择。
其次,使用线性轮胎模型,利用Kalman滤波算法和制动防抱死系统(Anti-lock Braking System,简称ABS)轮速传感器的信号,估计出了四个车辆状态参数。另外,使用Fiala轮胎模型,推导出了转向系总回正力矩的峰值和轮-地摩擦系数间的线性关系,进而利用递推最小二乘估计算法和电动助力转向(Electric Power Steering,简称EPS)系统自带的传感器信号设计了MAMM (Maximum Aligning Moment Method)算法,估计出了轮-地摩擦系数。仿真和试验结果证明,Kalman滤波算法和MAMM算法在小转角、纯侧滑情况下具有很好的估计精度,但在侧偏角或纵向力大幅增大时,估计精度显著下降。
为了改进线性算法的估计精度,使用精度更高的"Brush轮胎模型”,针对不同的传感器配置,设计了两种非线性观测器。其中,高精度方案的非线性观测器,在加入横摆角速度和侧向加速度传感器后,具有很高的估计精度,可为高端车的控制器提供必要的参数支持；而低成本方案的非线性观测器仅需要ABS和EPS提供的传感器信号就可以估计出车辆状态和轮-地摩擦系数,且相对于线性算法,估计精度大大提高。另外,结合MAMM算法和非线性观测器各自的优点,设计了一种联合估计算法,有效改进了对轮-地摩擦系数的估计精度。
最后,借鉴飞行器包络线控制和现有的车辆稳定性控制策略,设计了一种改进的车辆侧向稳定性控制策略(Enhanced Stability Control System,简称ESCS)。ESCS利用非线性观测器的估计结果,实时计算轮-地间的接触状况,使控制器能针对当前的轮-地交互作用情况选择到最佳的切入时机。同时,ESCS充分利用了现有的车载传感器和ABS的滑移率控制逻辑,有效扩展了ABS在车辆侧向稳定性方面的功能,并基于ESCS的控制逻辑,设计了一种最佳滑移率搜索算法,进一步提高了控制策略的控制效果。
VSC (Vehicle Stability Control) is designed to prevent vehicle lossof stability during traction or braking at corners. Recently, significantprogress has been made in VSC, however, its further extension andapplication are limited by two major respects: on one hand, more sensorsare required to improve the precision of the controller, which increasesthe cost of the control system; on the other hand, it’s hard for thecontroller to obtain the information of tire-road interaction, as a result ofwhich the control strategy can’t be adjusted when the road conditionvaries suddenly.
Model based estimation is a solution for the design of a low costcontroller. Utilizing the existing measurement signals, this method is ableto obtain vehicle states and parameters which are currently hard tomeasure. However, the complexity and the accuracy of the estimationalgorithm depend on those of the vehicle model. Therefore, a full vehiclemodel is established which includes: the tire model, the steering systemmodel, the braking system model and the single wheel model. Moreover,in order to provide the estimation algorithm with a wide choice, threedifferent kinds of tire models are proposed based on the analysis of tirelateral characteristics.
Based on the linear tire model, the Kalman filter algorithm is used toestimate four important vehicle states with the help of the measurementsignals of ABS (Anti-lock Braking System) wheel-speed sensors.Meanwhile, the linear relationship between the maximum total aligningmoment of the steering system and tire-road friction coefficient is derivedbased on Fiala tire model. A MAMM (Maximum Aligning MomentMethod) is developed for the estimation of tire-road friction coefficientutilizing only the measurement signals of EPS (Electric Power Steering).Furthermore, a RLS (Recursive Least Squares) algorithm is designed to identify the parameter online. Simulation and experimental results showthat the accuracies of Kalman filter algorithm and MAMM aresatisfactory under the condition of small steering angle and pure side slip;however the results become unsatisfactory with the increase of tire sideslip angles or tire longitudinal forces.
Based on the Brush tire model, two nonlinear observers are designedto improve the accuracy of the linear algorithm under two differentarrangements of sensors. The high accuracy nonlinear observer performsvery well but requires two additional sensors to measure yaw rate andlateral acceleration of the vehicle. Therefore, it can be an option for thecontroller equipped in luxury cars. On the contrary, the low cost nonlinearobserver, which performs much better than linear algorithm, only requiresthe measurement signals of ABS and EPS. Furthermore, a combinedmethod (CM) is proposed to improve the accuracy of tire-road frictioncoefficient by taking advantages of both the algorithm of MAMM andnonlinear observer.
Finally, an Enhanced Stability Control System (ESCS) is introduced,which borrows the algorithm of envelope control system applied inaircrafts and the current stability control system in production vehicles.With the help of the estimated parameters of the nonlinear observer, thecontroller of ESCS is able to obtain the real-time information of tire-roadinteraction and intervene at a better time. As a low cost and effectivesolution to extend the function of ABS to lateral dynamics in stability, theESCS control strategy is proposed based on only the existing EPS andABS sensors and the similar slip control algorithm in ABS. Meanwhile,utilizing the estimated tire-road friction coefficient and theone-dimensional search-gradient method, the so called “optimal slipratio” is calculated to make the performance of ESCS even better.
