Ronald H. Brown

Analysis of algorithms for velocity estimation from discrete position versus time data

Algorithms for constructing velocity approximations from discrete position versus time data are investigated. The study is limited to algorithms suitable to provide velocity information in discrete-time feedback control systems such as microprocessor-based systems with a discrete position encoder. Velocity estimators based on lines per period, reciprocal-time, Taylor series expansion, backward difference expansions, and least-square curve fits are presented. Based on computer simulations, comparisons of relative accuracies of the different algorithms are made. The least-squares velocity estimators filtered the effect of imperfect measurements best, whereas the Taylor series expansions and backward difference equation estimators respond better to velocity transients. >

On algorithms for velocity estimation using discrete position encoders

Velocity information for a rotating device may be acquired via a discrete position encoder. The focus of this paper is on analyzing and improving the low speed and high acceleration accuracy of velocity measurements through the use of digital filters for control applications. The frequency-domain characteristics of various digital filters that may be used to estimate velocity from pulse-encoded velocity data are investigated and used to explain the performance of the digital filters in estimating velocity for a given velocity profile. A new digital filter design technique based on an adaptive least squares approach is presented in addition to several previously developed fixed-time velocity estimators. The performance characteristics of these velocity estimators are compared using pulse-encoded velocity data from an actual motor.

Development of artificial neural network models to predict daily gas consumption

The development of feedforward artificial neural network (ANN) models to predict daily gas consumption is the subject of this paper. A methodology based on network sensitivities and intuition is discussed. The methodology is applied to two regions in Wisconsin served by the Wisconsin Gas Company (WGC). Training results show that ANN models reduce prediction root mean squared errors by more than half when compared with linear regression models. The ANN predictions are compared with predictions made by WGC gas controllers for the first 97 days of the 1994-1995 heating season. The ANN prediction errors are 82.2% and 69.7% of the WGC estimate errors for the two regions.

Velocity Observations From Discrete Position Encoders

A discrete position encoder is an inexpensive means for sensing the angular position of a rotating device. Often a system with higher performance can be achieved if the angular velocity is known in addition to the position. Typically, the output of a discrete position encoder is two square wave signals in quadrature. This paper investigates various methods for processing these signals to observe the velocity in real time. High performance observers based on Taylor series expansions, backward difference expansions, and least square curve fits are developed. The accuracy of the different observers are analyzed by simulations for systems with time measurement truncation and imperfect encoders. The least square curve fit based observers are the most tolerant observers investigated due to the inherent low pass filtering.

Identification algorithm for systemic arterial parameters with application to total artificial heart control

A new algorithm for estimating systemic arterial parameters from systolic pressure and flow measurements at the root of the aorta is developed and tested through a systems identification approach. The resulting procedure has direct application to a total artificial heart (TAH) control system currently under development. Identification models, representing the systemic arterial system, are developed from existing work in the area of cardiovascular modeling. The resistive and compliance components of these models are physically significant, representing overall hydraulic properties of the systemic arterial system. A unique method of parameterizing the identification models is designed which operates on the basis of aortic pressure and flow measurements taken exclusively during systole. The estimator is a modified recursive least squares algorithm which utilizes covariance modification to track time-varying parameters and a dead-zone to improve the robustness. Performance of the estimation algorithm was tested on data generated by a higher-order distributed model of the systemic arterial bed using normal canine parameters. Results from model-to-model experiments verify the consistency of the estimates and the ability of the estimator to converge quickly and track dynamically varying parameters.

Development of feed-forward network models to predict gas consumption

The development of feedforward artificial neural network based models to predict gas consumption on a daily basis is the subject of this paper. An iterative process based on network sensitivities and intuition to determine the proper input factors is discussed. The methods are applied to gas consumption for a region in metropolitan Milwaukee, WI. The obtained results indicate that the feedforward artificial neural network based models reduce the residual predicted consumption root mean squared errors by more than half when compared to models based on linear regression.<<ETX>>

Modeling torque in a switched reluctance motor for adaptive control purposes using self-organizing neural networks

Training paradigms for topology-preserving Kohonen neural networks are introduced for the purpose of identifying and controlling nonlinear systems. A procedure for locking neuron weights at specific locations in a region is presented. It exploits prior knowledge about the system of interest. As a result, superior representations of an arbitrary multivariable nonlinear mapping can be achieved. In addition, the common problem of twisted meshes in these neural networks is eliminated. The strategy introduced for preferentially training these networks at region boundaries overcomes the limitation of boundary contraction. As an example, a one-dimensional neural network is used to approximate a nonlinear function, although in general an n-dimensional mapping can be used to approximate an m-dimensional system for n<or=m. As a practical implementation, the modeling of the theoretical torque of a switched reluctance motor (SRM) as a function of position and current is presented. The topological torque representation is suitable for adaptive control of SRMs in high-performance applications.<<ETX>>

Torque prediction and maximization strategies for bifilar-wound hybrid step motors

A method to predict torque-speed characteristics of the bifilar-wound hybrid step motor with the inverse-diode-clamped drive circuit is presented. The phase excitation control strategies that maximize and minimize average torque production are found. A closed-form expression for average torque produced by the motor is found by representing the flux linkages and model parameters as Fourier series. The predicted average torque function is compared to other methods and experimentally measured results. Inclusion of the drive circuit model is shown to significantly improve prediction of torque speed characteristics. >

Effects of chopping on core losses and inductance profiles of SRM drives

A coupled finite element-state space (CFE-SS) model of switched reluctance motor (SRM) drive systems is used to evaluate the impact of chopping on the machine performance characteristics. The model is used to predict the impact of chopping on the machine inductance profiles, armature currents, and core losses. The modeling environment utilizes an iterative approach which includes the full impact of nonlinearities on the machine magnetic circuit, that results in the nonsinusoidal flux density waveforms in the different parts of the machine, modeling approach is applied to a four phase SRM. Furthermore, the predicted results are verified by comparison to experimental data.

Artificial neural network identification of partially known dynamic nonlinear systems

This paper presents a method for incorporating a priori information about an uncertain nonlinear system into the structure of a multilayer feedforward artificial neural network. Known information is incorporated into the activation function of the network output layer. An algorithm is derived for backpropagating the error and updating adjustable parameters within this layer that is consistent with existing supervised learning techniques. The developed technique is applied to the identification of a dynamic system and compared with conventional feedforward artificial neural network identifier. Results exhibit an improvement in the quality of the identification model and an increase in the rate of convergence. As a practical application, a prior information is utilized for identification of switched reluctance motor characteristics on the basis of experimental measurements. The results further demonstrate that artificial neural networks employing a priori information converge faster, require fewer adjustable weights, and more accurately predict the system of interest.<<ETX>>