This paper addresses modeling, simulation and controls of a robotic servicing system for the hubble space telescope servicing missions. The simulation models of the robotic system include flexible body dynamics, control systems and geometric models of the contacting bodies. These models are incorporated into MDA's simulation facilities, the multibody dynamics simulator "space station portable operations training simulator (SPOTS)". Three simulation examples of the robotic servicing operations are presented: (1) capture of the hubble space telescope, (2) berthing the hubble space telescope to the hubble robotic vehicle and (3) inserting the wide field camera into the hubble space telescope

The design and implementation of force control for robotic rigid disk grinding are described. Experiments were conducted using a PUMA 762/VAL II industrial robot equipped with a 4-hp pneumatic grinder and a JR/sup 3/ force sensor. An external, 386-based host microcomputer, communicating with VAL II online, performs the force control algorithm calculations. The robotic grinding force model used was an experimentally verified analytic model. It was found that the grinding forces are very sensitive to the robot arm stiffness. Also, the end-effector path tracking errors, caused by the limited accuracy of the PUMA robot, significantly affect the grinding forces. The experimental results show, however, that a finely tuned PID force-feedback controller is able to maintain the grinding forces at a specified value. It can effectively compensate for force errors caused by both step force disturbances and robot path-tracking errors. The benefits of such force control are demonstrated by improved profiles of finished workpieces.>

A knowledge-based system approach for designing an adaptive controller is introduced. The scheme has been used successfully in designing a self-tuning controller for force regulation in a computer numerically controlled (CNC) milling machine. In this scheme, frames are used for knowledge representation and rules of logic for reasoning. This synergistic combination of frames and rules provides the environment for intelligent control. As a consequence of representing knowledge in frames, the large amount of logic that goes along with most conventionally designed adaptive controllers to ensure safe operation is considerably reduced. Procedural attachments to the slots in the frame replace the extra logic elements in the knowledge-based controller. The self-tuning controller for the CNC milling machine is implemented on a 32-b microprocessor-based computer running at 20 MHz. The knowledge representation and the reasoning process are implemented in Prolog, whereas the numerical algorithms are written in C. Simulations and experimental results are provided that demonstrate the usefulness of this approach.>