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Lidar is a key sensor of autonomous driving systems, but the spatial distribution of its point cloud is uneven because of its scanning mechanism, which greatly degrades the clustering performance of the traditional density-based spatial clustering of application with noise (DSC). Considering the outline feature of detected objects for intelligent vehicles, a DSC-based adaptive clustering method (DAC) is proposed with the adoption of an elliptic neighborhood, which is designed according to the distribution properties of the point cloud. The parameters of the ellipse are adaptively adjusted with the location of the sample point to deal with the uniformity of points in different ranges. Furthermore, the dependence among different parameters of DAC is analyzed, and the parameters are numerically optimized with the KITTI dataset by considering comprehensive performance. To verify the effectiveness, a comparative experiment was conducted with a vehicle equipped with three IBEO LUX8 lidars on campus, and the results show that compared with DSC using a circular neighborhood, DAC has a better clustering performance and can notably reduce the rate of over-segmentation and under-segmentation.
Caihong Li; Feng Gao; Xiangyu Han; Bowen Zhang. A New Density-Based Clustering Method Considering Spatial Distribution of Lidar Point Cloud for Object Detection of Autonomous Driving. Electronics 2021, 10, 2005 .
AMA StyleCaihong Li, Feng Gao, Xiangyu Han, Bowen Zhang. A New Density-Based Clustering Method Considering Spatial Distribution of Lidar Point Cloud for Object Detection of Autonomous Driving. Electronics. 2021; 10 (16):2005.
Chicago/Turabian StyleCaihong Li; Feng Gao; Xiangyu Han; Bowen Zhang. 2021. "A New Density-Based Clustering Method Considering Spatial Distribution of Lidar Point Cloud for Object Detection of Autonomous Driving." Electronics 10, no. 16: 2005.
Performance limit evaluation of automatic driving system before putting into the market is critical for driving safety. The evolution test by genetic algorithm (GA) is a method by iteratively generating new test scenarios according to the last test results. To avoid its blind search for better efficiency, a scenario complexity index is proposed to measure the test effectiveness indirectly and guide the evolution process under the assumption that a complex scenario is more challenging to realise automatic driving. The traditional crossover and mutation operators are modified to generate more complex scenarios to improve the test efficiency. The advantage of the improved crossover/mutation operators in increasing the offspring's scenario complexity index is analysed in theory. Moreover, the influence of the design parameters on the evolution test process and the global convergence are also discussed. The new evolution test by this improved GA has been applied to find the collision condition of a parallel automatic parking system to validate its effectiveness.
Feng Gao; Qiang Zhang; Zaidao Han; Yiheng Yang. Evolution test by improved genetic algorithm with application to performance limit evaluation of automatic parallel parking system. IET Intelligent Transport Systems 2021, 15, 754 -764.
AMA StyleFeng Gao, Qiang Zhang, Zaidao Han, Yiheng Yang. Evolution test by improved genetic algorithm with application to performance limit evaluation of automatic parallel parking system. IET Intelligent Transport Systems. 2021; 15 (6):754-764.
Chicago/Turabian StyleFeng Gao; Qiang Zhang; Zaidao Han; Yiheng Yang. 2021. "Evolution test by improved genetic algorithm with application to performance limit evaluation of automatic parallel parking system." IET Intelligent Transport Systems 15, no. 6: 754-764.
To simultaneously deal with the uncertain interaction topology, parametric errors and external disturbances, this paper proposes a new coordinated control scheme for the platoon composed of nonlinear and heterogeneous automated vehicles (AVs). In this scheme, different perturbations are dealt with separately to reduce the contraction among them by using the sliding mode control theory. Considering the individual dynamics, a distributed coordinated controller including both lateral and longitudinal motions is designed for each AV with online estimation of the unknown parameters and disturbances. On the sliding surfaces of longitudinal motion, a decoupling approach using the eigenvalue decomposition of topological matrix and linear transformation is proposed to deal with the topological uncertainty. Then the dynamical system of platoon coupled by the information flow is decomposed into the subsystems with lower order. The relationship of the robust performance between the original and decoupled system is analyzed theoretically. Based on this theoretical conclusion, a numerical way is given based on linear matrix inequality (LMI) theory to design the parameters of sliding motion dynamics. By this way, the exact topological matrix is not necessary and only the bound of its eigenvalue is required. The effectiveness of the proposed strategy is validated by several comparative simulations under variety conditions.
