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Volume 8 Issue 1
Jan.  2021

IEEE/CAA Journal of Automatica Sinica

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Mohammad Al-Sharman, David Murdoch, Dongpu Cao, Chen Lv, Yahya Zweiri, Derek Rayside and William Melek, "A Sensorless State Estimation for A Safety-Oriented Cyber-Physical System in Urban Driving: Deep Learning Approach," IEEE/CAA J. Autom. Sinica, vol. 8, no. 1, pp. 169-178, Jan. 2021. doi: 10.1109/JAS.2020.1003474
Citation: Mohammad Al-Sharman, David Murdoch, Dongpu Cao, Chen Lv, Yahya Zweiri, Derek Rayside and William Melek, "A Sensorless State Estimation for A Safety-Oriented Cyber-Physical System in Urban Driving: Deep Learning Approach," IEEE/CAA J. Autom. Sinica, vol. 8, no. 1, pp. 169-178, Jan. 2021. doi: 10.1109/JAS.2020.1003474

A Sensorless State Estimation for A Safety-Oriented Cyber-Physical System in Urban Driving: Deep Learning Approach

doi: 10.1109/JAS.2020.1003474
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  • In today’s modern electric vehicles, enhancing the safety-critical cyber-physical system (CPS)’s performance is necessary for the safe maneuverability of the vehicle. As a typical CPS, the braking system is crucial for the vehicle design and safe control. However, precise state estimation of the brake pressure is desired to perform safe driving with a high degree of autonomy. In this paper, a sensorless state estimation technique of the vehicle’s brake pressure is developed using a deep-learning approach. A deep neural network (DNN) is structured and trained using deep-learning training techniques, such as, dropout and rectified units. These techniques are utilized to obtain more accurate model for brake pressure state estimation applications. The proposed model is trained using real experimental training data which were collected via conducting real vehicle testing. The vehicle was attached to a chassis dynamometer while the brake pressure data were collected under random driving cycles. Based on these experimental data, the DNN is trained and the performance of the proposed state estimation approach is validated accordingly. The results demonstrate high-accuracy brake pressure state estimation with RMSE of 0.048 MPa.

     

