**Acknowledgements**

where *ε* represents the level of uncertainty, with maximum mass 1600 kg, maximum road slope 10 deg, and maximum wind speed 20 m/s (when ε = 10). ε = 0 implies that there is no model uncertainty. The root mean square error (RMSE) of acceleration is used to measure the capability of tracking. **Figure 8** presents the RMSE of acceleration error and the number of gear shifting per minute (denoted as *Ngear/min*). In nominal condition, the RMSE of acceleration error of the three robust controllers is almost the same. As the uncertainty level increases, the tracking capability of the SMC and *H<sup>∞</sup>* quickly drops, whereas the MMS still holds acceptable accuracy. **Figure 8(b)** is used to release the concern that the MMS might largely increase the

This chapter proposes a robust acceleration control method for consistent node dynamics in a platoon of automated connected vehicles (CAVs). The design, which is based on multiple model switching (MMS) control structure, is able to offer more consistent and approximately linear node dynamics for upper level control design even under large uncertainties, including vehicle parametric variation, varying road slop and strong environmental wind. The following

**(1)** Homogeneous and linear node dynamics is important for platoon control. This requires the acceleration tracking performance to be accurate and consistent, and accordingly results in critical challenges because of the linearization error of powertrain dynamics and large model uncertainties in and around vehicles. The proposed MMS control structure can divide the large uncertainties of vehicle longitudinal dynamics into small ones. Accordingly, multiple robust controllers are designed from the multiple model set, and a scheduling logic is also presented to automatically select the most appropriate candidate controller into loop according to the errors between current vehicle dynamics and models.

number of gear shifting because of its switching structure.

**Figure 8.** Performances under different uncertain levels.

**5. Conclusions**

54 Autonomous Vehicle

remarks are concluded:

This was supported by the State Key Laboratory of Automotive Safety and Energy under Project No. KF16192 and NSF China with grant 51575293.
