Vehicle Mass-Centroid Sideslip Angle Estimation Based on Extension Fusion of Fuzzy Sliding-Mode Observer and Sensor Signal Integral
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摘要: 车辆质心侧偏角对于车辆横向稳定状态判断具有重要作用,对质心侧偏角的高估或低估都会对稳定性控制系统产生影响. 针对目前质心侧偏角估计方法仍具有较大误差且实用性不强,提出了以降低观测误差及提高估计系统实用性为目标的方法,构建了鲁棒性较强的模糊二阶滑模观测器计算质心侧偏角观测值,同时采用惯性测量单元信号计算质心侧偏角积分值. 之后分析了两种估计方法的优缺点,对质心侧偏角观测估计值与传感器信号积分估计值进行可拓融合,以实现采用传感器信号估计对观测值进行修正. 最后通过Simulink/TruckSim仿真、硬件在环仿真,进行了质心侧偏角估计方法的验证. 在实车定圆加速测试工况中以控制效果论证了所提出方法的有效性. 研究表明所提出方法能够准确反映实际质心侧偏角状态,并且可靠性、实用性均较佳.Abstract: Vehicle mass-centroid sideslip angle plays an important role in the judgment of vehicle lateral stability. Overestimation or underestimation of the sideslip angle will affect the stability control system. At present, the estimation of sideslip angle still has a large error and has not been practically used in engineering. In order to reduce the observation error and improve the practicability of the estimation system, a robust fuzzy second-order sliding-mode observer was proposed to calculate the observed value of the sideslip angle, and the integral value of the sideslip angle was calculated by using the inertial measurement unit signal. Then, the advantages and disadvantages of the two estimation methods were analyzed, and the observation estimation value and the integral estimation value of the sideslip angle were extensively fused to realize the correction of the observation value with the sensor signal. Finally, the estimation method of sideslip angle was verified by Simulink/TruckSim simulation and hardware in the loop simulation. The effectiveness of the proposed method is proved based on the control effect in real vehicle test under the constant circular acceleration condition. The results show that the proposed method can accurately reflect the actual sideslip angle, and has good reliability and practicability.
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Key words:
- mass-centroid sideslip angle /
- fuzzy logic /
- sliding mode observer /
- extension fusion
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表 1
$ {\lambda }_{1} $ 为输出的模糊规则表Table 1. Fuzzy rules of the output
$ {\lambda }_{1} $ $ {\lambda }_{1} $ $ {\mathrm{N}\mathrm{B}}_{{\mathrm{e}}_{\mathrm{\omega }}} $ $ {\mathrm{N}\mathrm{M}}_{{\mathrm{e}}_{\mathrm{\omega }}} $ $ {\mathrm{Z}}_{{\mathrm{e}}_{\mathrm{\omega }}} $ $ {\mathrm{P}\mathrm{M}}_{{\mathrm{e}}_{\mathrm{\omega }}} $ $ {\mathrm{P}\mathrm{B}}_{{\mathrm{e}}_{\mathrm{\omega }}} $ $ {\mathrm{N}\mathrm{B}}_{{\dot{\mathrm{e}}}_{\mathrm{\omega }}} $ $ {\mathrm{M}\mathrm{I}}_{{\mathrm{\lambda }}_{1}} $ $ {\mathrm{M}\mathrm{I}}_{{\mathrm{\lambda }}_{1}} $ $ {\mathrm{M}\mathrm{I}}_{{\mathrm{\lambda }}_{1}} $ $ {\mathrm{M}}_{{\mathrm{\lambda }}_{1}} $ $ {\mathrm{M}}_{{\mathrm{\lambda }}_{1}} $ $ {\mathrm{N}\mathrm{M}}_{{\dot{\mathrm{e}}}_{\mathrm{\omega }}} $ $ {\mathrm{M}\mathrm{I}}_{{\mathrm{\lambda }}_{1}} $ $ {\mathrm{M}\mathrm{I}}_{{\mathrm{\lambda }}_{1}} $ $ {\mathrm{S}}_{{\mathrm{\lambda }}_{1}} $ $ {\mathrm{M}}_{{\mathrm{\lambda }}_{1}} $ $ {\mathrm{B}}_{{\mathrm{\lambda }}_{1}} $ $ {\mathrm{Z}}_{{\dot{\mathrm{e}}}_{\mathrm{\omega }}} $ $ {\mathrm{S}}_{{\mathrm{\lambda }}_{1}} $ $ {\mathrm{S}}_{{\mathrm{\lambda }}_{1}} $ $ {\mathrm{M}}_{{\mathrm{\lambda }}_{1}} $ $ {\mathrm{B}}_{{\mathrm{\lambda }}_{1}} $ $ {\mathrm{B}}_{{\mathrm{\lambda }}_{1}} $ $ {\mathrm{P}\mathrm{M}}_{{\dot{\mathrm{e}}}_{\mathrm{\omega }}} $ $ {\mathrm{S}}_{{\mathrm{\lambda }}_{1}} $ $ {\mathrm{M}}_{{\mathrm{\lambda }}_{1}} $ $ {\mathrm{B}}_{{\mathrm{\lambda }}_{1}} $ $ {\mathrm{M}\mathrm{A}}_{{\mathrm{\lambda }}_{1}} $ $ {\mathrm{M}\mathrm{A}}_{{\mathrm{\lambda }}_{1}} $ $ {\mathrm{P}\mathrm{B}}_{{\dot{\mathrm{e}}}_{\mathrm{\omega }}} $ $ {\mathrm{M}}_{{\mathrm{\lambda }}_{1}} $ $ {\mathrm{M}}_{{\mathrm{\lambda }}_{1}} $ $ {\mathrm{M}\mathrm{A}}_{{\mathrm{\lambda }}_{1}} $ $ {\mathrm{M}\mathrm{A}}_{{\mathrm{\lambda }}_{1}} $ $ {\mathrm{M}\mathrm{A}}_{{\mathrm{\lambda }}_{1}} $ 表 2
$ {\lambda }_{2} $ 为输出的模糊规则表Table 2. Fuzzy rules of the output
$ {\lambda }_{2} $ $ {\lambda }_{2} $ $ {\mathrm{N}\mathrm{B}}_{{\mathrm{e}}_{{\mathrm{a}}_{\mathrm{y}}}} $ $ {\mathrm{Z}}_{{\mathrm{e}}_{{\mathrm{a}}_{\mathrm{y}}}} $ $ {\mathrm{P}\mathrm{B}}_{{\mathrm{e}}_{{\mathrm{a}}_{\mathrm{y}}}} $ ${\mathrm{N}\mathrm{B} }_{ {\dot{e } }_{ {a }_{y } } }$ $ {\mathrm{M}\mathrm{I}}_{{\mathrm{\lambda }}_{2}} $ $ {\mathrm{M}\mathrm{I}}_{{\mathrm{\lambda }}_{2}} $ $ {\mathrm{M}}_{{\mathrm{\lambda }}_{2}} $ ${\mathrm{Z} }_{ {\dot{e } }_{ {a }_{y } } }$ $ {\mathrm{M}\mathrm{I}}_{{\mathrm{\lambda }}_{2}} $ $ {\mathrm{M}}_{{\mathrm{\lambda }}_{2}} $ $ {\mathrm{M}\mathrm{A}}_{{\mathrm{\lambda }}_{2}} $ ${\mathrm{P}\mathrm{B} }_{ {\dot{e } }_{ {a }_{y } } }$ $ {\mathrm{M}}_{{\mathrm{\lambda }}_{2}} $ $ {\mathrm{M}\mathrm{A}}_{{\mathrm{\lambda }}_{2}} $ $ {\mathrm{M}\mathrm{A}}_{{\mathrm{\lambda }}_{2}} $ 表 3 模糊调节与可拓集合相关参数
Table 3. Parameters used in fuzzy regulation and extension set
参数 数值 单位 $ {\lambda }_{1}^{\mathrm{m}\mathrm{i}\mathrm{n}} $ 0.08 - $ {\lambda }_{1}^{\mathrm{m}\mathrm{a}\mathrm{x}} $ 0.13 - $ {\lambda }_{2}^{\mathrm{m}\mathrm{i}\mathrm{n}} $ −0.18 - $ {\lambda }_{2}^{\mathrm{m}\mathrm{a}\mathrm{x}} $ −0.10 - $ {\lambda }_{3}^{\mathrm{m}\mathrm{i}\mathrm{n}} $ −0.01 - $ {\lambda }_{3}^{\mathrm{m}\mathrm{a}\mathrm{x}} $ −0.005 - $ {a}_{1} $ 2 $ \mathrm{m}/{\mathrm{s}}^{2} $ $ {a}_{2} $ 3 $ \mathrm{m}/{\mathrm{s}}^{2} $ $ {a}_{3} $ 7 $ \mathrm{m}/{\mathrm{s}}^{2} $ $ {V}_{1} $ 25 $ \mathrm{k}\mathrm{m}/\mathrm{h} $ $ {V}_{2} $ 80 $ \mathrm{k}\mathrm{m}/\mathrm{h} $ 表 4 TruckSim仿真车辆参数
Table 4. Simulation vehicle parameters in TruckSim
参数 释义 数值 单位 ${m}_{{\rm{s}}}$ 簧上质量 4457 $ \mathrm{k}\mathrm{g} $ ${m}_{{\rm{u}}}$ 簧下质量 1305 $ \mathrm{k}\mathrm{g} $ $ m $ 整车总质量 5762 $ \mathrm{k}\mathrm{g} $ ${h}_{{\rm{g}}}$ 质心高度 1173 $ \mathrm{m}\mathrm{m} $ $ a $ 质心到前轴距离 1113 $ \mathrm{m}\mathrm{m} $ $ b $ 质心到后轴距离 2787 $ \mathrm{m}\mathrm{m} $ ${I}_{\textit{z}}$ 簧上质量绕z轴
转动惯量34818.2 $\mathrm{k}\mathrm{g}\cdot{\mathrm{m} }^{2}$ ${C}_{{\rm{f}}}$ 前轴侧偏刚度 −160000 $ \mathrm{N}/\mathrm{r}\mathrm{a}\mathrm{d} $ ${C}_{{\rm{r}}}$ 后轴侧偏刚度 −400000 $ \mathrm{N}/\mathrm{r}\mathrm{a}\mathrm{d} $ -
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