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2002 | Buch

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Flexible Robot Dynamics and Controls

verfasst von: Rush D. Robinett III, Clark R. Dohrmann, G. Richard Eisler, John T. Feddema, Gordon G. Parker, David G. Wilson, Dennis Stokes

Verlag: Springer US

Buchreihe : IFSR International Series in Systems Science and Systems Engineering

Enthalten in: Professional Book Archive

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Über dieses Buch

This book is the result of over ten (10) years of research and development in flexible robots and structures at Sandia National Laboratories. The authors de cided to collect this wealth of knowledge into a set of viewgraphs in order to teach a graduate class in Flexible Robot Dynamics and Controls within the Mechanical En gineering Department at the University of New Mexico (UNM). These viewgraphs, encouragement from several students, and many late nights have produced a book that should provide an upper-level undergraduate and graduate textbook and a reference for experienced professionals. The content of this book spans several disciplines including structural dynam ics, system identification, optimization, and linear, digital, and nonlinear control theory which are developed from several points of view including electrical, me chanical, and aerospace engineering as well as engineering mechanics. As a result, the authors believe that this book demonstrates the value of solid applied theory when developing hardware solutions to real world problems. The reader will find many real world applications in this book and will be shown the applicability of these techniques beyond flexible structures which, in turn, shows the value of mul tidisciplinary education and teaming.

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Inhaltsverzeichnis

Frontmatter

Chapter 1. Introduction

Abstract

Sandia National Laboratories (Sandia) began in 1945 on Sandia Base in Albuquerque, New Mexico, as Z Division, part of what’s now Los Alamos National Lab (LANL). Both laboratories were born out of America’s World War II atomic bomb development effort—the Manhattan Project. Sandia came into being as an ordinance design, testing, and assembly facility, and was located on Sandia Base to be close to an airfield and work closely with the military.

Rush D. Robinett III, Clark R. Dohrmann, G. Richard Eisler, John T. Feddema, Gordon G. Parker, David G. Wilson, Dennis Stokes

Chapter 2. Mathematical Preliminaries

Abstract

An understanding of a broad range of mathematical topics is required to analyze, simulate, and control flexible robotics systems. By no means does this chapter provide a complete coverage of all of the mathematical tools that can be used by an engineer, but this chapter does provide a brief review of the most pertinent concepts. For those readers who are familiar with the topics discussed in this chapter, this review is meant to jog one’s memory and one can always choose to skip this chapter and go directly to Chapter 3 and/or (without any loss of continuity) review the references given at the end of the chapter. On the other hand, this chapter provides an unconventional review of variational calculus and methods for those who are interested in a different point of view. For those readers who are unfamiliar with any of these topics, one should refer to the appropriate references for a more detailed treatment of the material before proceeding to subsequent chapters.

Rush D. Robinett III, Clark R. Dohrmann, G. Richard Eisler, John T. Feddema, Gordon G. Parker, David G. Wilson, Dennis Stokes

Chapter 3. Flexible Robot Dynamic Modeling

Abstract

Several of the control strategies for flexible link robots described in the remainder of the book rely on an accurate dynamic model of the system. Creating a dynamic model that accounts for link flexibility adds additional challenges beyond the standard rigid link robot dynamics. The most apparent complexity arises due to the additional degrees-of-freedom associated with link deformations. Although in theory this adds an infinite number of degrees-of-freedom, in practice only a finite number are used to generate a model that is sufficiently accurate for predictive simulation and control design. Another complexity (and perhaps a less obvious one) is the appearance of first-order (not negligible) dynamic effects due to second-order kinematic and force effects that at first glance appear to be negligible. For simple robot configurations, these effects can be handled in several intuitive ways. However, for complicated geometries, a systematic approach is needed to ensure that coupling effects are not inadvertently lost. Much of this chapter is devoted to describing such an approach, called the method of quadratic modes.