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  • [1]
    C. Lv, Y. H. Liu, X. S. Hu, H. Y. Guo, D. P. Cao, and F. Y. Wang, “Simultaneous observation of hybrid states for cyber-physical systems: A case study of electric vehicle powertrain,” IEEE Trans. Cybern., vol. 48, no. 8, pp. 2357–2367, Aug. 2018. doi: 10.1109/TCYB.2017.2738003
    [2]
    J. Lee, B. Bagheri, and H. A. Kao, “A cyber-physical systems architecture for industry 4.0-based manufacturing systems,” Manuf. Lett., vol. 3, pp. 18–23, Jan. 2015. doi: 10.1016/j.mfglet.2014.12.001
    [3]
    G. Xiong, F. H. Zhu, X. W. Liu, X. S. Dong, W. L. Huang, S. H. Chen, and K. Zhao, “Cyber-physical-social system in intelligent transportation,” IEEE/CAA J. Autom. Sinica, vol. 2, no. 3, pp. 320–333, Jul. 2015. doi: 10.1109/JAS.2015.7152667
    [4]
    L. Li, X. Y. Peng, F. Y. Wang, D. P. Cao, and L. X. Li, “A situation-aware collision avoidance strategy for car-following,” IEEE/CAA J. Autom. Sinica, vol. 5, no. 5, pp. 1012–1016, Sept. 2018. doi: 10.1109/JAS.2018.7511198
    [5]
    Y. T. Li, C. Lv, J. Z. Zhang, Y. Zhang, and W. J. Ma, “High-precision modulation of a safety-critical cyber-physical system: Control synthesis and experimental validation,” IEEE/ASME Trans. Mech., vol. 23, no. 6, pp. 2599–2608, Dec. 2018. doi: 10.1109/TMECH.2018.2833542
    [6]
    F. Y. Wang, N. N. Zheng, D. P. Cao, C. M. Martinez, L. Li, and T. Liu, “Parallel driving in CPSS: A unified approach for transport automation and vehicle intelligence,” IEEE/CAA J. Autom. Sinica, vol. 4, no. 4, pp. 577–587, Sept. 2017. doi: 10.1109/JAS.2017.7510598
    [7]
    T. Liu, H. L. Yu, H. Y. Guo, Y. C. Qin, and Y. Zou, “Online energy management for multimode plug-in hybrid electric vehicles,” IEEE Trans. Ind. Inform., vol. 15, no. 7, pp. 4352–4361, Jul. 2019. doi: 10.1109/TII.2018.2880897
    [8]
    T. Liu, B. Tian, Y. F. Ai, and F. Y. Wang, “Parallel reinforcement learning-based energy efficiency improvement for a cyber-physical system,” IEEE/CAA J. Autom. Sinica, vol. 7, no. 2, pp. 617–626, Mar. 2020. doi: 10.1109/JAS.2020.1003072
    [9]
    T. Liu, B. Wang, and C. L. Yang, “Online Markov Chain-based energy management for a hybrid tracked vehicle with speedy Q-learning,” Energy, vol. 160, pp. 544–555, Oct. 2018. doi: 10.1016/j.energy.2018.07.022
    [10]
    J. J. Castillo, J. A. Cabrera, A. J. Guerra, and A. Simón, “A novel electrohydraulic brake system with tire-road friction estimation and continuous brake pressure control,” IEEE Trans. Ind. Electron., vol. 63, no. 3, pp. 1863–1875, Mar. 2016. doi: 10.1109/TIE.2015.2494041
    [11]
    A. Dadashnialehi, A. Bab-Hadiashar, Z. W. Cao, and A. Kapoor, “Intelligent sensorless antilock braking system for brushless in-wheel electric vehicles,” IEEE Trans. Ind. Electron., vol. 62, no. 3, pp. 1629–1638, Mar. 2015. doi: 10.1109/TIE.2014.2341601
    [12]
    A. Fazeli, M. Zeinali, and A. Khajepour, “Application of adaptive sliding mode control for regenerative braking torque control,” IEEE/ASME Trans. Mech., vol. 17, no. 4, pp. 745–755, Aug. 2012. doi: 10.1109/TMECH.2011.2129525
    [13]
    C. Q. Qiu, G. L. Wang, M. Y. Meng, and Y. J. Shen, “A novel control strategy of regenerative braking system for electric vehicles under safety critical driving situations,” Energy, vol. 149, pp. 329–340, Apr. 2018. doi: 10.1016/j.energy.2018.02.046
    [14]
    L. H. Wang, Z. F. Zhan, X. Yang, Q. M. Wang, Y. F. Zhang, L. Zheng, and G. Guo, “Development of BP neural network PID controller and its application on autonomous emergency braking system,” in Proc. IEEE Intelligent Vehicles Symp., Changshu, China, 2018.
    [15]
    J. Y. Tan, C. L. Xu, L. Li, F. Y. Wang, D. P. Cao, and L. X. Li, “Guidance control for parallel parking tasks,” IEEE/CAA J. Autom. Sinica, vol. 7, no. 1, pp. 301–306, Jan. 2020. doi: 10.1109/JAS.2019.1911855
    [16]
    Y. F. Li, C. C. Tang, S. Peeta, and Y. B. Wang, “Integral-sliding-mode braking control for a connected vehicle platoon: Theory and application,” IEEE Trans. Ind. Electron., vol. 66, no. 6, pp. 4618–4628, Jun. 2019. doi: 10.1109/TIE.2018.2864708
    [17]
    A. Dadashnialehi, A. Bab-Hadiashar, Z. W. Cao, and R. Hoseinnezhad, “Reliable EMF-sensor-fusion-based antilock braking system for BLDC motor in-wheel electric vehicles,” IEEE Sens. Lett., vol. 1, no. 3, pp. 6000304, Jun. 2017.
    [18]
    N. G. Ding and X. F. Zhan, “Model-based recursive least square algorithm for estimation of brake pressure and road friction, “ in Proc. FISITA World Automotive Congr., Berlin, Germany, 2012.
