郑冬冬 - 北京理工大学

个人简介

教育与工作经历 2006.09-2010.07 北京理工大学自动化学院自动化学士 2010.09-2012.07 北京理工大学自动化学院控制工程硕士 2012.09-2017.05 加拿大康考迪亚大学工程与计算科学学院机械工程博士 2017.11-2019.04 新加坡国立大学生物医疗工程博士后 2019.05-2020.08 瑞士洛桑联邦理工学院工程学院机械工程博士后 2020.10-现在北京理工大学自动化学院智信所预聘助理教授近5年承担的科研项目机器人制造系统中的视觉/触觉融合伺服控制加拿大国家科学与工程研究委员会 C$170,000 2015-2017 参与压电驱动器及扑翼微型航空器的机制研究及优化新加坡国立大学 S$300,000 2017-2019 参与分布式能源系统的暂态控制研究瑞士国家科学基金 Fr.400,000 2019-2020 参与大负载多电机驱动系统的多时间尺度建模及控制国家自然科学基金委￥300,000 2022-2024 主持

研究领域

基于深度学习/神经网络的无人机/无人车/机器人自适应控制微电网/新能源装置控制

近期论文

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D.-D. Zheng, S. S. Madani and A. Karimi*, “Data-driven distributed online learning control for islanded microgrids,” IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 2022. S. Hu, X. Ren*, M. Wang, D.-D. Zheng, “Observer-based optimal adaptive control for multimotor driving servo systems,” International Journal of Robust and Nonlinear Control, vol. 31, pp. 9745-9766, 2021. P. Wang, X. Ren*, D.-D. Zheng, “Event-triggered resilient control for cyber-physical systems under periodic DoS jamming attacks,” Information Sciences, vol. 577, pp. 541-556, 2021. Y. Cheng, X. Ren*, D.-D. Zheng and L. Li, “Non-Linear Bandwidth Extended-State-Observer Based Non-Smooth Funnel Control for Motor-Drive Servo Systems,” IEEE Transactions on Industrial Electronics, vol. 69, no. 6, pp. 6215-6224, June 2022. K. Guo*, D.-D. Zheng*, and Jianyong Li, “Optimal Bounded Ellipsoid Identification With Deterministic and Bounded Learning Gains: Design and Application to Euler-Lagrange Systems,” IEEE Transactions on Cybernetics, 2021. D.-D. Zheng, Pengcheng Li, W.-F. Xie, and Dan Li, “Identification and control of flexible joint robot using multitime-scale neural network,” Journal of Shanghai Jiao Tong University (Science), vol. 25, no. 5, pp. 553-560, 2020. Y. Zhang, M. Wang, Y. Cheng, D.-D. Zheng and Y. Peng, “A stick-slip/inchworm hybrid rotary piezo motor based on a symmetric triangular driving mechanism,” Applied Physics Letters, vol. 115, no. 13, pp. 131904, 2019. K. Guo, Y. Pan, D.-D. Zheng and H. Yu, “Composite learning control of robotic systems: A least squares modulated approach,” Automatica, vol. 111, 2020. D.-D. Zheng, Y. Pan, K. Guo and H. Yu, “Identification and control of nonlinear systems using neural networks: a singularity-free approach,” IEEE Transactions on Neural Networks and Learning Systems, vol. 30, no. 9, pp. 2696–2706, Sept. 2019. Q. Chen, X. Ren, J. Na, and D.-D. Zheng, “Adaptive robust finite-time neural control of uncertain PMSM servo system with nonlinear dead zone,” Neural Computing and Applications, vol. 28, no. 12, pp. 3725–3736, 2017. D.-D. Zheng, W.-F. Xie, T. Chai, and Z. Fu, “Identification and trajectory tracking control of nonlinear singularly perturbed systems,” IEEE Transactions on Industrial Electronics, vol. 64, no. 5, pp. 3737–3747, May 2017. D.-D. Zheng, W.-F. Xie, X. Ren, and J. Na, “Identification and control for singularly perturbed systems using multitime-scale neural networks,” IEEE Transactions on Neural Networks and Learning Systems, vol. 28, no. 2, pp. 321–333, Feb. 2017. Z.-J. Fu, W.-F. Xie, S. Rakheja, and D.-D. Zheng, “Adaptive optimal control of unknown nonlinear systems with different time scales,” Neurocomputing, vol. 238, pp. 179–190, 2017. D.-D. Zheng, Z. Fu, W.-F. Xie, and W. Luo, “Indirect adaptive control of nonlinear system via dynamic multilayer neural networks with multi-time scales,” International Journal of Adaptive Control and Signal Processing, vol. 29, no. 4, pp. 505–523, 2015. J. Na, X. Ren, and D.-D. Zheng, “Adaptive control for nonlinear pure-feedback systems with high-order sliding mode observer,” IEEE Transactions on Neural Networks and Learning Systems, vol. 24, no. 3, pp. 370–382, Mar. 2013.