Transition Space

Informal causal descriptions of physical systems abound in sources such as encyclopedias, reports and user's manuals. Yet these descriptions remain largely opaque to computer processing. This paper proposes a representational framework in which such descriptions are viewed as providing partial specifications of paths in a space of possible transitions, or transition space. In this framework, the task of comprehending informal causal descriptions emerges as one of completing the specifications of paths in transition space---filling causal gaps and relating accounts of activity varied by analogy and abstraction. The use of the representation and its operations is illustrated in the context of a simple description concerning rocket propulsion.

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.

Swimming in Space-Time

Cyclic changes in the shape of a quasi-rigid body on a curved manifold can lead to net translation and/or rotation of the body in the manifold. Presuming space-time is a curved manifold as portrayed by general relativity, translation in space can be accomplished simply by cyclic changes in the shape of a body, without any thrust or external forces.

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.

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.

Motor Control and Learning by the State Space Model

A model is presented that deals with problems of motor control, motor learning, and sensorimotor integration. The equations of motion for a limb are parameterized and used in conjunction with a quantized, multi-dimensional memory organized by state variables. Descriptions of desired trajectories are translated into motor commands which will replicate the specified motions. The initial specification of a movement is free of information regarding the mechanics of the effector system. Learning occurs without the use of error correction when practice data are collected and analyzed.