# Research on Robust Control of Intelligent Vehicle Adaptive Cruise

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## Abstract

**:**

## 1. Introduction

## 2. Adaptive Cruise Longitudinal Kinematics Model between Vehicles

## 3. Upper Controller Design

## 4. Vehicle Longitudinal Dynamics Model

- (1)
- Simplify the four-wheel model into a two-wheel model, taking into account the uniformity of the vehicle’s overall weight and the load difference between the left and right wheels.
- (2)
- Without considering the tire slip characteristics, the road can provide sufficient road adhesion coefficient when the car is driving, thereby maximizing the braking effect [9].

## 5. Lower-Level Controller Design Based on μ Control Method

- (1)
- To ensure the tracking ability of the vehicle, the primary goal of the entire control system is to reduce the error between the actual acceleration of the vehicle and the expected acceleration outputted by the upper controller, so that the cruise system has a strong ability to follow cars.
- (2)
- Considering the disturbance of vehicle parameters, model uncertainty, and other disturbance factors, as well as the stability of the vehicle during driving, higher requirements are put forward for the control of the system. In the real car-following process, the speed of the front car and the distance between the main car and the front car are measured by onboard radar sensors, while the main car uses wheel speed sensors and acceleration sensors to obtain information such as vehicle speed and acceleration. Due to the various state information of the system being affected by external disturbances, noise in the measured signal is inevitable. Therefore, the controller should have good anti-interference performance.

#### 5.1. Inverse Longitudinal Dynamic Model

#### 5.2. Longitudinal Dynamic Perturbation Model

^{2}in the low-frequency range and 0.3 m/s

^{2}in the high-frequency range. For the uncertain model error of the system, one can select the weight function ${W}_{m}=\left(s+0.25\right)/\left(s+1\right)$ to indicate that the uncertain model error of the vehicle inverse model at low frequencies is 25%, while the uncertain model error at high frequencies is 100%.

#### 5.3. Lower-Level Controller Design

## 6. Simulation Verification and Analysis

## 7. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 13.**Difference between the distance of two vehicles and the expected distance (25% increse in mass).

Parameter | Numerical Value | Symbol |
---|---|---|

Mass | 1330 kg | m |

Moment of inertia of front and rear tires | 1.75 kg·m^{2} | J_{f}\J_{r} |

Rolling friction | 0.03 | μ |

Automotive frontal area | 1.87 m^{2} | A |

Wheelbase | 2.62 m | L |

Drag coefficient | 0.38 kg/m^{2} | ρ |

Wheel radius | 0.31 m | r |

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## Share and Cite

**MDPI and ACS Style**

Zhu, Z.; Bei, S.; Li, B.; Liu, G.; Tang, H.; Zhu, Y.; Gao, C.
Research on Robust Control of Intelligent Vehicle Adaptive Cruise. *World Electr. Veh. J.* **2023**, *14*, 268.
https://doi.org/10.3390/wevj14100268

**AMA Style**

Zhu Z, Bei S, Li B, Liu G, Tang H, Zhu Y, Gao C.
Research on Robust Control of Intelligent Vehicle Adaptive Cruise. *World Electric Vehicle Journal*. 2023; 14(10):268.
https://doi.org/10.3390/wevj14100268

**Chicago/Turabian Style**

Zhu, Zhaoxin, Shaoyi Bei, Bo Li, Guosi Liu, Haoran Tang, Yunhai Zhu, and Chencheng Gao.
2023. "Research on Robust Control of Intelligent Vehicle Adaptive Cruise" *World Electric Vehicle Journal* 14, no. 10: 268.
https://doi.org/10.3390/wevj14100268