Shaping Inputs to Reduce Vibration in Flexible Space Structures

Future NASA plans to launch large space strucutres solicit the need for effective vibration control schemes which can solve the unique problems associated with unwanted residual vibration in flexible spacecraft. In this work, a unique method of input command shaping called impulse shaping is examined. A theoretical background is presented along with some insight into the methdos of calculating multiple mode sequences. The Middeck Active Control Experiment (MACE) is then described as the testbed for hardware experiments. These results are shown and some of the difficulties of dealing with nonlinearities are discussed. The paper is concluded with some conclusions about calculating and implementing impulse shaping in complex nonlinear systems.

Minimizing Residual Vibrations in Flexible Systems

Residual vibrations degrade the performance of many systems. Due to the lightweight and flexible nature of space structures, controlling residual vibrations is especially difficult. Also, systems such as the Space Shuttle remote Manipulator System have frequencies that vary significantly based upon configuration and loading. Recently, a technique of minimizing vibrations in flexible structures by command input shaping was developed. This document presents research completed in developing a simple, closed- form method of calculating input shaping sequences for two-mode systems and a system to adapt the command input shaping technique to known changes in system frequency about the workspace. The new techniques were tested on a three-link, flexible manipulator.

Pose Determination of a Grasped Object Using Limited Sensing

This report explores methods for determining the pose of a grasped object using only limited sensor information. The problem of pose determination is to find the position of an object relative to the hand. The information is useful when grasped objects are being manipulated. The problem is hard because of the large space of grasp configurations and the large amount of uncertainty inherent in dexterous hand control. By studying limited sensing approaches, the problem's inherent constraints can be better understood. This understanding helps to show how additional sensor data can be used to make recognition methods more effective and robust.

Representing Knowledge of Large-Scale Space

This dissertation presents a model of the knowledge a person has about the spatial structure of a large-scale environment: the "cognitive map". The functions of the cognitive map are to assimilate new information about the environment, to represent the current position, and to answer route-finding and relative-position problems. This model (called the TOUR model) analyzes the cognitive map in terms of symbolic descriptions of the environment and operations on those descriptions. Knowledge about a particular environment is represented in terms of route descriptions, a topological network of paths and places, multiple frames of reference for relative positions, dividing boundaries, and a structure of containing regions. The current position is described by the "You Are Here" pointer, which acts as a working memory and a focus of attention. Operations on the cognitive map are performed by inference rules which act to transfer information among different descriptions and the "You Are Here" pointer. The TOUR model shows how the particular descriptions chosen to represent spatial knowledge support assimilation of new information from local observations into the cognitive map, and how the cognitive map solves route-finding and relative-position problems. A central theme of this research is that the states of partial knowledge supported by a representation are responsible for its ability to function with limited information of computational resources. The representations in the TOUR model provide a rich collection of states of partial knowledge, and therefore exhibit flexible, "common-sense" behavior.

Automatic Design of a Maglev Controller in State Space

We describe the automatic synthesis of a global nonlinear controller for stabilizing a magnetic levitation system. The synthesized control system can stabilize the maglev vehicle with large initial displacements from an equilibrium, and possesses a much larger operating region than the classical linear feedback design for the same system. The controller is automatically synthesized by a suite of computational tools. This work demonstrates that the difficult control synthesis task can be automated, using programs that actively exploit knowledge of nonlinear dynamics and state space and combine powerful numerical and symbolic computations with spatial-reasoning techniques.