Brian Armstrong-Hélouvry

Bio: Brian Armstrong-Hélouvry is an academic researcher from University of Wisconsin–Milwaukee. The author has contributed to research in topics: Friction torque & Nonlinear system. The author has an hindex of 10, co-authored 14 publications receiving 3857 citations. Previous affiliations of Brian Armstrong-Hélouvry include University of Wisconsin-Madison.

Control of Machines with Friction

Abstract: 1. Introduction.- 2. Friction in Machines.- 2.1. The Contemporary Model of Machine Friction.- 2.2. Boundary Lubricants: a Domain of Many Choices.- 2.3. Relaxation Oscillations.- 2.4. Friction Modeling in the Controls Literature.- 2.5. An Integrated Friction Model.- 3. Experiment Design.- 4. Repeatability.- 5. Break-Away Experiments.- 5.1. Experimental Issues in Measuring Break-Away Torque.- 5.2. Building the Compensation Table.- 6. Friction as a Function of Velocity.- 6.1. Analysis of Variance in the Motion Friction Data.- 6.2. Friction at Low Velocities.- 6.3. Friction During Compliant Motion.- 6.4. The Dahl Effect.- 6.5. The Stribeck Effect.- 6.6. Temporal Effects in the Rise and Decay of Friction.- 6.7. Variance in Friction as Process Noise.- 7. Analysis of Stick-Slip.- 7.1. Dimensional Analysis.- 7.2. Perturbation Analysis.- 7.3. The Impact of Static Friction Rising as a Function of Dwell Time.- 7.4. Integral Control.- 8. Demonstrations of Friction Compensation.- 8.1. Open-Loop Motion of One Joint.- 8.2. Open-Loop Motion of Three Joints.- 8.3. Friction Compensated Force Control.- 9. Suggestions Toward Friction Modeling and Compensation.- 9.1. Suggestions on Experimental Technique.- 9.2. Suggestions on Control.- 9.3. Conclusion.- Appendix A: Small Studies.- A.1 Friction as a Function of Motor Angle.- A.2 Joint 2 Motor Alone and Joint 2 Link Alone.- A.3 Trials with Dither.- A.4 Friction as a Function of Load.- A.5 Creep.- A.6 Effects that were not Observed.

Stick slip and control in low-speed motion

Abstract: Dimensional and perturbation analysis are applied to the problem of stick slip encountered during the motion of machines. The friction model studied is motivated by current tribological results and is appropriate for lubricated metal contacts. The friction model incorporates Coulomb, viscous, and Stribeck friction with frictional memory and rising static friction. Through dimensional analysis an exact model of the nonlinear system can be formed using five parameters rather than ten, greatly facilitating study and explicitly revealing the interaction of parameters. By converting the system of differential equations into a set of integrations, the perturbation technique makes approximate analysis possible where only numerical techniques had been available before. The analysis predicts the onset of stick slip as a function of plant and controller parameters; these results are compared with experimental data. >

A search for consensus among model parameters reported for the PUMA 560 robot

Abstract: The PUMA 560 robot is the white rat of robotics research - it has been studied and used in countless experiments over many years and in many laboratories. However, it remains a challenge to assemble the complete data needed for model-based control of the robot. This paper presents a numerical comparison of kinematic, dynamic and electrical parameters for the PUMA 560 robot which have been reported in the literature. For the first time, data from several experiments are presented in a single system of coordinates, which facilitates comparison. Differences in the data and the various methods of measurement are discussed. New data have been gathered and are presented where the record was incomplete. >

Stick-slip arising from Stribeck friction

Abstract: The implications of Stribeck friction (a nonlinear low-velocity friction effect that contributes to and perhaps dominates stick-slip motion) for feedback control are explored. Through the use of dimensional analysis, the following are examined: (1) the minimum velocity below which stick-slip will occur; (2) the accuracy of sensing required to eliminate stick-slip by feedback control; (3) the slip distance during stick-slip motion; and (4) scaling of Stribeck friction and stick-slip. A seven-parameter friction-plus-control model is proposed; dimensional analysis is employed to reduce the degrees of freedom of this model to three. >

A new model for control of systems with friction

Abstract: In this paper we propose a new dynamic model for friction. The model captures most of the friction behavior that has been observed experimentally. This includes the Stribeck effect, hysteresis, spring-like characteristics for stiction, and varying break-away force. Properties of the model that are relevant to control design are investigated by analysis and simulation. New control strategies, including a friction observer, are explored, and stability results are presented. >

Adaptive Control

Abstract: This book, written by two major figures in adaptive control, provides a wealth of material for researchers, practitioners, and students. While some researchers in adaptive control may note the absence of a particular topic, the book‘s scope represents a high-gain instrument. It can be used by designers of control systems to enhance their work through the information on many new theoretical developments, and can be used by mathematical control theory specialists to adapt their research to practical needs. The book is strongly recommended to anyone interested in adaptive control.

Rigid Body Dynamics Algorithms

Abstract: Rigid Body Dynamics Algorithms presents the subject of computational rigid-body dynamics through the medium of spatial 6D vector notation. It explains how to model a rigid-body system and how to analyze it, and it presents the most comprehensive collection of the best rigid-bodydynamics algorithms to be found in a single source. The use of spatial vector notation greatly reduces the volume of algebra which allows systems to be described using fewer equations and fewer quantities. It also allows problems to be solved in fewer steps, and solutions to be expressed more succinctly. In addition algorithms are explained simply and clearly, and are expressed in a compact form. The use of spatial vector notation facilitates the implementation of dynamics algorithms on a computer: shorter, simpler code that is easier to write, understand and debug, with no loss of efficiency.