In this thesis, novel implementation methods for FPGA based real-time motion control systems are investigated. These methods are examined for conventional and modern controller topologies as well as peripheral device interfaces which are mutually essential pieces of a motion controller. The developed methods are initially tested one by one to assess the performance of the individual design / and finally an assembled solution is developed to test the overall design. Tests of the overall design are realized via hardware-in-the-loop simulation of a real-world control problem, selected as a CNC machining center. The developed methods are discussed in terms of their success, resource consumptions and attainable sampling rates.

This dissertation presents a high-precision positioner with a novel superimposed concentrated-field permanent-magnet matrix. This extended-range multi-axis positioner can generate all 6-DOF (degree-of-freedom) motions with only a single moving part. It is actuated by three planar levitation motors, which are attached on the bottom of the moving part. Three aerostatic bearings are used to provide the suspension force against the gravity for the system. The dynamic model of the system is developed and analyzed. And several control techniques including SISO (single input and single output) and MIMO (multi inputs and multi outputs) controls are discussed in the dissertation. The positioner demonstrates a position resolution of 20 nm and position noise of 10 nm rms in x and y and 15 nm rms in z. The angular resolution around the x-, y-, and z-axes is in sub-microradian order. The planar travel range is 160 mm ?? 160 mm, and the maximum velocity achieved is 0.5 m/s at a 5-m/s2 acceleration, which can enhance the throughput in precision manufacturing. Various experimental results are presented in this dissertation to demonstrate the positioner??s capability of accurately tracking any planar trajectories. Those experimental results verified the potential utility of this 6-DOF high-precision positioner in precision manufacturing and factory automation.

This dissertation is the first attempt to demonstrate the use of magnetic-levitation (maglev) positioners for commercial applications requiring nanopositioning. The key objectives of this research were to devise the control strategies and motion planning to overcome the inherent technical challenges of the maglev systems, and test them on the developed maglev systems to demonstrate their capabilities as the next-generation nanopositioners. Two maglev positioners based on novel actuation schemes and capable of generating all the six-axis motions with a single levitated platen were used in this research. These light-weight single-moving platens have very simple and compact structures, which give them an edge over most of the prevailing nanopositioning technologies and allow them to be used as a cluster tool for a variety of applications. The six-axis motion is generated using minimum number of actuators and sensors. The two positioners operate with a repeatable position resolution of better than 3 nm at the control bandwidth of 110 Hz. In particular, the Y-stage has extended travel range of 5 mm × 5 mm. They can carry a payload of as much as 0.3 kg and retain the regulated position under abruptly and continuously varying load conditions. This research comprised analytical design and development, followed by experimental verification and validation. Preliminary analysis and testing included open-loop stabilization and rigorous set-point change and load-change testing to demonstrate the precision-positioning and load-carrying capabilities of the maglev positioners. Decentralized single-input-single-output (SISO) proportional-integral-derivative (PID) control was designed for this analysis. The effect of actuator nonlinearities were reduced through actuator characterization and nonlinear feedback linearization to allow consistent performance over the large travel range. Closed-loop system identification and order-reduction algorithm were developed in order to analyze and model the plant behavior accurately, and to reduce the effect of unmodeled plant dynamics and inaccuracies in the assembly. Coupling among the axes and subsequent undesired motions and crosstalk of disturbances was reduced by employing multivariable optimal linear-quadratic regulator (LQR). Finally, application-specific nanoscale path planning strategies and multiscale control were devised to meet the specified conflicting time-domain performance specifications. All the developed methodologies and algorithms were implemented, individually as well as collectively, for experimental verification. Some of these applications included nanoscale lithography, patterning, fabrication, manipulation, and scanning. With the developed control strategies and motion planning techniques, the two maglev positioners are ready to be used for the targeted applications.

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In this dissertation, we propose a universal motion-based control framework that supports general functionalities on 2D and 3D user interfaces with a single integrated design. We develop a hybrid framework of optical and inertial sensing technologies to track 6-DOF (degrees of freedom) motion of a handheld device, which includes the explicit 6-DOF (position and orientation in the global coordinates) and the implicit 6-DOF (acceleration and angular speed in the device-wise coordinates). Motion recognition is another key function of the universal motion-based control and contains two parts: motion gesture recognition and air-handwriting recognition. The interaction technique of each task is carefully designed to follow a consistent mental model and ensure the usability. The universal motion-based control achieves seamless integration of 2D and 3D interactions, motion gestures, and air-handwriting. Motion recognition by itself is a challenging problem. For motion gesture recognition, we propose a normalization procedure to effectively address the large in-class motion variations among users. The main contribution is the investigation of the relative effectiveness of various feature dimensions (of tracking signals) for motion gesture recognition in both user-dependent and user-independent cases. For air-handwriting recognition, we first develop a strategy to model air-handwriting with basic elements of characters and ligatures. Then, we build word-based and letter-based decoding word networks for air-handwriting recognition. Moreover, we investigate the detection and recognition of air-fingerwriting as an extension to air-handwriting. To complete the evaluation of air-handwriting, we conduct usability study to support that air-handwriting is suitable for text input on a motion-based user interface.

