Systems, Control, and Robotics Laboratory
Department of Electrical and Computer Engineering
University of California, Davis
Davis, CA 95616
(Phone)916-752-3168
(Home)916-758-9775
(Fax)916-752-8428

Located in room 2227 and 2230 in Engineering Unit II.

Now at Mechatronics Engineering Department 
College of Engineering
Chungnam National University
Taejon, Korea
(phone)82-42-821-6876
(Fax)82-42-825-9225
e-mail : jung@meca.chungnam.ac.kr, jungs@hanbat.chungnam.ac.kr

Education

• Han Yang University, 1983.
• BS ECE - Wayne State University, 1988.
• MSEE - University of California, Davis, 1991.
• Ph.D. - University of California, Davis , June 1996

Research Interests

• Neural Networks in Control

  Our main research objective in neural network control area is to use a neural network as a controller or an auxiliary compensator to compensate for any uncertainties in plant. Our control strategy is on-line learning and control. Within this framework, we have developed on-line learning algorithms for training neural controller.

• Robotics,
• Fuzzy Control

• Neural Network Application to Robot Position Control

  Neural network as a nonlinear adaptive controller is used to compensate for uncertainties in Robot dynamics. Many neural network compensation algorithms have been proposed including feedback error learning(FEL) in the literature. We are proposing a Reference compensation technique(RCT) that neural network compensates at the input trajectory rather than at torque. Learning and control is done on-line. It turns out that the performance of RCT is much better than that of FEL. Besides, RCT has a structural advantage that compensation can be done without modifying internal control structures. Development of a unified neural controller is a future research.

• Neural Network Application to Impedance Force Control of Robot Manipulator

  Neural networks are used as compensators that cancel out uncertainties in Robot dynamics and unknown environment(stiffness and position). The unknown environment stiffness can be estimated by sensed force and end-effector position. The new force tracking impedance function that has an infinite compliance is realized. The new impedance function has the capability of tracking desired force by specifying the estimated environment position as a reference. The inaccurate estimation of environment position can also be compensated by a neural network. Therefore, a single neural network can compensate for all the uncertainties occurred when performing impedance force control. The learning signal is developed.

• Robust Force Control Algorithm Development

  Without using neural network, the robust position control(time -delayed control) is used as an alternative control method. New impedance function that has a capability of desired force tracking can deal with unknown environment stiffness and unknown flat environment surface. If the environment surface is not flat, then a simple adaptive control algorithm can deal with unknown environment surface profile by compensating for uncertainties from inaccurate estimations. Stability and convergence are guaranteed. Experimental results using PUMA 560 showed that the performances were satisfied. This adaptive impedance control is so simple and robust that it is readily used in industrial applications.

• Robot Control Application in Manufacturing : Deburring, Polishing

  The new impedance function can readily be applied to actual industrial applications. Tasks such as deburring and polishing process using industrial robots require sophisticated force control algorithms.

• Analytical Control System Designs, PIDA Controller

  The PIDA controller is an extension of the well known PID controller. It is known that PID controller is not suitable for a third order plant due to lack of one degree of freedom of zero. Adding a zero to PID controller becomes a PIDA(Proportional-integral-derivative-acceleration). The analytical controller design algorithm is developed. Its performance is guaranteed.

Journal Publications

• S. Jung and T.C. Hsia, A New Neural Network Control Techniques for Robot Manipulators", ROBOTICA, Vol. 13, 1995, pp. 477-484.

• S. Jung and T.C. Hsia, A Study On New Neural Network Schemes for Robot Manipulator Control", ROBOTICA, Vol. 14, 1996, pp. 7-16.

• T.C. Hsia and S. Jung, A Simple Alternative to Neural Network Control Scheme for Robot Manipulator", IEEE Trans. on Industrial Electronics, Vol. 42, 1995, pp. 414-416.

• S. Jung and T.C. Hsia, On An Effective Design Approach of Cartesian Space Neural Network Control for Robot Manipulators", ROBOTICA, Vol. 15, Part 3, 1997, pp. 305-312

• S. Jung and T.C. Hsia, New Neural Network Inverse Control Technique for PD Control of Robot Manipulator", submitted to IEEE Trans. on Systems, Man, and Cybernetics, 1996

• S. Jung and T.C. Hsia, New Neural Impedance Force Control of Robot Manipulators", to appear in IEEE Trans. on Industrial Electronics, 1996

• S. Jung and R.C. Dorf, Novel Analytic PID and PIDA Controller Design Techniques and Applications to Motion Control", submitted to IEEE Trans. on Control System Technology, 1995

• R.C. Dorf and S. Jung, Analytic Design Techniques of Lead-lag Controller for Control System Education", submitted to IEEE Trans. on Education, 1995

Conference Proceedings

• S. Jung and T.C. Hsia, Simple Robust Force Control for Unknown Environment, Asian Control Conference, July, 1997, Seoul,

• S. Jung and R. C. Dorf, Computer Aided Design for Robust PID and PIDA Controller ", Asian Control Conference , July, 1997, Seoul,

