Design and control of dual-stage feed drives

High precision positioning is one of the most important features of a precision machine. Such a machine is required to provide versatility, speed and workspace as well as high precision positioning. Combining coarse (large stroke) and fine (high resolution) drive stages, connected in series, in a dual-stage feed drive (DSFD) provides the capacity of a large workspace with the property of high precision motion.; This thesis presents an innovative design of a DSFD system for machines. It studies the design methodology and the implementation of the system and investigates several considerations that govern the design process and determine the performance. A DSFD setup based on a combination of piezoelectric actuators (PA's) and linear motors (LM's) was built for experimental testing.; The thesis also presents a controller design methodology for machine tool direct feed drives. The methodology is applied to the LM and PA under study. The structure of each plant model is obtained from physical laws and its parameters are obtained using system identification. A single transfer function (TF) model is shown to accurately predict the response of the LM. For the PA, multiple local models are required to accurately represent its dynamics.; Two DSFDs, single-axis and two-axis, are designed with PAs for the fine stages and LMs for the coarse stages. Both feature flexures for frictionless precision motion that are designed to meet the static and dynamic requirements of a milling process. A model-based control algorithm is designed to ensure that the stages work together in a complementary fashion. The single-axis DSFD reduced the tracking error by about 75% in comparison to a similarly controlled LM drive. A second DSFD was built for milling experiments. In sinusoidal profile cutting the maximum tracking error was reduced by 83% and the average magnitude of the error was reduced by 63%. In sharp corner cutting the DSFD reduced the maximum tracking error by 38% and the average magnitude of the error by 39%. (Abstract shortened by UMI.)...