基于模型的估计方法能充分利用现有的传感器信息,估计出一些不易测量的车辆状态或轮-地交互作用参数,是减少设计和生产成本的有效方法。但是这种估计算法的复杂程度和计算精度在很大程度上取决于所使用的车辆模型。所以,本文首先建立了一个完整的整车动力学模型,包括：轮胎模型、转向系统模型、制动系统模型和单轮模型。其中,为了更好地描述轮-地交互作用,本文基于车轮侧偏特性建立了三种不同精度的轮胎模型,以期为不同的估计算法提供更合理的选择。
其次,使用线性轮胎模型,利用Kalman滤波算法和制动防抱死系统(Anti-lock Braking System,简称ABS)轮速传感器的信号,估计出了四个车辆状态参数。另外,使用Fiala轮胎模型,推导出了转向系总回正力矩的峰值和轮-地摩擦系数间的线性关系,进而利用递推最小二乘估计算法和电动助力转向(Electric Power Steering,简称EPS)系统自带的传感器信号设计了MAMM (Maximum Aligning Moment Method)算法,估计出了轮-地摩擦系数。仿真和试验结果证明,Kalman滤波算法和MAMM算法在小转角、纯侧滑情况下具有很好的估计精度,但在侧偏角或纵向力大幅增大时,估计精度显著下降。
为了改进线性算法的估计精度,使用精度更高的"Brush轮胎模型”,针对不同的传感器配置,设计了两种非线性观测器。其中,高精度方案的非线性观测器,在加入横摆角速度和侧向加速度传感器后,具有很高的估计精度,可为高端车的控制器提供必要的参数支持；而低成本方案的非线性观测器仅需要ABS和EPS提供的传感器信号就可以估计出车辆状态和轮-地摩擦系数,且相对于线性算法,估计精度大大提高。另外,结合MAMM算法和非线性观测器各自的优点,设计了一种联合估计算法,有效改进了对轮-地摩擦系数的估计精度。
最后,借鉴飞行器包络线控制和现有的车辆稳定性控制策略,设计了一种改进的车辆侧向稳定性控制策略(Enhanced Stability Control System,简称ESCS)。ESCS利用非线性观测器的估计结果,实时计算轮-地间的接触状况,使控制器能针对当前的轮-地交互作用情况选择到最佳的切入时机。同时,ESCS充分利用了现有的车载传感器和ABS的滑移率控制逻辑,有效扩展了ABS在车辆侧向稳定性方面的功能,并基于ESCS的控制逻辑,设计了一种最佳滑移率搜索算法,进一步提高了控制策略的控制效果。
VSC (Vehicle Stability Control) is designed to prevent vehicle lossof stability during traction or braking at corners. Recently, significantprogress has been made in VSC, however, its further extension andapplication are limited by two major respects: on one hand, more sensorsare required to improve the precision of the controller, which increasesthe cost of the control system; on the other hand, it’s hard for thecontroller to obtain the information of tire-road interaction, as a result ofwhich the control strategy can’t be adjusted when the road conditionvaries suddenly.
Model based estimation is a solution for the design of a low costcontroller. Utilizing the existing measurement signals, this method is ableto obtain vehicle states and parameters which are currently hard tomeasure. However, the complexity and the accuracy of the estimationalgorithm depend on those of the vehicle model. Therefore, a full vehiclemodel is established which includes: the tire model, the steering systemmodel, the braking system model and the single wheel model. Moreover,in order to provide the estimation algorithm with a wide choice, threedifferent kinds of tire models are proposed based on the analysis of tirelateral characteristics.
Based on the linear tire model, the Kalman filter algorithm is used toestimate four important vehicle states with the help of the measurementsignals of ABS (Anti-lock Braking System) wheel-speed sensors.Meanwhile, the linear relationship between the maximum total aligningmoment of the steering system and tire-road friction coefficient is derivedbased on Fiala tire model. A MAMM (Maximum Aligning MomentMethod) is developed for the estimation of tire-road friction coefficientutilizing only the measurement signals of EPS (Electric Power Steering).Furthermore, a RLS (Recursive Least Squares) algorithm is designed to identify the parameter online. Simulation and experimental results showthat the accuracies of Kalman filter algorithm and MAMM aresatisfactory under the condition of small steering angle and pure side slip;however the results become unsatisfactory with the increase of tire sideslip angles or tire longitudinal forces.
Based on the Brush tire model, two nonlinear observers are designedto improve the accuracy of the linear algorithm under two differentarrangements of sensors. The high accuracy nonlinear observer performsvery well but requires two additional sensors to measure yaw rate andlateral acceleration of the vehicle. Therefore, it can be an option for thecontroller equipped in luxury cars. On the contrary, the low cost nonlinearobserver, which performs much better than linear algorithm, only requiresthe measurement signals of ABS and EPS. Furthermore, a combinedmethod (CM) is proposed to improve the accuracy of tire-road frictioncoefficient by taking advantages of both the algorithm of MAMM andnonlinear observer.
Finally, an Enhanced Stability Control System (ESCS) is introduced,which borrows the algorithm of envelope control system applied inaircrafts and the current stability control system in production vehicles.With the help of the estimated parameters of the nonlinear observer, thecontroller of ESCS is able to obtain the real-time information of tire-roadinteraction and intervene at a better time. As a low cost and effectivesolution to extend the function of ABS to lateral dynamics in stability, theESCS control strategy is proposed based on only the existing EPS andABS sensors and the similar slip control algorithm in ABS. Meanwhile,utilizing the estimated tire-road friction coefficient and theone-dimensional search-gradient method, the so called “optimal slipratio” is calculated to make the performance of ESCS even better.
引文
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