Gao Feng; Dongfang Dang; Yingdong He. Robust Coordinated Control of Nonlinear Heterogeneous Platoon Interacted by Uncertain Topology. IEEE Transactions on Intelligent Transportation Systems 2020, PP, 1 -11.
AMA StyleGao Feng, Dongfang Dang, Yingdong He. Robust Coordinated Control of Nonlinear Heterogeneous Platoon Interacted by Uncertain Topology. IEEE Transactions on Intelligent Transportation Systems. 2020; PP (99):1-11.
Chicago/Turabian StyleGao Feng; Dongfang Dang; Yingdong He. 2020. "Robust Coordinated Control of Nonlinear Heterogeneous Platoon Interacted by Uncertain Topology." IEEE Transactions on Intelligent Transportation Systems PP, no. 99: 1-11.
The test of intelligent driving systems is faced with the challenges of efficiency because real traffic scenarios are infinite, uncontrollable and difficult to be precisely defined. Based on the complexity index of scenario designed to measure the test effect indirectly, a new combinational generation algorithm of test cases is proposed to make a balance between multiple objects including coverage, case number and test effect. Then a joint simulation platform based on Matlab, PreScan and Carsim is set up to realize the automatic construction of 3D test environment from the generated scenarios, conduction of test and evaluation of test results seamlessly. The proposed strategy has been validated by application to a traffic jam pilot system and the results show that it is beneficial to improve the complexity of scenario and the designed scenarios can find system faults effectively, and the required time to conduct tests is reduced obviously by automation.
Feng Gao; Jianli Duan; Zaidao Han; Yingdong He. Automatic Virtual Test Technology for Intelligent Driving Systems Considering Both Coverage and Efficiency. IEEE Transactions on Vehicular Technology 2020, 69, 14365 -14376.
AMA StyleFeng Gao, Jianli Duan, Zaidao Han, Yingdong He. Automatic Virtual Test Technology for Intelligent Driving Systems Considering Both Coverage and Efficiency. IEEE Transactions on Vehicular Technology. 2020; 69 (12):14365-14376.
Chicago/Turabian StyleFeng Gao; Jianli Duan; Zaidao Han; Yingdong He. 2020. "Automatic Virtual Test Technology for Intelligent Driving Systems Considering Both Coverage and Efficiency." IEEE Transactions on Vehicular Technology 69, no. 12: 14365-14376.
In open traffic environments, humans still have to remain in the control loop of vehicle due to the insufficient of the existing technologies and their high costs. For the realization of cooperation between the human and the automatic driving system, the determination of the time when automatic driving is necessary is very important. To avoid unnecessary intervention when the driver has the control authority of vehicle, a new driving capability-based transition strategy was proposed, which comprehensively considers the driver’s correction ability and the driving risk. The transition time from the human driver to the automatic driving system is determined by an unreliable domain (UD), whose boundary is modeled according to the driving data recorded by a driving simulator and statistically described by a log-normal distribution. Furthermore, an adaptive algorithm is designed to update the parameters of UD boundary online to make this strategy suitable for different drivers. This UD-based transition strategy is validated by several tests on the driving simulator. The bench test results show that the individual driving characteristic can be identified by the adaptive algorithm in time, the transition time determined by UD is more accurate, and sufficient time is reserved for the correction carried out by the automatic driving system.
Fengmin Tang; Feng Gao; Zilong Wang. Driving Capability-Based Transition Strategy for Cooperative Driving: From Manual to Automatic. IEEE Access 2020, 8, 139013 -139022.
AMA StyleFengmin Tang, Feng Gao, Zilong Wang. Driving Capability-Based Transition Strategy for Cooperative Driving: From Manual to Automatic. IEEE Access. 2020; 8 (99):139013-139022.
Chicago/Turabian StyleFengmin Tang; Feng Gao; Zilong Wang. 2020. "Driving Capability-Based Transition Strategy for Cooperative Driving: From Manual to Automatic." IEEE Access 8, no. 99: 139013-139022.
