Chien Chern Cheah

Bio: Chien Chern Cheah is an academic researcher from Nanyang Technological University. The author has contributed to research in topics: Control theory & Adaptive control. The author has an hindex of 34, co-authored 180 publications receiving 4324 citations. Previous affiliations of Chien Chern Cheah include Ritsumeikan University.

Adaptive Tracking Control for Robots with Unknown Kinematic and Dynamic Properties

Abstract: 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.

Approximate Jacobian control for robots with uncertain kinematics and dynamics

Abstract: 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.

Adaptive Jacobian tracking control of robots with uncertainties in kinematic, dynamic and actuator models

Abstract: 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.

Learning impedance control for robotic manipulators

Abstract: Most researches on learning control of constrained robots have been focused on the problem of hybrid position/force control where the learning controllers are designed to track the desired motion and force trajectories. The learning impedance control of robotic manipulators, however, has not been developed so far. In this paper, a 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. Sufficient conditions for guaranteeing the convergence of the system are derived. Simulation results of a cylindrical robot are presented to illustrate the performances of the proposed learning impedance controller.

Computer vision : a modern approach = 计算机视觉 : 一种现代的方法

Abstract: From the Publisher: The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.

A survey of iterative learning control

Abstract: This article surveyed the major results in iterative learning control (ILC) analysis and design over the past two decades. Problems in stability, performance, learning transient behavior, and robustness were discussed along with four design techniques that have emerged as among the most popular. The content of this survey was selected to provide the reader with a broad perspective of the important ideas, potential, and limitations of ILC. Indeed, the maturing field of ILC includes many results and learning algorithms beyond the scope of this survey. Though beginning its third decade of active research, the field of ILC shows no sign of slowing down.

Coverage control for mobile sensing networks

Abstract: This paper presents control and coordination algorithms for groups of vehicles. The focus is on autonomous vehicle networks performing distributed sensing tasks where each vehicle plays the role of a mobile tunable sensor. The paper proposes gradient descent algorithms for a class of utility functions which encode optimal coverage and sensing policies. The resulting closed-loop behavior is adaptive, distributed, asynchronous, and verifiably correct.

Adaptive Control

Abstract: This book, written by two major figures in adaptive control, provides a wealth of material for researchers, practitioners, and students. While some researchers in adaptive control may note the absence of a particular topic, the book‘s scope represents a high-gain instrument. It can be used by designers of control systems to enhance their work through the information on many new theoretical developments, and can be used by mathematical control theory specialists to adapt their research to practical needs. The book is strongly recommended to anyone interested in adaptive control.

Iterative Learning Control: Brief Survey and Categorization

Abstract: In this paper, the iterative learning control (ILC) literature published between 1998 and 2004 is categorized and discussed, extending the earlier reviews presented by two of the authors. The papers includes a general introduction to ILC and a technical description of the methodology. The selected results are reviewed, and the ILC literature is categorized into subcategories within the broader division of application-focused and theory-focused results.