    [19]
    G. R. Jiang, X. L. Miao, Y. H. Wang, J. Chen, D. L. Li, L. F. Liu, and F. Muhammad, “Real-time estimation of the pressure in the wheel cylinder with a hydraulic control unit in the vehicle braking control system based on the extended Kalman filter,” Proc Inst. Mech. Eng.,Part D:J. Autom. Eng., vol. 231, no. 10, pp. 1340–1352, Sept. 2016.
    [20]
    L. Li, J. Song, Z. Q. Han, and L. Kong, “Hydraulic model and inverse model for electronic stability program online control system,” Chin. J. Mech. Eng., vol. 44, no. 2, pp. 139–144, Feb. 2008. doi: 10.3901/JME.2008.02.139
    [21]
    J. Z. Zhang, C. Lv, J. F. Gou, and D. C. Kong, “Cooperative control of regenerative braking and hydraulic braking of an electrified passenger car,” Proc. Inst. Mech. Eng.,Part D:J. Autom. Eng., vol. 226, no. 10, pp. 1289–1302, Oct. 2012. doi: 10.1177/0954407012441884
    [22]
    K. O’Dea, “Anti-lock braking performance and hydraulic brake pressure estimation,” Technical Paper 2005-01-1061, SAE, 2005.
    [23]
    C. Lv, Y. Xing, J. Z. Zhang, X. X. Na, Y. T. Li, T. Liu, D. P. Cao, and F. Y. Wang, “Levenberg-Marquardt backpropagation training of multilayer neural networks for state estimation of a safety-critical cyber-physical system,” IEEE Trans. Ind. Inform., vol. 14, no. 8, pp. 3436–3446, Aug. 2018. doi: 10.1109/TII.2017.2777460
    [24]
    N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: A simple way to prevent neural networks from overfitting,” J. Mach. Learn. Res., vol. 15, pp. 1929–1958, Jan. 2014.
    [25]
    Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, May 2015. doi: 10.1038/nature14539
    [26]
    I. Gooodfellow, Y. Bengio, and A. Courville, Deep Learning. Cambridge: MIT Press, 2016.
    [27]
    A. L. Maas, A. Y. Hannun, and A. Y. Ng, “Rectifier nonlinearities improve neural network acoustic models,” in Proc. 30th Int. Conf. Machine Learning, Atlanta, USA, 2013.
    [28]
    P. Baldi and P. Sadowski, “The dropout learning algorithm,” Artif. Intell., vol. 210, pp. 78–122, May 2014. doi: 10.1016/j.artint.2014.02.004
    [29]
    G. E. Dahl, T. N. Sainath, and G. E. Hinton, “Improving deep neural networks for LVCSR using rectified linear units and dropout,” in Proc. IEEE Int. Conf. Acoustics, Speech and Signal Processing, Vancouver, Canada, 2013.
    [30]
    C. G. Yan, H. T. Xie, D. B. Yang, J. Yin, Y. D. Zhang, and Q. H. Dai, “Supervised hash coding with deep neural network for environment perception of intelligent vehicles,” IEEE Trans. Intell. Trans. Syst., vol. 19, no. 1, pp. 284–295, Jan. 2018. doi: 10.1109/TITS.2017.2749965
    [31]
    L. Xu, “An overview and perspectives on bidirectional intelligence: Lmser duality, double IA harmony, and causal computation,” IEEE/CAA J. Autom. Sinica, vol. 6, no. 4, pp. 865–893, Jul. 2019. doi: 10.1109/JAS.2019.1911603
    [32]
    A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” in Proc. 25th Int. Conf. Neural Information Processing Systems, Granada, Spain, 2012.
    [33]
    C. Szegedy, W. Liu, Y. Q. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition, Boston, USA, 2015.
    [34]
    J. Redmon and A. Farhadi, “YOLO9000: Better, faster, stronger,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition, Honolulu, USA, 2017.
    [35]
    W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C. Y. Fu, and A. C. Berg, “SSD: Single shot MultiBox detector,” in Proc. 14th European Conf. Computer Vision, Amsterdam, The Netherlands, 2016.
    [36]
    F. B. Naeini, A. M. Alali, R. Al-Husari, A. Rigi, M. K. Al-Sharman, D. Makris, and Y. Zweiri, “A novel dynamic-vision-based approach for tactile sensing applications,” IEEE Trans. Instrum. Meas., vol. 69, no. 5, pp. 1881–1893, May 2020. doi: 10.1109/TIM.2019.2919354
    [37]
    W. N. Lu, B. Liang, Y. Cheng, D. S. Meng, J. Yang, and T. Zhang, “Deep model based domain adaptation for fault diagnosis,” IEEE Trans. Ind. Electron., vol. 64, no. 3, pp. 2296–2305, Mar. 2017. doi: 10.1109/TIE.2016.2627020
    [38]
    M. Xia, T. Li, L. Xu, L. Z. Liu, and C. W. de Silva, “Fault diagnosis for rotating machinery using multiple sensors and convolutional neural networks,” IEEE/ASME Trans. Mech., vol. 23, no. 1, pp. 101–110, Feb. 2018. doi: 10.1109/TMECH.2017.2728371
    [39]
    J. Pan, Y. Y. Zi, J. L. Chen, Z. T. Zhou, and B. Wang, “LiftingNet: A novel deep learning network with layerwise feature learning from noisy mechanical data for fault classification,” IEEE Trans. Ind. Electron., vol. 65, no. 6, pp. 4973–4982, Jun. 2018. doi: 10.1109/TIE.2017.2767540