• S. Jung, T.C. Hsia and R.G. Bonitz, On Robust Impedance Force Control of Robot Manipulator, IEEE Robotics and Automation , New Mexico 1997

• S. Jung and T.C. Hsia, Neural Network Force Control Technique for Unknown Environment Characteristics, US-Japan Graduate Student Forum in Robotics, November, 1996, Osaka, Japan,

• S. Jung and T.C. Hsia, Robust Neural Force Control with Robot Dynamic Uncertainties under Totally Unknown Environment, IEEE/RJS International Conference on Intelligent Robots and Systems, pp.582-589, November, 1996, Osaka, Japan

• S. Jung and R. C. Dorf, Analytic PIDA Controller Design Technique for a Third Order System, IEEE Conference on Decision and Control , pp 2513-2518, December, 1996, Kobe, Japan

• S. Jung and R.C. Dorf, Novel Analytic Technique for PID and PIDA Controller Design,, pp 163-168, IFAC, July, 1996, San Francisco

• S. Jung, A Non-model Based Robot manipulator Control Using Neural Networks, 1996 KACC , November, 1996, Korea. written in Korean

• S. Jung and T.C. Hsia and D. S. Ahn, Force Tracking Position-Based Impedance Control of Robot manipulator with Unknown Stiffness, 1996 KACC , November, 1996, Korea.

• S. Jung and T.C. Hsia, Precision Position Control of Industrial Robot Manipulators using Neural Network, 1996 KOFST International Conference, June, 1996, Seoul.

• S. Jung and T.C. Hsia, Neural Network Impedance Force Control Techniques for Surface Polishing Process in Manufacturing Industries, 1996 KOFST International Conference, June, 1996, Seoul.

• S. Jung and T.C. Hsia, Neural Network Reference Compensation Technique for Position Control of Robot Manipulator, IEEE Conference on Neural Network, pp 1735-1741, June, 1996, Washington D. C.

• S. Jung and T.C. Hsia, New Neural Network Technique for Non-model Based Robot Manipulator Control, IEEE Conference on Systems, Man, and Cybernetics, pp 2928-2933, October, 1995, Vancouver, Canada The Outstanding Student Paper Award

• S. Jung and T.C. Hsia, Neural Network Techniques for Robust Force Control of Robot Manipulators, IEEE Symposium on Intelligent Control, pp 111-116, August, 1995, Monterey

• S. Jung and T.C. Hsia, A New Neural Network Control Technique for Robot Manipulators, American Control Conference, pp 878-882, June, 1995, Seattle

• R.C. Dorf and S. Jung and J. Dawes and L. Ng, An s-plane Analytic Technique for Lead-lag Controller Design, American Control Conference, pp 2227-2228, June, 1995, Seattle

• S. Jung and T.C. Hsia and R. G. Bonitz, On Force Tracking Impedance Control with Unknown Environment Stiffness, IASTED Conference on Robotics and Manufacturing, pp 181-184, June, 1995, Cancun, Mexico

• S. Jung and T.C. Hsia, On Neural Network Controller Design for Position Based Force Control of Robot Manipulators, IASTED Conference on Robotics and Manufacturing, pp 159-162, June, 1995, Cancun, Mexico

• S. Jung and T.C. Hsia, On Neural Network Application to Robust Impedance Control of Robot Manipulator, IEEE Conference on Robotics and Automations, pp 869-874, May, 1995, Nagoya, Japan

• S. Jung and T.C. Hsia, On Reference Trajectory Modification Approach for Cartesian Space Neural Network Control of Robot Manipulators, IEEE Conference on Robotics and Automations, pp 575-580, May, 1995, Nagoya, Japan

• S. Jung and T.C. Hsia, Neural Network Inverse Control of Robot Manipulator, International Conference on Neural Information Processing, ICONIP, pp 603-608, October, 1994, Seoul, Korea,

• S. Jung and T.C. Hsia, On-Line Neural Network Control of Robot Manipulators, International Conference on Neural Information Processing, ICONIP, pp 1663-1668, October, 1994, Seoul, Korea, The Best Student Paper Award

Thesis

• S. Jung, On Tracking Performance of a Robot Manipulator under Force Control, University of California, Davis, March, 1991
• S. Jung, Neural Network Controllers for Robot Manipulators, University of California, Davis, June, 1996

Affiliations

• Institute of Electrical and Electronics Engineering Student Member (IEEE)
• Tau Beta Pi
• Eta Kappa Nu
• IASTED Member
• KSEA Member
• KEE Member
• Member of DKC

Myself

• All kinds of Sport - like to sweat, but no time
• Tennis - Medium
• Bowling - Avg. 130
• Teaching Children
• Favorite song - "The Rose"
• Favorite ? - "Alba?????"

Other Interesting Links

• Korean Graduate Student Association in Davis
• Davis Korean Community
• Davis Korean Community Church

  

  

  Questions? Comments? E-mail me at jung@ece.ucdavis.edu
  
  

  ---

  There have been visitors to this homepage since February 1, 1996.