Considering the problem that does all the information have the same effect on the control performance of multi-agent system, this paper analyzes the influence of leader state on closed loop dynamics of formation theoretically. For the first time, it is proved that the leader information can mask out the effect of others and the closed loop dynamics of formation is equivalent to the leader follower topology if all followers can receive the leader information and others. Based on this new foundation, an estimator for leader state is designed using the sliding mode control theory. This estimator is independent of the dynamical functions of agents and only the minimum eigenvalue of topological matrix is required to ensure the convergence of estimation error even when leader runs dynamically. Several simulations have been conducted to further validate the correctness of this new theoretical foundation and the effectiveness of the estimator based formation control strategy.
Bo He; Feng Gao. Influence Analysis of Leader Information with Application to Formation Control of Multi-agent Systems. International Journal of Control, Automation and Systems 2020, 18, 3062 -3072.
AMA StyleBo He, Feng Gao. Influence Analysis of Leader Information with Application to Formation Control of Multi-agent Systems. International Journal of Control, Automation and Systems. 2020; 18 (12):3062-3072.
Chicago/Turabian StyleBo He; Feng Gao. 2020. "Influence Analysis of Leader Information with Application to Formation Control of Multi-agent Systems." International Journal of Control, Automation and Systems 18, no. 12: 3062-3072.
Vehicles are highly coupled and multi-degree nonlinear systems. The establishment of an appropriate vehicle dynamical model is the basis of motion planning for autonomous vehicles. With the development of autonomous vehicles from L2 to L3 and beyond, the automatic driving system is required to make decisions and plans in a wide range of speeds and on bends with large curvature. In order to make precise and high-quality control maneuvers, it is important to account for the effects of dynamical coupling in these working conditions. In this paper, a new single-coupled dynamical model (SDM) is proposed to deal with the various dynamical coupling effects by identifying and simplifying the complicated one. An autonomous vehicle motion planning problem is then formulated using the nonlinear model predictive control theory (NMPC) with the SDM constraint (NMPC-SDM). We validated the NMPC-SDM with hardware-in-the-loop (HIL) experiments to evaluate improvements to control performance by comparing with the planners original design, using the kinematic and single-track models. The comparative results show the superiority of the proposed motion planning algorithm in improving the maneuverability and tracking performance.
Dongfang Dang; Feng Gao; Qiuxia Hu. Motion Planning for Autonomous Vehicles Considering Longitudinal and Lateral Dynamics Coupling. Applied Sciences 2020, 10, 3180 .
AMA StyleDongfang Dang, Feng Gao, Qiuxia Hu. Motion Planning for Autonomous Vehicles Considering Longitudinal and Lateral Dynamics Coupling. Applied Sciences. 2020; 10 (9):3180.
Chicago/Turabian StyleDongfang Dang; Feng Gao; Qiuxia Hu. 2020. "Motion Planning for Autonomous Vehicles Considering Longitudinal and Lateral Dynamics Coupling." Applied Sciences 10, no. 9: 3180.
Detection of the traffic light is a key function of the automatic driving system for urban traffic. Considering the characteristics of classical and self-learning algorithms, a fusion logic is proposed to make up the shortcoming of learning algorithms by combining the known knowledge with the learning features to detect the red and yellow–green traffic light without turn indicator. The relationship of detection performance among different detectors is established analytically. Then the improvement of detection performance by fusion is analysed theoretically and optimised numerically. According to the analysis results, the hybrid detector is designed by using the colour information in hue-saturation-intensity to extract the candidate region, the hog feature to identify the shape information of traffic light classified by a support vector machine, and a comparatively simple convolutional neural network (CNN) with the classical AlexNet structure to act as the self-learned detector. The effectiveness of the hybrid method is validated by several comparative tests with single CNN detectors and other fusion methods on the training dataset, and the extensibility to new application conditions is evaluated by vehicle tests.
Feng Gao; Caimei Wang. Hybrid strategy for traffic light detection by combining classical and self‐learning detectors. IET Intelligent Transport Systems 2020, 14, 735 -741.
AMA StyleFeng Gao, Caimei Wang. Hybrid strategy for traffic light detection by combining classical and self‐learning detectors. IET Intelligent Transport Systems. 2020; 14 (7):735-741.
