工学学士 (自动化系), 海军航空工程学院, 中国, 1986;
工学硕士 (自动化系), 海军航空工程学院, 中国, 1989;
工学博士 (计算机科学与技术系), 清华大学, 中国, 1997.
智能技术与系统国家重点实验室: 副主任;
IEEE: 高级会员;
中国人工智能学会: 理事 ;
中国人工智能学会: 智能控制与智能管理专业委员会副主任兼秘书长;
IEEE控制系统协会: 智能控制技术委员会委员;
Mechatronics, IEEE Trans. on Neural Networks, Soft Computing: 编委;
智能技术学报: 编委;
Information Science: 客座编辑;
中国科学: 客座编辑.
智能控制, 机器人与飞行器的导航与控制
网络控制系统
人工认知系统的信息感知和处理.
我从事非线性系统的神经模糊建模、控制与滤波研究十余年。在国家自然科学基金、973项目、863项目和国防预研项目的资助下，我在非线性系统的神经模糊稳定自适应控制和鲁棒控制、马尔可夫（Markov）跳变系统的滤波等方面取得了一系列研究成果，具体包括：
1. 非线性系统的神经模糊稳定自适应控制。我提出了神经变结构控制和神经模糊动态逆的设计思想，建立了较为完整的机器人神经网络稳定自适应控制的理论与方法，并完成了主要理论方法的实验验证。代表性工作获第十八届Choon-Gang国际学术一等奖和IEEE CSS 北京分会优秀论文奖。
2. 多时标非线性系统和马尔可夫跳变系统的控制与滤波。我从理论上证明柔性机械臂、飞翼式飞机、大型柔性空间结构等一大类多时标非线性系统可以采用模糊奇异摄动模型逼近的存在性和充分性，并给出其在给定逼近精度下的必要条件。我提出的基于摄动参数无关和相关解的自适应和鲁棒控制方法为实现该类系统的高精度和高稳定度控制开拓了新的途径。针对实际系统中常遇到的饱和效应、模态不可测等困难，我给出了有效的控制与滤波方法。研究成果应邀到本领域2006年IEEE智能系统设计与应用国际会议上做大会报告。
3. 面向需求的应用技术研究。我建立了国内第一个柔性双臂空间机器人和面向在轨服务的遥操作地面实验系统。我在空间机器人动力学参数的在轨辨识、抑振轨迹规划、位置/力混合控制、以及面向在轨服务的遥操作双边控制和共享控制技术方面取得创新性成果，申请受理发明专利8项、授权3项，并应邀在2009年IEEE测量技术与机械学自动化国际会议上做大会报告。
近年来，我共在国际期刊发表论文58篇（SCI收录52篇），这些论文在Web of Science网络版中他引300余次。我的专著《机械手神经网络稳定自适应控制的理论与方法》被高等教育出版社于2005年出版，专著《空间机器人学：动力学、规划与控制》即将被清华大学出版社出版，译著《机器人学导论》已为三十多所大学作为教材使用，获得好评。
国家杰出青年基金 (2006);
教育部新世纪人才奖 (2004);
第十八届Choon-Gang国际学术奖 (2003);
教育部提名国家科技进步二等奖——柔性连杆机械臂实验平台及其系统建模与控制 (2002);
北京市科学技术进步二等奖——非线性系统神经模糊稳定自适应控制与鲁棒控制的理论与方法(2004);
全国优秀博士论文奖 (2000).
[1] 孙富春, 孙增圻, 张钹著. 机械手神经网络稳定自适应控制的理论与方法. 高等教育出版社, 2005.1.
[2] F. C. Sun and F. G. Wu. Chapter 8: A discrete-time jump fuzzy system approach to networked control system design. In Networked Control Systems: Theory and Applications，Eds. Fei-YueWang & Derong Liu, West Publishing Company: March 2008.
[3] F. C. Sun, Y. Tan and C. Wang. Special issue on pattern recognition and information processing using neural networks. Soft Computing, 2010, 14(2): 101-102.
[4] L. Lee and F. C. Sun. The direct adaptive control based on the singularly perturbed model with the unknown consequence. International Journal of Control Automation & Systems, 2010, 8(2): 38-243.
[5] H. Q. Wang and F. C. Sun. An unbiased LSSVM model for classification and regression. Soft Computing, 2010, 14(2): 171-180.
[6] N. Chen, F. C.Sun and L. G, Ding. An adaptive PNN-DS approach to classification using multi-sensor information fusion. Neural Computing & Applications, 2009, 18(5):455-467.
[7] H. Wu, F. C. Sun and H. P. Liu. Fuzzy particle filtering for uncertain systems. IEEE Trans. Fuzzy Systems, 2008, 16(5):114-119.
