Dr. Bernard Friedland is a Distinguished Professor in the Department of Electrical and Computer Engineering at the New Jersey Institute of Technology which he joined in January 1990. He was a Lady Davis Visiting Professor at the Technion--Israel Institute of Technology and has held appointments as an Adjunct Professor of Electrical Engineering at the Polytechnic University, New York University, and Columbia University. He was born and educated in New York City and received his B.S., M.S., and Ph.D. degrees from Columbia University.
Dr. Friedland is author of two textbooks on automatic control and co-author a textbook on circuit theory and another on linear system theory. He is the author or co-author of over 100 technical papers on control theory and its applications. His theoretical contributions include: a technique of quasi-optimum control, treatment of bias in recursive filtering, design of reduced-order linear regulators, modeling of pulse-width modulated control systems, maximum likelihood failure detection, friction modeling and compensation, and parameter estimation.
For 27 years prior to joining NJIT, Dr. Friedland was Manager of Systems Research in the Kearfott Guidance and Navigation Corporation in Little Falls, NJ. While at Kearfott, he was awarded 12 patents in the field of navigation, instrumentation, and control systems.
Dr. Friedland is the recipient of the 1982 Oldenberger Medal of the ASME. He is a Fellow of the IEEE and the ASME and has received the the IEEE Third Millennium Medal and the IEEE Control Systems Society´s Distinguished Member Award.
Ph.D. Electrical Engineering Faculty of Pure Science Columbia University 1957
M.S. Electrical Engineering School of Engineering Columbia University 1954
B.S. Electrical Engineering School of Engineering Columbia University 1953
A.B. Pre-engineering Columbia College Columbia University 1952
Control theory, especially methods for control of linear and nonlinear systems with data sources of multiple types
Applications to friction modeling, compensation, traction control, rapid thermal processing, robotic vehicle navigation and control
Friction Modeling and Compensation
For the past several years Dr. Friedland, in collaboration with Dr. Avraham Harnoy of the Department of the NJIT Department of Mechanical Engineering, has been investigating the modeling and compensation of friction in control systems.
Dr. Harnoy and his students have developed dynamics models of friction in lubricated journal bearings based on the underlying physics and have experimentally verified these models. Current research is aimed at developing models for different types of friction and surface contact using a similar methodology.
Dr. Friedland and his students have developed a simple but effective technique for compensating friction in control systems based on use of a nonlinear observer to estimate the friction coefficient of a model based on simple Coulomb friction. Although the dynamics of the model are not used in the observer, it has been experimentally verified that the observer can track the friction dynamics with reasonable accuracy. Nevertheless, further performance improvements are expected to result from including the friction dynamics in the observer and estimating additional parameters.
In addition to the compensation of unwanted friction, Dr. Friedland and his students have initiated investigation of the modeling of friction in mechanical systems using frictional traction.
State and Parameter Estimation
A nonlinear observer for estimating one or more parameters of a dynamic system was developed by Dr. Friedland as an extension of the technique used for estimating the friction coefficient. He and his former student, Dr. David Haessig, are now investigating the extension of this technique to the estimation of not only the parameters, but also the state of a dynamic system.
Applications of Extended Linearization (State-Dependent Algebraic Riccati Equation)
In his textbook, Advanced Control System Design, Dr. Friedland suggested the possibility of achieving a suitable control system design by "extended linearization" of the dynamics of a nonlinear system. Independently, Dr. James Cloutier arrived at the same method which he called the state-dependent algebraic Riccati equation method. A number of investigators have shown the validity of the method in examples, although a rigorous proof of stability has eluded discovery.
Dr. Friedland has shown that the method is capable of producing very good performance in systems with "hard" nonlinearities, such as friction, backlash, and limit stops.
Current research is being pursued on implementation issues to avoid on-line solution of the algebraic Riccati equation.