Chicago/Turabian StyleFeng Gao; Caimei Wang. 2020. "Hybrid strategy for traffic light detection by combining classical and self‐learning detectors." IET Intelligent Transport Systems 14, no. 7: 735-741.
Due to the limitation of current technologies and product costs, humans are still in the driving loop, especially for public traffic. One key problem of cooperative driving is determining the time when assistance is required by a driver. To overcome the disadvantage of the driver state-based detection algorithm, a new index called the correction ability of the driver is proposed, which is further combined with the driving risk to evaluate the driving capability. Based on this measurement, a degraded domain (DD) is further set up to detect the degradation of the driving capability. The log normal distribution is used to model the boundary of DD according to the bench test data, and an online algorithm is designed to update its parameter interactively to identify individual driving styles. The bench validation results show that the identification algorithm of the DD boundary converges finely and can reflect the individual driving characteristics. The proposed degradation detection algorithm can be used to determine the switching time from manual to automatic driving, and this DD-based cooperative driving system can drive the vehicle in a safe condition.
Feng Gao; Bo He; Yingdong He. Detection of Driving Capability Degradation for Human-Machine Cooperative Driving. Sensors 2020, 20, 1968 .
AMA StyleFeng Gao, Bo He, Yingdong He. Detection of Driving Capability Degradation for Human-Machine Cooperative Driving. Sensors. 2020; 20 (7):1968.
Chicago/Turabian StyleFeng Gao; Bo He; Yingdong He. 2020. "Detection of Driving Capability Degradation for Human-Machine Cooperative Driving." Sensors 20, no. 7: 1968.
The uncertainties arising from plant model and topologies have been a major challenge in multi-agent consensus control. This paper presents a distributed robust control method for an uncertain multi-agent system with eigenvalue-bounded topologies. The heterogeneity of node dynamics is described as the uncertainties of a linear model with a common certain part. The linear transformation method is adopted to decompose topologically coupled controllers. Then LMI (linear matrix inequalities) technique is used to numerically solve the distributed robust controller problem. It is proved that such a controller is robust stable under the condition that the topology is eigenvalue-bounded. The effectiveness of this method is validated by the simulation of a group of unmanned ground vehicles compared with the LQR controller.
Keqiang Li; Shengbo Eben Li; Feng Gao; Ziyu Lin; Jie Li; Qi Sun. Robust Distributed Consensus Control of Uncertain Multiagents Interacted by Eigenvalue-Bounded Topologies. IEEE Internet of Things Journal 2020, 7, 3790 -3798.
AMA StyleKeqiang Li, Shengbo Eben Li, Feng Gao, Ziyu Lin, Jie Li, Qi Sun. Robust Distributed Consensus Control of Uncertain Multiagents Interacted by Eigenvalue-Bounded Topologies. IEEE Internet of Things Journal. 2020; 7 (5):3790-3798.
Chicago/Turabian StyleKeqiang Li; Shengbo Eben Li; Feng Gao; Ziyu Lin; Jie Li; Qi Sun. 2020. "Robust Distributed Consensus Control of Uncertain Multiagents Interacted by Eigenvalue-Bounded Topologies." IEEE Internet of Things Journal 7, no. 5: 3790-3798.
Jianli Duan; Feng Gao; Yingdong He. Test Scenario Generation and Optimization Technology for Intelligent Driving Systems. IEEE Intelligent Transportation Systems Magazine 2020, 1 -1.
AMA StyleJianli Duan, Feng Gao, Yingdong He. Test Scenario Generation and Optimization Technology for Intelligent Driving Systems. IEEE Intelligent Transportation Systems Magazine. 2020; ():1-1.
Chicago/Turabian StyleJianli Duan; Feng Gao; Yingdong He. 2020. "Test Scenario Generation and Optimization Technology for Intelligent Driving Systems." IEEE Intelligent Transportation Systems Magazine , no. : 1-1.