Rush D. Robinett III, Clark R. Dohrmann, G. Richard Eisler, John T. Feddema, Gordon G. Parker, David G. Wilson, Dennis Stokes

Chapter 4. System Identification

Abstract

System Identification (System ID) plays a key role in control system design and input shaping ^1,2. The first thing that a controls engineer learns in the real world is that the transfer function is not written on the outside of the hardware container. So, how does one obtain the transfer function? System ID is used to obtain the transfer function and the critical parameters of simplified systems models that are required for input shaping designs. System models are usually an approximation and need to be refined by comparing to experimental data. On the other hand, empirical models can be developed directly from experimental data when no reasonable theoretical models exist for a system. In any case, System ID provides a systematic way to develop and/or refine the system model. This chapter describes the basic concepts of System ID including linear and nonlinear least squares, as well as, the more advanced concept of homotopy to increase the robustness of the System ID tools. The last section demonstrates the backward propagation technique for multiple-link robots and actuator System ID.

Rush D. Robinett III, Clark R. Dohrmann, G. Richard Eisler, John T. Feddema, Gordon G. Parker, David G. Wilson, Dennis Stokes

Chapter 5. Input Shaping for Path Planning

Abstract

Input shaping is an effective way to optimize the performance of robots, flexible structures, spacecraft, telescopes, and other systems that have vibration, control authority, tracking, and/or pointing constraints. These constraints along with the dynamics and kinematics of the system under consideration can be included in a trajectory optimization/path planning procedure to ensure that the system meets the desired performance. Input shaping is particularly useful when the closed-loop controller cannot be modified or tuned. For example, many pedestal-based robots have closed architecture control systems that restrict access to the servo-loop controls. This chapter begins with the overhead gantry robot and a vibration constraint referred to as swing-free input shaping.

Rush D. Robinett III, Clark R. Dohrmann, G. Richard Eisler, John T. Feddema, Gordon G. Parker, David G. Wilson, Dennis Stokes

Chapter 6. Linear Feedback Control

Abstract

This chapter describes several linear feedback control techniques that can be used to robustly control flexible dynamic systems. As with any dynamic system, it is often difficult to accurately model the system with enough fidelity that open loop control performs as intended. Because modeling errors are often unavoidable, linear feedback is often used to compensate for these modeling uncertainty. Even though many of the flexible dynamic systems are nonlinear, their models can be adequately linearized about operating points and standard linear feedback control techniques can be applied with satisfactory results.

Rush D. Robinett III, Clark R. Dohrmann, G. Richard Eisler, John T. Feddema, Gordon G. Parker, David G. Wilson, Dennis Stokes

Chapter 7. Nonlinear Systems and Sliding Mode Control

Abstract

Many systems of practical interest are nonlinear, but sometimes it is possible to consider small motions about an operating and/or equilibrium point. In this case, a linear set of dynamic equations can be formulated, thus facilitating the use of linear analysis and design techniques. When it is inappropriate to linearize the system, the linear design tools cannot be applied and instead nonlinear analysis is required ^1,2. Two of the more important analyses that are often needed are stability determination and controller design. Several examples are presented to illustrate these situations.

Rush D. Robinett III, Clark R. Dohrmann, G. Richard Eisler, John T. Feddema, Gordon G. Parker, David G. Wilson, Dennis Stokes

Chapter 8. Adaptive Sliding Mode Control

Abstract

Traditionally, adaptive control is applied to dynamic systems that have constant or slowly-varying, uncertain or unknown parameters, such as, manipulator payloads. In the presence of changing plant dynamics, adaptive control design inherently adjusts control system parameters. Adaptive SMC is a specialized form of adaptive control algorithms that falls into the category of robust adaptive control design. A term is included in the control law development that ensures stability in the presence of disturbances, unmodeled dynamics, and modeling inaccuracies.

Rush D. Robinett III, Clark R. Dohrmann, G. Richard Eisler, John T. Feddema, Gordon G. Parker, David G. Wilson, Dennis Stokes

Backmatter

Titel: Flexible Robot Dynamics and Controls
verfasst von: Rush D. Robinett III
Clark R. Dohrmann
G. Richard Eisler
John T. Feddema
Gordon G. Parker
David G. Wilson
Dennis Stokes
Copyright-Jahr: 2002
Verlag: Springer US
Electronic ISBN: 978-1-4615-0539-6
Print ISBN: 978-1-4613-5122-1
DOI: https://doi.org/10.1007/978-1-4615-0539-6