    [40]
    H. Oh, J. H. Jung, B. C. Jeon, and B. D. Youn, “Scalable and unsupervised feature engineering using vibration-imaging and deep learning for rotor system diagnosis,” IEEE Trans. Ind. Electron., vol. 65, no. 4, pp. 3539–3549, Apr. 2018. doi: 10.1109/TIE.2017.2752151
    [41]
    L. Yao and Z. Q. Ge, “Deep learning of semisupervised process data with hierarchical extreme learning machine and soft sensor application,” IEEE Trans. Ind. Electron., vol. 65, no. 2, pp. 1490–1498, Feb. 2018. doi: 10.1109/TIE.2017.2733448
    [42]
    S. C. Gao, M. C. Zhou, Y. R. Wang, J. J. Cheng, H. Yachi, and J. H. Wang, “Dendritic neuron model with effective learning algorithms for classification, approximation, and prediction,” IEEE Trans. Neural Netw. Learn. Syst., vol. 30, no. 2, pp. 601–614, Feb. 2019. doi: 10.1109/TNNLS.2018.2846646
    [43]
    M. K. Al-Sharman, Y. Zweiri, M. A. K. Jaradat, R. Al-Husari, D. Gan, and L. D. Seneviratne, “Deep-learning-based neural network training for state estimation enhancement: Application to attitude estimation,” IEEE Trans. Instrum. Meas., vol. 69, no. 1, pp. 24–34, Jan. 2020. doi: 10.1109/TIM.2019.2895495
    [44]
    J. Schmidhuber, “Deep learning in neural networks: An overview,” Neural Netw., vol. 61, pp. 85–117, Jan. 2015. doi: 10.1016/j.neunet.2014.09.003
    [45]
    Y. Gal and Z. Ghahramani, “Dropout as a Bayesian approximation: Representing model uncertainty in deep learning,” in Proc. 33rd Int. Conf. Machine Learning, New York, USA, 2016.
    [46]
    D. P. Kingma and J. L. Ba, “Adam: A method for stochastic optimization,” in Proc. 3rd Int. Conf. Learning Representations, San Diego, USa, 2015.
    [47]
    Y. Yuan, J. Z. Zhang, Y. T. Li, and C. Li, “A novel regenerative electrohydraulic brake system: Development and hardware-in-loop tests,” IEEE Trans. Veh. Technol., vol. 67, no. 12, pp. 11440–11452, Dec. 2018. doi: 10.1109/TVT.2018.2872030
    [48]
    C. Lv, Y. Xing, C. Lu, Y. H. Liu, H. Y. Guo, H. B. Gao, and D. P. Cao, “Hybrid-learning-based classification and quantitative inference of driver braking intensity of an electrified vehicle,” IEEE Trans. Veh. Technol., vol. 67, no. 7, pp. 5718–5729, Jul. 2018.
    [49]
    M. K. S. Al-Sharman, “Auto takeoff and precision landing using integrated GPS/INS/Optical flow solution,” M.S. thesis, American University of Sharjah, Sharjah, UAE, 2015.
    [50]
    H. Y. Guo, H. P. Guo, H. Chen, C. Lv, H. J. Wang, and S. Q. Yang, “Vehicle dynamic state estimation: State of the art schemes and perspectives,” IEEE/CAA J. Autom. Sinica, vol. 5, no. 2, pp. 418–431, Mar. 2018. doi: 10.1109/JAS.2017.7510811
    [51]
    M. K. S. Al-Sharman, “Attitude estimation for a small-scale flybarless helicopter,” in Multisensor Attitude Estimation: Fundamental Concepts and Applications, H. Fourati, D. E. C. Belkhiat, and K. Iniewski, Eds. Boca Raton: CRC Press, 2016, pp. 513–528.
    [52]
    K. Saadeddin, M. F. Abdel-Hafez, and M. A. Jarrah, “Estimating vehicle state by GPS/IMU fusion with vehicle dynamics,” J. Intell. Robot. Syst., vol. 74, no. 1–2, pp. 147–172, Apr.–Jun. 2014. doi: 10.1007/s10846-013-9960-1
    [53]
    M. K. Al-Sharman, M. A. Al-Jarrah, and M. Abdel-Hafez, “Auto takeoff and precision terminal-phase landing using an experimental optical flow model for Global Positioning System/Inertial Navigation System enhancement,” ASCE-ASME J. Risk Uncertainty Eng. Syst. B,Mech. Eng., vol. 5, no. 1, pp. 011001, Mar. 2019.
    [54]
    J. C. Principe, N. R. Euliano, and W. C. Lefebvre, Neural and Adaptive Systems: Fundamentals through Simulations. New York, USA: Wiley, 2000.

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    Highlights

    • A sensorless novel deep-learning-based algorithm is developed for brake pressure state estimation of an electric vehicle.
    • This state estimation technique uses current DL techniques and functions, such as dropout and ReLU to provide overfitting-free models of the state estimator.
    • The implementation of the proposed network is based on experimental data acquired using a real experimental vehicle testing environment.
    • Compared with conventional training methods, the proposed approach demonstrates more accurate brake pressure state estimation with RMSE errors of 0.048 MPa.
    • The proposed deep learning structure is expandable, hence, it can estimate other EV states in urban and high-way environments.

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