To deal with the challenges caused by the weakness of wireless communication, this paper presents a distributed H∞ control strategy for platooning of automatic vehicles (AVs) connected by switching and undirected topologies. With the compensation of the powertrain nonlinearities by an inverse model, the node dynamics is described by a linear system with bounded uncertainty. Then the platoon system controlled by a distributed state-feedback controller is decomposed into multiple low order subsystems by applying the eigenvalue decomposition and linear transformation. The open gain of these subsystems dependends on the eigenvalues of the topological matrix. The sufficient condition for the H∞ performance of platoon is proved by using the invariant of signal amplitude of the linear transformation. A numerical way is further provided to solve the distributed controller by using the LMI approach. For this new synthesis method, only the bounds of the topological eigenvalues are necessary. And the designed state-feedback can control the platoon composed of disturbed nodes and interacted by uncertain even switching topologies in a satisfactory way. The effectiveness of this distributed H∞ control strategy is validated by comparative bench tests between nominal and disturbed conditions.
Feng Gao; Fan-Xia Lin; Bao Liu. Distributed H∞ Control Of Platoon Interacted by Switching and Undirected Topology. International Journal of Automotive Technology 2020, 21, 259 -268.
AMA StyleFeng Gao, Fan-Xia Lin, Bao Liu. Distributed H∞ Control Of Platoon Interacted by Switching and Undirected Topology. International Journal of Automotive Technology. 2020; 21 (1):259-268.
Chicago/Turabian StyleFeng Gao; Fan-Xia Lin; Bao Liu. 2020. "Distributed H∞ Control Of Platoon Interacted by Switching and Undirected Topology." International Journal of Automotive Technology 21, no. 1: 259-268.
Feng Gao; Caimei Wang; Caihong Li. A Combined Object Detection Method With Application to Pedestrian Detection. IEEE Access 2020, 8, 194457 -194465.
AMA StyleFeng Gao, Caimei Wang, Caihong Li. A Combined Object Detection Method With Application to Pedestrian Detection. IEEE Access. 2020; 8 ():194457-194465.
Chicago/Turabian StyleFeng Gao; Caimei Wang; Caihong Li. 2020. "A Combined Object Detection Method With Application to Pedestrian Detection." IEEE Access 8, no. : 194457-194465.
This paper proposes a new methodology based on the multi-port network theory to predict the vehicle-level electromagnetic compatibility performance. The original EMC problem is firstly converted to a network by separating the electrical large structures and electrical small components. The impedance is proposed to describe the coupling process of network to eliminate the influence of port impedance on network. Based on this network model, the relationship between the exciting sources and the sensitive components is set up using the multi-port network theory. Furthermore, some application problems, such as measurement of parameters, are also discussed. After validated by a bench test, this methodology for vehicle level electromagnetic compatibility was further applied to predict and improve the low frequency radiated emission of an electric vehicle. The application results show that it can be used to predict electromagnetic interference and analyze the main exciting source satisfactorily.
Feng Gao; Hanzhe Dai; Jiawei Qi; Zilong Wang. Vehicle-Level Electromagnetic Compatibility Prediction Based on Multi-Port Network Theory. International Journal of Automotive Technology 2019, 20, 1277 -1285.
AMA StyleFeng Gao, Hanzhe Dai, Jiawei Qi, Zilong Wang. Vehicle-Level Electromagnetic Compatibility Prediction Based on Multi-Port Network Theory. International Journal of Automotive Technology. 2019; 20 (6):1277-1285.
Chicago/Turabian StyleFeng Gao; Hanzhe Dai; Jiawei Qi; Zilong Wang. 2019. "Vehicle-Level Electromagnetic Compatibility Prediction Based on Multi-Port Network Theory." International Journal of Automotive Technology 20, no. 6: 1277-1285.
The wide application of electric vehicle (EV) brings us many challenges of electromagnetic compatibility (EMC). Automotive manufactures are obliged to ensure that their products comply with the EMC regulations. However, EV is a complicated system, which is composed of variety electromagnetic interferences (EMI), sensitive equipment and coupling paths. This poses great challenges to troubleshoot EMC problems efficiently especially at the early stage. This article proposes an electromagnetic topology based model and analysis method for vehicle‐level EMI prediction. This approach decomposes an EV into multiple subsystems and transforms the electromagnetic coupling paths into multi‐port networks connected by topological matrices. By this way, each part of the EMC model can be set up separately with different technologies and then vehicle‐level EMI is predicted by algebra calculation. The effectiveness of this method has been validated by comparing the predicted radiated emission at low frequency with the experimental results, and application to troubleshooting of the emission problem.
