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- 저자 : Luo Yugong (검색결과 3 건)
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Jinghua Guo,Yugong Luo,Chuan Hu,Chen Tao,Keqiang Li 한국자동차공학회 2019 International journal of automotive technology Vol.20 No.2
In order to enhance the tracking performance and improve the stability of intelligent electric vehicles, combined Lane keeping and direct yaw moment control is a good choice. In this paper, an uncertain model of intelligent electric vehicles for combing the lane keeping and direct yaw moment control is deduced, in which time delay and data dropouts are involved. Since the intelligent electric vehicles have the features of time delay and strong uncertainties, a novel robust guaranteed cost combined lane keeping and direct yaw moment control system is constructed to manage the lateral motion of intelligent electric vehicles. The asymptotic stability of combined lane keeping and direct moment control system is verified based on the Lyapunov stability theory. Simulation tests are carried out to demonstrate the feasibility of the proposed control approach, and the results indicate that the presented robust combined control approach can accurately achieve the lane tracking capability, stability and maneuverability of intelligent electric vehicles.
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ROBUST H∞ FAULT-TOLERANT LATERAL CONTROL OF FOUR-WHEEL-STEERING AUTONOMOUS VEHICLES
Jinghua Guo,Yugong Luo,Keqiang Li 한국자동차공학회 2020 International journal of automotive technology Vol.21 No.4
This paper presents a robust H∞ fault-tolerant lateral controller of four wheel steering autonomous vehicles to enhance the autonomous driving performance and to track the desired road when a steering wheel fault happens. First, the lateral dynamic model of four wheel steering autonomous vehicles is constructed, which contains the features of parameter uncertainties and actuator faults of vehicles. Then, since the faulty steering wheel may fail to offer the desired torque and harm the lateral motion control system of autonomous vehicles, a novel robust H∞ fault tolerant state feedback lateral control law of four steering autonomous vehicles is designed to deal with actuator faults and parameter uncertainties. Finally, simulation tests are implemented in the Adams-Simulink joint platform with a high-fidelity and full-car model, and results verify the validity of this proposed control scheme.
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Guo Jinghua,Li WenChang,Luo Yugong,Li Keqiang 한국자동차공학회 2023 International journal of automotive technology Vol.24 No.4
This paper presents a novel model predictive adaptive cruise control strategy of intelligent electric vehicles based on deep reinforcement learning algorithm for driver characteristics. Firstly, the influence mechanism of factors such as inter-vehicle distance, relative speed and time headway (THW) on the driver’s behavior in the process of car following is analyzed by the correlation coefficient method. Then, the driver behavior in the process of car following is learned from the natural driving data, the car following model is established by the deep deterministic policy gradient (DDPG) algorithm, and the output acceleration of the DDPG model is used as the reference trajectory of the ego vehicle’s acceleration. Next, in order to reflect the driver behavior and achieve multi performance objective optimization of adaptive cruise control of intelligent electric vehicles, the model predictive controller (MPC) is designed and used for tracking the desired acceleration produced by the car following DDPG model. Finally, the performance of the proposed adaptive cruise control strategy is evaluated by the experimental tests, and the results demonstrate the effectiveness of proposed control strategy.
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