Browsing by Author "Guo, Hongyan"

Now showing 1 - 4 of 4
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    Dual-envelop-oriented moving horizon path tracking control for fully automated vehicles
    (Elsevier, 2017-02-14) Guo, Hongyan; Liu, Jun; Cao, Dongpu; Chen, Hong; Yu, Ru; Lv, Chen
    A novel description of dual-envelop-oriented path tracking issue is presented for fully automated vehicles which considers shape of vehicle as inner-envelop (I-ENV) and feasible road region as outer-envelop (O-ENV). Then implicit linear model predictive control (MPC) approach is proposed to design moving horizon path tracking controller in order to solve the situations that may cause collision and run out of road in traditional path tracking method. The proposed MPC controller employed varied sample time and varied prediction horizon and could deal with modelling error effectively. In order to specify the effectiveness of the proposed dual-envelop-oriented moving horizon path tracking method, veDYNA-Simulink joint simulations in different running conditions are carried out. The results illustrate that the proposed path tracking scheme performs well in tracking the desired path, and could increase path tracking precision effectively.
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    Hazard-evaluation-oriented moving horizon parallel steering control for driver-automation collaboration during automated driving
    (IEEE, 2018-08-14) Guo, Hongyan; Song, Linhuan; Liu, Jun; Wang, Fei-Yue; Cao, Dongpu; Chen, Hong; Lv, Chen; Luk, Patrick Chi-Kwong
    Prompted by emerging developments in connected and automated vehicles, parallel steering control, one aspect of parallel driving, has become highly important for intelligent vehicles for easing the burden and ensuring the safety of human drivers. This paper presents a parallel steering control framework for an intelligent vehicle using moving horizon optimization. The framework considers lateral stability, collision avoidance and actuator saturation and describes them as constraints, which can blend the operation of a human driver and a parallel steering controller effectively. Moreover, the road hazard and the steering operation error are employed to evaluate the operational hazardous of an intelligent vehicle. Under the hazard evaluation, the intelligent vehicle will be mainly operated by the human driver when the vehicle operates in a safe and stable manner. The automated steering driving objective will play an active role and regulate the steering operations of the intelligent vehicle based on the hazard evaluation. To verify the effectiveness of the proposed hazard-evaluation-oriented moving horizon parallel steering control approach, various validations are conducted, and the results are compared with a parallel steering scheme that does not consider automated driving situations. The results illustrate that the proposed parallel steering controller achieves acceptable performance under both conventional conditions and hazardous conditions.
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    Hybrid-learning-based classification and quantitative inference of driver braking intensity of an electrified vehicle
    (IEEE, 2018-02-21) Lv, Chen; Xing, Yang; Lu, Chao; Liu, Yahui; Guo, Hongyan; Gao, Hongbo; Cao, Dongpu
    The recognition of driver's braking intensity is of great importance for advanced control and energy management for electric vehicles. In this paper, the braking intensity is classified into three levels based on novel hybrid unsupervised and supervised learning methods. First, instead of selecting threshold for each braking intensity level manually, an unsupervised Gaussian Mixture Model is used to cluster the braking events automatically with brake pressure. Then, a supervised Random Forest model is trained to classify the correct braking intensity levels with the state signals of vehicle and powertrain. To obtain a more efficient classifier, critical features are analyzed and selected. Moreover, beyond the acquisition of discrete braking intensity level, a novel continuous observation method is proposed based on Artificial Neural Networks to quantitative analyze and recognize the brake intensity using the prior determined features of vehicle states. Experimental data are collected in an electric vehicle under real-world driving scenarios. Finally, the classification and regression results of the proposed methods are evaluated and discussed. The results demonstrate the feasibility and accuracy of the proposed hybrid learning methods for braking intensity classification and quantitative recognition with various deceleration scenarios.
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    Simultaneous observation of hybrid states for cyber-physical systems: a case study of electric vehicle powertrain
    (IEEE, 2017-08-22) Lv, Chen; Liu, Yahui; Hu, Xiaosong; Guo, Hongyan; Cao, Dongpu; Wang, Fei-Yue
    As a typical cyber-physical system (CPS), electrified vehicle becomes a hot research topic due to its high efficiency and low emissions. In order to develop advanced electric powertrains, accurate estimations of the unmeasurable hybrid states, including discrete backlash nonlinearity and continuous half-shaft torque, are of great importance. In this paper, a novel estimation algorithm for simultaneously identifying the backlash position and half-shaft torque of an electric powertrain is proposed using a hybrid system approach. System models, including the electric powertrain and vehicle dynamics models, are established considering the drivetrain backlash and flexibility, and also calibrated and validated using vehicle road testing data. Based on the developed system models, the powertrain behavior is represented using hybrid automata according to the piecewise affine property of the backlash dynamics. A hybrid-state observer, which is comprised of a discrete-state observer and a continuous-state observer, is designed for the simultaneous estimation of the backlash position and half-shaft torque. In order to guarantee the stability and reachability, the convergence property of the proposed observer is investigated. The proposed observer are validated under highly dynamical transitions of vehicle states. The validation results demonstrates the feasibility and effectiveness of the proposed hybrid-state observer.