[8] F. C. Sun, L. Li, H. X. Li and H. P. Liu. Neuro-fuzzy dynamic-inversion- based adaptive control for robotic manipulators-discrete time case. IEEE Trans. On Industrial Electronics, 2007, 54(3): 1342-1351.
[9] W. Hao, and F. C. Sun. Adaptive kriging control of discrete-time nonlinear systems. IEE Proceedings- Control Theory and Applications, 2007, 1(3):646-656.
[10] Y. G. Tang, F. C. Sun and Z. Q. Sun. Neural network control of flexible-link manipulators using sliding mode. Neurocomputing, 2006, 70:288-295.
[11] F. C. Sun, Z. Q. Sun and H. X. Li. Stable adaptive controller design of robotic manipulators via neuro-fuzzy dynamic inversion. Journal of Robotic Systems, 2005, 2005, 22(12):809-819.
[12] F. C. Sun, H. P. Liu and K. Z. He. Reduced-order H∞ filtering for linear systems with Markovian jump parameters. Systems and Control Letters, 2005，54(8):739-746.
[13] H. P. Liu, F. C. Sun and Z. Q. Sun. Stability analysis and synthesis of fuzzy singularly perturbed systems. IEEE Trans. on Fuzzy Systems, 2005, 13(2):273-284.
[14] F. C. Sun, H. P. Liu and Z. Q. Sun. Comments on ‘Constrained controller design of discrete Takagi-Sugeno fuzzy models’. Fuzzy Sets and Systems, 2004, 146(2): 473-476.
[15]H. P. Liu, F. C. Sun, K. Z. He and Z. Q. Sun. Design of reduced-order H∞ filter for Markovian jump linear systems with time-delay. IEEE Trans. on Circuits and Systems, Part II ---- Express Briefs, 2004, 51(11)：607-612.
[16] F. C. Sun, Z. Q. Sun and L. Li. Adaptive dynamic neuro-fuzzy controller design of robotic manipulators. Fuzzy Sets and Systems, 2003, 134(1): 117-133.
[17]F. C. Sun and Z. Q. Sun. Hybrid neuro-fuzzy adaptive control for flexible-link manipulators. International Journal of Fuzzy Systems, 2003, 5(2): 89-97.
[18] F. C. Sun, H. X. Li and L. Li. Robot discrete adaptive control based on dynamic inversion using dynamical neural networks. Automatica, 2002, 38(11): 1977-1983.
[19] F. C. Sun, Z. Q. Sun and P. Y. Woo. Neural network-based adaptive controller design of robot manipulators with an observer. IEEE Trans. on Neural Networks, 2001, 12(1): 54-67.
[20] F. C. Sun, Z. Q. Sun, Y. B. Chen and R. J. Zhang. Neural adaptive tracking controller for robot manipulators with unknown dynamics. IEE Proceedings, Part D: Control Theory and Applications, 2000, 147(3): 366-370.
[21] F. C. Sun, Z. Q. Sun and F. Gary. An adaptive fuzzy controller based on sliding mode for robot manipulators. IEEE Trans. on Systems, Man, and Cybernetics. (Part B: Cybernetics), 1999, 29(5): 661-667.
[22] F. C. Sun, Z. Q. Sun, Y. Y. Zhu and W. J. Lu. Stable neuro-adaptive control for robots with unknown dynamics. Journal of Intelligent and Robotic Systems. 1999, 26(1): 91-100.
[23] F. C. Sun and Z. Q. Sun. Robot adaptive control based on dynamic inversion using dynamical neural networks. Machine Intelligence and Robot Control, 1999, 1(2): 71-78.
[24] F. C. Sun, Z. Q. Sun, and P. Y. Woo. Stable neural network-based adaptive control for sampled-data nonlinear systems. IEEE Trans. on Neural Networks, 1998, 9(5): 956-968.
[25] F. C. Sun, Z. Q. Sun, M. H. Guo and R.J. Zhang. A stable neural network-based adaptive controller for robot manipulators. International Journal of Intelligent and Robotic systems, 1998, 2(3): 413-432.
[26] F. C. Sun and Z. Q. Sun. Stable sampled-data adaptive control of robots using neural network models. Journal of Intelligent and Robotic Systems. 1997, 20 (2): 131-155.
[27]H. Wu, F. C. Sun, Z. Q. Sun and L. C. Wu. Optimal trajectory planning of a flexible dual-arm space robot with vibration reduction. Journal of Intelligent and Robotic Systems, 2004, 40(2):147-163.
[28] N. Zhao and F. C. Sun. Comments on ‘Fuzzy control design for nonlinear singularly perturbed systems with pole placement constraints: an LMI approach’. IEEE Trans. on Systems, Man and Cybernetics, 2004, 34(6):2422.