Stuart A. Schweid

Closed loop low-velocity regulation of hybrid stepping motors amidst torque disturbances

To regulate the velocity of hybrid stepper motor motion control systems, a control law which exploits the nonlinear dynamics to create an analog positional control in conjunction with a traditional linear control is introduced. This nonlinear approach allows a much coarser position sensor to be used, including position estimates based on back EMF measurements. The form of the control law admits the use of a wide variety of compensators, whereas earlier laws use only velocity damping compensation. Two specific compensators, i.e., velocity damping and integral control are analyzed in detail, then compared to each other and to open loop microstepping control. It is shown that velocity damping allows the design of the eigenvalues of the closed loop system and provides a linear system approach about a specified operating point. Unfortunately, this operating point includes the value of external DC torque (drag) present, so the closed loop dynamics cannot be guaranteed amidst steady state torque fluctuations. Integral feedback (within a PID controller) improves upon velocity damping by not only allowing the design of the closed loop eigenvalues, but also by completely linearizing the system regardless of external DC torque values. Furthermore, the integral feedback produces zero steady state position error (as expected from linear control theory) and significantly decreases the tendency of the motor to lose step. Experimental results validate the analyses. >

Hybrid step motor position estimation from back EMF

Hybrid stepping motors exhibit a back EMF voltage from which absolute position measurements may be extracted. This work analyses a novel technique for extracting these position measurements for constant velocity applications. The method produces accurate measurements (/spl plusmn/0.1 mechanical degrees). To increase the utility of these measurements, Kalman filtering techniques have been employed. To reject unmodeled disturbance step functions, the nonlinear dynamics have been exploited. The resulting control law is able to use Kalman filtering to improve the bandwidth while retaining disturbance step rejection.

Step motor supply: Minimizing torque ripple induced by digital linearization

Abstract A model of the torque ripple induced when linearizing hybrid stepping motors is developed. Using this model, the optimal waveforms for linearization are derived. These optimized waveforms require only a slightly different table look-up, yet they reduce the square root of the mean square torque ripple approximately five-fold, thus making square wave commutation possible for some applications. Frequency analysis of the resulting torque ripple provides a method of trading-off linearization computations for performance. Experimental results validate this analysis.

Model-less and model-based computationally efficient motion estimation for video compression in transportation applications

Block-based motion estimation is an important component in many video coding standards that aims at removing temporal redundancy between neighboring frames. Traditional methods for block-based motion estimation such as the Exhaustive Block Matching Algorithm (EBMA) are capable of achieving good matching performance but are computationally expensive. Alternatives to EBMA have been proposed to reduce the amount of search points by trading off matching optimality with computational resources. Although they exploit shared local spatial attributes around the target block, they fail to take advantage of the characteristics of the video sequences acquired with stationary cameras used in transportation and surveillance applications, where motion patterns are largely regularized; often, they also fail to yield semantically meaningful motion vector fields. In this paper, we propose two alternative approaches to improve the efficiency of motion estimation in video compression: (i) a highly efficient model-less approach that estimates the direction and magnitude of motion of objects in the scene and predicts the optimal search direction/neighborhood location for motion vectors; and (ii) a model-based approach that learns the dominant spatiotemporal characteristics of the motion patterns captured in the video via statistical models and enables reduced searches according to the constructed models. We demonstrate via experimental validation that the proposed methods attain computational savings, achieve improved reconstruction error and prediction capabilities for a given search neighborhood size, and yield more semantically meaningful motion vector fields when coupled with traditional motion estimation algorithms.

Velocity regulation of stepper motors amidst constant disturbances

To regulate the velocity of hybrid stepper motor motion control systems, a control law which exploits the nonlinear dynamics to create an analog positional control in conjunction with a traditional linear control is introduced. This nonlinear approach allows coarse position sensors to be used, including position estimates based on back EMF measurements. Two specific compensators, i.e., velocity damping and integral control are analyzed in detail, then compared to each other and to open loop microstepping control. It is shown that velocity damping allows the design of the eigenvalues of the closed loop system and provides a linear system approach about a specified operating point. However, this operating point includes the value of external DC torque (drag), so the closed loop dynamics cannot be guaranteed amidst steady state torque fluctuations. Integral feedback (within a PID controller) improves upon velocity damping by not only allowing the design of the closed loop eigenvalues, but also by completely linearizing the system regardless of external DC torque values.