**Robust Accelerating Control for Consistent Node Dynamics in a Platoon of CAVs**

Feng Gao, Shengbo Eben Li and Keqiang Li

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/63352

#### **Abstract**

[22] Y. Kuwata, G. A. Fiore, J. Teo, E. Frazzoli, and J. P. How, "Motion planning for urban driving using RRT," in *IEEE/RSJ International Conference on Intelligent Robots and*

[23] Y. Kuwata, S. Karaman, J. Teo, E. Frazzoli, J. P. How, and G. Fiore, "Real-time motion planning with applications to autonomous urban driving," *IEEE Transactions on Control*

[24] L. Han, Q. H. Do, and S. Mita, "Unified path planner for parking an autonomous vehicle based on RRT," in *2011 IEEE International Conference on Robotics and Automation*

[25] R. Kala, and K. Warwick, "Planning of multiple autonomous vehicles using RRT," in *2011 IEEE 10th International Conference on Cybernetic Intelligent Systems (CIS)*, 2011, pp.

[26] J. H. Jeon, S. Karaman, and E. Frazzoli, "Anytime computation of time-optimal off-road vehicle maneuvers using the RRT\*," in *2011 50th IEEE Conference on Decision and Control*

[27] L. Ma, J. Xue, K. Kawabata, J. Zhu, C. Ma, and N. Zheng, "Efficient sampling-based motion planning for on-road autonomous driving," *IEEE Transactions on Intelligent*

[28] S. S. Mehta, C. Ton, M. J. McCourt, Z. Kan, E. Doucette, and W. Curtis, "Human-assisted RRT for path planning in urban environments," in *2015 IEEE International Conference*

[29] M. Du, T. Mei, H. Liang, J. Chen, R. Huang, and P. Zhao, "Drivers' visual behaviorguided RRT motion planner for autonomous on-road driving," *Sensors*, 16, 102, 2016.

[30] X. Lan and S. Di Cairano, "Continuous curvature path planning for semi-autonomous vehicle maneuvers using RRT," in *2015 European Control Conference (ECC)*, 2015, pp.

[31] D. Dunbar, and G. Humphreys, "A spatial data structure for fast Poisson-disk sample

*and European Control Conference (CDC-ECC)*, 2011, pp. 3276–3282.

*on Systems, Man, and Cybernetics (SMC)*, 2015, pp. 941–946.

generation," *ACM Transaction Graphic*, 25(3), 503–508, 2006.

*Systems, 2008. IROS 2008*, 2008, pp. 1681–1686.

*Systems Technology*, 17, 1105–1118, 2009.

*Transportation Systems*, 16, 2015, 1961–1976.

*(ICRA)*, 2011, pp. 5622–5627.

20–25.

38 Autonomous Vehicle

2360–2365.

Driving as a platoon has potential to significantly benefit traffic capacity and safety. To generate more identical dynamics of nodes for a platoon of automated connected vehicles (CAVs), this chapter presents a robust acceleration controller using a multi‐ ple model control structure. The large uncertainties of node dynamics are divided into small ones using multiple uncertain models, and accordingly multiple robust control‐ lers are designed. According to the errors between current node and multiple models, a scheduling logic is proposed, which automatically selects the most appropriate candidate controller into loop. Even under relatively large plant uncertainties, this method can offer consistent and approximately linear dynamics, which simplifies the synthesis of upper level platoon controller. This method is validated by comparative simulations with a sliding model controller and a fixed **H**∞ controller.

**Keywords:** automated connected vehicles (CAVs), platoon control, acceleration con‐ trol, robustness, multi-model
