Model reference learning control for a class of nonlinear systems

In the conventional learning control design, a desired output trajectory is specified and an iterative aigorithm is implemented to improve the tracking performance as the action is repeated. This limits the potential researches and applications of the learning controller because in some important tasks like impedance control of robotic manipulator, an explicit reference model is given rather than the desired trajectory. As a result, learning impedance control has not been studied in the force control and learning control of robotic manipulator.Doctor of Philosophy (EEE

Learning control of motion and force for constrained manipulators

It is usually adequate to control the motion of robotic manipulator for tasks such as spray painting, pick and place or spot welding, all of which the manipulator can move freely in the workspace. However, for more demanding tasks such as deburring, spot welding and assembly, constraints are imposed on the motion and the manipulator end effector is interacting with the environments. So far, most researches on learning control have focused on the problem of free motion learning control.Master of Engineerin

Multiple task-space robot control : sense locally, act globally

Task-space sensory feedback information such as visual feedback is used in many modern robot control systems as it improves robustness to model uncertainty. However, existing sensory feedback control schemes are only valid locally in a finite task space within a limited sensing zone where singularity of the Jacobian matrix is avoided. In this paper, the global stability problem of task-space sensory feedback control system is formulated and solved. The proposed method is based on multiple regional feedback information where each feedback information is employed in a local region. The combination of the local feedback covers the entire workspace and thus guarantees the global movement of the robot. In addition, the switching from one feedback information to another is embedded in the controller without using any hard or discontinuous switching. Experimental results are presented to illustrate the performance of the proposed controller

Adaptive Robot Control for Human-dominant Interactions using a General Task Function

When humans and a robot manipulator are sharing the same workspace, the robot is required to interact with the humans in addition to performing the standard robot tasks. Due to the different natures of the robot tasks and interaction tasks, different controllers are required when the task is switched from one to another. However, few results have been obtained in integrating the robot task and the interaction task using one general controller. In this paper, a general task function is employed so that different task requirements can be specified by changing certain task parameters instead of the controller. A simple active role allocation is developed such that when the human is outside the robots workspace, the robot performs the desired robot task and when the human enters the workspace, the robot interacts with human in a way or behaviour as specified by the human. The stability of overall system which integrates both the robot task and interaction task is shown by using Lyapunov like analysis. Experimental results are presented to illustrate the performance of proposed controller.ASTAR (Agency for Sci., Tech. and Research, S’pore)Accepted versio