Feng Gao; Hanzhe Dai; Cunxue Wu; Zilong Wang. A topological approach to model and improve vehicle‐level electromagnetic radiation. International Journal of RF and Microwave Computer-Aided Engineering 2019, 29, 1 .
AMA StyleFeng Gao, Hanzhe Dai, Cunxue Wu, Zilong Wang. A topological approach to model and improve vehicle‐level electromagnetic radiation. International Journal of RF and Microwave Computer-Aided Engineering. 2019; 29 (10):1.
Chicago/Turabian StyleFeng Gao; Hanzhe Dai; Cunxue Wu; Zilong Wang. 2019. "A topological approach to model and improve vehicle‐level electromagnetic radiation." International Journal of RF and Microwave Computer-Aided Engineering 29, no. 10: 1.
In this paper, a car-following model considering the preceding vehicle type is proposed to describe the longitudinal driving behavior closer to reality. Based on the naturalistic driving data sampled in real traffic for more than half a year, the relation between ego vehicle velocity and relative distance was analyzed by a multi-variable Gaussian Mixture model, from which it is found that the driver following behavior is influenced by the type of leading vehicle. Then a Hidden Markov model was designed to identify the vehicle type. This car-following model was trained and tested by using the naturalistic driving data. It can identify the leading vehicle type, i.e., passenger car, bus, and truck, and predict the ego vehicle velocity and relative distance based on a series of limited historical data in real time. The experimental validation results show that the identification accuracy of vehicle type under the static and dynamical conditions are 96.6% and 83.1%, respectively. Furthermore, comparing the results with the well-known collision avoidance model and intelligent driver model show that this new model is more accurate and can be used to design advanced driver assist systems for better adaptability to traffic conditions.
Ping Wu; Feng Gao; Keqiang Li. A Vehicle Type Dependent Car-following Model Based on Naturalistic Driving Study. Electronics 2019, 8, 453 .
AMA StylePing Wu, Feng Gao, Keqiang Li. A Vehicle Type Dependent Car-following Model Based on Naturalistic Driving Study. Electronics. 2019; 8 (4):453.
Chicago/Turabian StylePing Wu; Feng Gao; Keqiang Li. 2019. "A Vehicle Type Dependent Car-following Model Based on Naturalistic Driving Study." Electronics 8, no. 4: 453.
To combat the variety uncertainties in topologies, dynamical models and disturbances, this paper presents a distributed sliding mode control strategy for formation control of multiple AVs. In this scheme, all collected information of each AV is used for its control and different perturbations are dealt with separately to reduce the contractions among them. Furthermore, a distributed adaptive algorithm is designed to replace the witching part for smoothness of control. The convergence of sliding surfaces of both two controllers is analysed theoretically. The sliding dynamics is affected by both the feedback and interaction topology. The existing decoupling method to handle the variety topologies can be used to synthesize the sliding dynamics. Finally, this approach has been applied to vehicular platooning and validated by several comparative simulations. The results show that the proposed method can control multiple AVs better than the state feedback strategy.
Feng Gao; Bao Liu; Jiawei Qi; Caimei Wang. Distributed sliding mode control for formation of multiple nonlinear AVs coupled by uncertain topology. SN Applied Sciences 2019, 1, 374 .
AMA StyleFeng Gao, Bao Liu, Jiawei Qi, Caimei Wang. Distributed sliding mode control for formation of multiple nonlinear AVs coupled by uncertain topology. SN Applied Sciences. 2019; 1 (4):374.
Chicago/Turabian StyleFeng Gao; Bao Liu; Jiawei Qi; Caimei Wang. 2019. "Distributed sliding mode control for formation of multiple nonlinear AVs coupled by uncertain topology." SN Applied Sciences 1, no. 4: 374.
