Robust stabilization of longitudinal tracking for cooperative adaptive cruise control considering input saturation

2020 ◽  
Vol 34 (35) ◽  
pp. 2050409
Author(s):  
Youguo He ◽  
Xiaoxiao Tian ◽  
Jie Shen ◽  
Chaochun Yuan ◽  
Yingkui Du
Keyword(s):  
Adaptive Cruise Control ◽  
Control Method ◽  
Controller Design ◽  
Actuator Saturation ◽  
Kinematic Model ◽  
Input Saturation ◽  
Robust Stabilization ◽  
State Feedback Control ◽  
Cruise Control ◽  
Cooperative Adaptive Cruise Control

This paper is concerned with the problem of constraint control for cooperative adaptive cruise control (CACC) with input saturation and input-additive uncertainties. An integrated longitudinal kinematic model of CACC system including vehicle model and constant time headway is established taking into account input saturation and input-additive uncertainties. According to the system’s robustness requirements under input saturation, the saturation control method is introduced. In order to achieve robust global stabilization of the system, a low-gain state feedback control law is designed by using linear low-gain feedback and gain scheduling. Meanwhile, in order to avoid the saturation of the control system, the low gain parameter [Formula: see text] is introduced into the controller design. Finally, the simulation of homogeneous and heterogeneous platoons is carried out by MATLAB/Simulink, which verifies the feasibility and effectiveness of the designed controller. Compared with the SMC controller, saturation controller successfully suppresses the acceleration amplification in the process of propagation along the vehicle platoon, avoids actuator saturation and realizes the stability of CACC system.

Human-Aware Autonomous Control for Cooperative Adaptive Cruise Control (CACC) Systems

2015 ◽  
Author(s):  
Xujie Wang ◽  
Yue Wang
Keyword(s):  
Adaptive Cruise Control ◽  
Controller Design ◽  
Cruise Control ◽  
String Stability ◽  
Driving Experience ◽  
Traffic Efficiency ◽  
Cooperative Adaptive Cruise Control ◽  
Human Driver ◽  
Driving Assistance ◽  
System Controller

This paper discusses the design of a human-aware cooperative adaptive cruise control (CACC) system that (i) takes into account driver comfort in autonomous cruise control, and (ii) provides assistive corrections to avoid driver errors. To incorporate driver characteristics into system controller design, two self-learning algorithms are used to estimate driver’s preferred time headway. We then develop a human-like blending control for CACC based on a model predictive control (MPC)-type method, which integrates the driver comfort, traffic efficiency, and fuel economy criteria. Furthermore, a driving assistance controller is developed to help human driver to maintain string stability in platoon. Simulation results show that (i) the human-like CACC design can significantly improve driving experience, and (ii) with the help of the assistive controller, string stability is satisfied for both exclusively autonomous CACC and when the CACC switches to manual driving in a platoon.

Multiobjective Optimization Framework for Cooperative Adaptive Cruise Control Vehicles in the Automated Vehicle Platooning Environment

2017 ◽  
Vol 2625 (1) ◽  
pp. 32-42 ◽  
Author(s):  
Zijia Zhong ◽  
Joyoung Lee ◽  
Liuhui Zhao
Keyword(s):  
Multiobjective Optimization ◽  
Fuel Consumption ◽  
Adaptive Cruise Control ◽  
Control Method ◽  
Cruise Control ◽  
Longitudinal Control ◽  
Target Time ◽  
Cooperative Adaptive Cruise Control ◽  
Vehicle Platoon ◽  
Unsafe Condition

Automated longitudinal control technology has been tested through cooperative adaptive cruise control (CACC), which is envisioned to improve highway mobility drastically by forming a vehicle platoon with short headway while maintaining stable traffic flow under disturbances. Compared with previous research efforts with the pseudomultiobjective optimization process, this paper proposes an automated longitudinal control framework based on multiobjective optimization (MOOP) for CACC by taking into consideration four optimization objectives: mobility, safety, driver comfort, and fuel consumption. Of the target time headways that have been tested, the proposed CACC platoon control method achieved the best performance with 0.9- and 0.6-s target time headways. Compared with a non-optimization-based CACC, the MOOP CACC achieved 98%, 93%, 42%, and 33% objective value reductions of time headway deviation, unsafe condition, jitter, and instantaneous fuel consumption, respectively. In comparison with a single-objective-optimization-based approach, which optimized only one of the four proposed objectives, it was shown that the MOOP-based CACC maintained a good balance between all of the objective functions and achieved Pareto optimality for the entire platoon.

Smart Driving System for Improving Traffic Flow

2017 ◽  
Vol 7 (7) ◽  
pp. 236
Author(s):  
Rajesh Kumar Gupta ◽  
L. N. Padhy ◽  
Sanjay Kumar Padhi
Keyword(s):  
Traffic Flow ◽  
Traffic Congestion ◽  
Urban Areas ◽  
Adaptive Cruise Control ◽  
Human Perception ◽  
Cruise Control ◽  
Driving System ◽  
V2v Communication ◽  
Traffic Conditions ◽  
Cooperative Adaptive Cruise Control

Traffic congestion on road networks is one of the most significant problems that is faced in almost all urban areas. Driving under traffic congestion compels frequent idling, acceleration, and braking, which increase energy consumption and wear and tear on vehicles. By efficiently maneuvering vehicles, traffic flow can be improved. An Adaptive Cruise Control (ACC) system in a car automatically detects its leading vehicle and adjusts the headway by using both the throttle and the brake. Conventional ACC systems are not suitable in congested traffic conditions due to their response delay.  For this purpose, development of smart technologies that contribute to improved traffic flow, throughput and safety is needed. In today’s traffic, to achieve the safe inter-vehicle distance, improve safety, avoid congestion and the limited human perception of traffic conditions and human reaction characteristics constrains should be analyzed. In addition, erroneous human driving conditions may generate shockwaves in addition which causes traffic flow instabilities. In this paper to achieve inter-vehicle distance and improved throughput, we consider Cooperative Adaptive Cruise Control (CACC) system. CACC is then implemented in Smart Driving System. For better Performance, wireless communication is used to exchange Information of individual vehicle. By introducing vehicle to vehicle (V2V) communication and vehicle to roadside infrastructure (V2R) communications, the vehicle gets information not only from its previous and following vehicle but also from the vehicles in front of the previous Vehicle and following vehicle. This enables a vehicle to follow its predecessor at a closer distance under tighter control.

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