Prof. Chien Chern Cheah

It has been almost two decades since the first globally tracking convergent adaptive controllers were derived for robot with dynamic uncertainties. However, the problem of concurrent adaptation to both kinematic and dynamic uncertainties has never been systematically solved. This is the subject of this paper. We derive a new adaptive Jacobian controller for trajectory tracking of robot with uncertain kinematics and dynamics. It is shown that the robot endeffector is able to converge to a desired trajectory with the uncertain kinematics and dynamics parameters being updated online by parameter update laws. The algorithm requires only to measure the end-effector position, besides the robot's joint angles and joint velocities. The proposed controller can also be extended to adaptive visual tracking control with uncertain camera parameters, taking into consideration the uncertainties of the nonlinear robot kinematics and dynamics. Experimental results are presented to illustrate the performance of the proposed controllers. In the experiments, we demonstrate that the robot's shadow can be used to control the robot.

11 August 2003 in IEEE Transactions on Robotics and Automation

Journal Article

Approximate jacobian control for robots with uncertain kinematics and dynamics

Chien Chern Cheah

M. Hirano

S. Kawamura

S. Arimoto

Most research so far in robot control has assumed either kinematics or Jacobian matrix of the robots from joint space to Cartesian space is known exactly. Unfortunately, no physical parameters can be derived exactly. In addition, when the robot picks up objects of uncertain lengths, orientations, or gripping points, the overall kinematics from the robot's base to the tip of the object becomes uncertain and changes according to different tasks. Consequently, it is unknown whether stability of the robot could be guaranteed in the presence of uncertain kinematics. In order to overcome these drawbacks, in this paper, we propose simple feedback control laws for setpoint control without exact knowledge of kinematics, Jacobian matrix, and dynamics. Lyapunov functions are presented for stability analysis of feedback control problem with uncertain kinematics. We shall show that the end-effector's position converges to a desired position in a finite task space even when the kinematics and Jacobian matrix are uncertain. Experimental results are presented to illustrate the performance of the proposed controllers.

19 June 2006 in IEEE Transactions on Automatic Control

Journal Article

Adaptive Jacobian Tracking Control of Robots With Uncertainties in Kinematic, Dynamic and Actuator Models

Chien Chern Cheah

C. Liu

J.J.E. Slotine

Most research so far on robot trajectory control has assumed that the kinematics of the robot is known exactly. However, when a robot picks up tools of uncertain lengths, orientations, or gripping points, the overall kinematics becomes uncertain and changes according to different tasks. Recently, we derived a new adaptive Jacobian tracking controller for robots with uncertain kinematics and dynamics. This note extends the results to include redundant robots and adaptation to actuator parameters. Experimental results are presented to illustrate the performance of the proposed controller.

1 June 1998 in IEEE Transactions on Robotics and Automation

Journal Article

Learning impedance control for robotic manipulators

Chien Chern Cheah

Danwei Wang

In this paper, an iterative learning impedance control problem for robotic manipulators is formulated and solved. A target impedance is specified and a learning controller is designed such that the system follows the desired response specified by the target model as the actions are repeated. A design method for analyzing the convergence of the learning impedance system is developed. A sufficient condition for guaranteeing the convergence of the system is also derived. The proposed learning impedance control scheme is implemented on an industrial selective compliance assembly robot arm (SCARA) robot, SEIKO TT3000. Experimental results verify the theory and confirm the effectiveness of the learning impedance controller

1 June 2013 in Automatica

Journal Article

Dynamic trapping and manipulation of biological cells with optical tweezers

Xiang Li

Chien Chern Cheah

Songyu Hu

Dong Sun

About Me

Chien Chern Cheah received his B.E. degree in Electrical Engineering from the National University of Singapore in 1990, and M.E. and Ph.D. degrees in Electrical Engineering, both from Nanyang Technological University, Singapore, in 1993 and 1996, respectively. From 1990 to 1991, he worked as a design engineer in Chartered Electronics Industries, Singapore. He was a research fellow in the Department of Robotics, at Ritsumeikan University, Japan from 1996 to 1998. He joined the School of Electrical and Electronic Engineering, Nanyang Technological University as an assistant professor in 1998. He is currently an associate professor at Nanyang Technological University. In November 2002, he received the overseas attachment fellowship from the Agency for Science, Technology, and Research (A*STAR), Singapore to visit the Nonlinear Systems Laboratory, Massachusetts Institute of Technology. He serves as an associate editor for Automatica. He has served as an associate editor for IEEE Transactions on Robotics from 2010 to 2013. He was the program co-chair of the IEEE Conference on Robotics and Automation (ICRA) 2017, and program chair of the International Conference on Control, Automation, Robotics and Vision (ICARCV) in 2012 and 2006. His research interests include robot control, adaptive and learning systems, micro-manipulation, human–robot interaction, and multi-robot systems.

Prof. Chien Chern Cheah

Prof. Chien Chern Cheah

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