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A long-standing goal in computer graphics is to create and control realistic motion for virtual human characters. Despite the progress made over the last decade, it remains challenging to design a system that allows a random user to intuitively create and control life-like human motions. This dissertation focuses on exploring theory, algorithms and applications that enable novice users to quickly and easily create and control natural-looking motions, including both full-body movement and hand articulations, for human characters. More specifically, the goals of this research are: (1) to investigate generative statistical models and physics-based dynamic models to precisely predict how humans move and (2) to demonstrate the utility of our motion models in a wide range of applications including motion analysis, synthesis, editing and acquisition. We have developed two novel generative statistical models from prerecorded motion data and show their promising applications in real time motion editing, online motion control, offline animation design, and motion data processing. In addition, we have explored how to model subtle contact phenomena for dexterous hand grasping and manipulation using physics-based dynamic models. We show for the first time how to capture physically realistic hand manipulation data from ambiguous image data obtained by video cameras.

Model-based control strategies for robot manipulators can present numerous performance advantages when an accurate model of the system dynamics is available. In practice, obtaining such a model is a challenging task which involves modeling such physical processes as friction, which may not be well understood and difficult to model. Furthermore, uncertainties in the physical parameters of a system may be introduced from significant discrepancies between the manufacturer data and the actual system. Traditionally, adaptive and robust control strategies have been developed to deal with parametric uncertainty in the dynamic model, but often require knowledge of the structure of the dynamics. Recent approaches to model-based manipulator control involve data-driven learning of the inverse dynamics relationship, eliminating the need for any a-priori knowledge of the system model. Locally Weighted Projection Regression (LWPR) has been proposed for learning the inverse dynamics function of a manipulator. Due to its use of simple local, linear models, LWPR is suitable for online and incremental learning. Although global regression techniques such as Gaussian Process Regression (GPR) have been shown to outperform LWPR in terms of accuracy, due to its heavy computational requirements, GPR has been applied mainly to offline learning of inverse dynamics. More recent efforts in making GPR computationally tractable for real-time control have resulted in several approximations which operate on a select subset, or sparse representation of the entire training data set. Despite the significant advancements that have been made in the area of learning control, there has not been much work in recent years to evaluate these newer regression techniques against traditional model-based control strategies such as adaptive control. Hence, the first portion of this thesis provides a comparison between a fixed model-based control strategy, an adaptive controller and the LWPR-based learning controller. Simulations are carried out in order to evaluate the position and orientation tracking performance of each controller under varied end effector loading, velocities and inaccuracies in the known dynamic parameters. Both the adaptive controller and LWPR controller are shown to have comparable performance in the presence of parametric uncertainty. However, it is shown that the learning controller is unable to generalize well outside of the regions in which it has been trained. Hence, achieving good performance requires significant amounts of training in the anticipated region of operation. In addition to poor generalization performance, most learning controllers commence learning entirely from `scratch,' making no use of any a-priori knowledge which may be available from the well-known rigid body dynamics (RBD) formulation. The second portion of this thesis develops two techniques for online, incremental learning algorithms which incorporate prior knowledge to improve generalization performance. First, prior knowledge is incorporated into the LWPR framework by initializing the local linear models with a first order approximation of the prior information. Second, prior knowledge is incorporated into the mean function of Sparse Online Gaussian Processes (SOGP) and Sparse Pseudo-input Gaussian Processes (SPGP), and a modified version of the algorithm is proposed to allow for online, incremental updates. It is shown that the proposed approaches allow the system to operate well even without any initial training data, and further performance improvement can be achieved with additional online training. Furthermore, it is also shown that even partial knowledge of the system dynamics, for example, only the gravity loading vector, can be used effectively to initialize the learning.

In this study, a three-axes PC numerical control system for industrial applications has been developed. With this system, fast and cheap prototyping of designed objects can be realized. The system consists of software and a hardware which includes an XYZ positioning table and three step motors controlling this table. A proper drive circuit for the stepper motors is utilized. The software digitizes two dimensional drawings of three dimensional objects and generates the control signals for the XYZ positioning table. The software is developed under Microsoft Studio Visual Basic 6.0 environment regardless of the OS of the PC. The parallel port of the PC has been utilized for generating the necessary control signals for the stepper motors.