The popularity of the electric vehicle (EV) brings us many challenges of electromagnetic compatibility (EMC). Automotive manufacturers are obliged to keep their products in compliance with EMC regulations. However, the EV is a complex system composed of various electromagnetic interferences (EMI), sensitive equipment and complicated coupling paths, which pose great challenges to the efficient troubleshooting of EMC problems. This paper presents an electromagnetic topology (EMT) based model and analysis method for vehicle-level EMI prediction, which decomposes an EV into multi-subsystems and transforms electromagnetic coupling paths into network parameters. This way, each part could be modelled separately with different technologies and vehicle-level EMI was able to be predicted by algebra calculations. The effectiveness of the proposed method was validated by comparing predicted vehicle-radiated emissions at low frequency with experimental results, and application to the troubleshooting of emission problems.
Cunxue Wu; Feng Gao; Hanzhe Dai; Zilong Wang. A Topology-Based Approach to Improve Vehicle-Level Electromagnetic Radiation. Electronics 2019, 8, 364 .
AMA StyleCunxue Wu, Feng Gao, Hanzhe Dai, Zilong Wang. A Topology-Based Approach to Improve Vehicle-Level Electromagnetic Radiation. Electronics. 2019; 8 (3):364.
Chicago/Turabian StyleCunxue Wu; Feng Gao; Hanzhe Dai; Zilong Wang. 2019. "A Topology-Based Approach to Improve Vehicle-Level Electromagnetic Radiation." Electronics 8, no. 3: 364.
To overcome the challenges arising from the weakness of wireless communication, this paper presents a distributed H∞ control method for multi-AVs connected by an uncertain and switching topology in a platoon. After compensating for the powertrain nonlinearities, we model the node dynamics as a linear uncertain system. By applying the eigenvalue decomposition and linear transformation, the platoon system is decomposed to multiple low order subsystems depending on the eigenvalues of the topological matrix. The sufficient condition ensuring the robust performance of the platoon is presented by using the invariant of signal amplitude of the linear transformation. Then a numerical method is provided to solve the state feedback controller by using the LMI scheme. Only the bounds of the topological eigenvalues are necessary for this new synthesis method and the designed controller can govern the platoon composed of disturbed nodes and interacting by uncertain even switching topologies in a satisfactory way. The effectiveness of this distributed H∞ control strategy is validated by comparative bench tests between nominal and disturbed conditions.
Feng Gao; Dongfang Dang; Qiuxia Hu; Yingdong He. Distributed H∞ Control of AVs Interacting by Uncertain and Switching Topology in a Platoon. Journal of Advanced Transportation 2019, 2019, 1 -13.
AMA StyleFeng Gao, Dongfang Dang, Qiuxia Hu, Yingdong He. Distributed H∞ Control of AVs Interacting by Uncertain and Switching Topology in a Platoon. Journal of Advanced Transportation. 2019; 2019 ():1-13.
Chicago/Turabian StyleFeng Gao; Dongfang Dang; Qiuxia Hu; Yingdong He. 2019. "Distributed H∞ Control of AVs Interacting by Uncertain and Switching Topology in a Platoon." Journal of Advanced Transportation 2019, no. : 1-13.
The application of wireless communication to platooning brings such challenges as information delay and varieties of interaction topologies. To compensate for the information delay, a state predictor based control strategy is proposed, which transmits the future information of nodes instead of current values. Based on the closed loop dynamics of platoon with state predictor and feedback controller, a decoupling strategy is presented to analysis and design the platoon control system with lower order by adopting the eigenvalue decomposition of topological matrix. A numerical method based on LMI (Linear Matrix Inequality) is provided to find the required robust performance controller. Moreover, the influence of information delay on performance is studied theoretically and it is found that the tolerable maximum delay is determined by the maximum topological eigenvalue. The effectiveness of the proposed strategy is validated by several comparative simulations under various conditions with other methods.
Bao Liu; Feng Gao; Yingdong He; Caimei Wang. Robust Control of Heterogeneous Vehicular Platoon with Non-Ideal Communication. Electronics 2019, 8, 207 .
AMA StyleBao Liu, Feng Gao, Yingdong He, Caimei Wang. Robust Control of Heterogeneous Vehicular Platoon with Non-Ideal Communication. Electronics. 2019; 8 (2):207.
Chicago/Turabian StyleBao Liu; Feng Gao; Yingdong He; Caimei Wang. 2019. "Robust Control of Heterogeneous Vehicular Platoon with Non-Ideal Communication." Electronics 8, no. 2: 207.