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Motion Control

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Springer Handbook of Robotics

Abstract

This Chapter will focus on the motion control of robotic rigid manipulators. In other words, this Chapter does not treat the motion control of mobile robots, flexible manipulators, and manipulators with elastic joints. The main challenge in the motion control problem of rigid manipulators is the complexity of their dynamics and uncertainties. The former results from nonlinearity and coupling in the robot manipulators. The latter is twofold: structured and unstructured. Structured uncertainty means imprecise knowledge of the dynamic parameters and will be touched upon in this Chapter, whereas unstructured uncertainty results from joint and link flexibility, actuator dynamics, friction, sensor noise, and unknown environment dynamics, and will be treated in other Chapters.

In this Chapter, we begin with an introduction to motion control of robot manipulators from a fundamental viewpoint, followed by a survey and brief review of the relevant advanced materials. Specifically, the dynamic model and useful properties of robot manipulators are recalled in Sect. 6.1. The joint and operational space control approaches, two different viewpoints on control of robot manipulators, are compared in Sect. 6.2. Independent joint control and proportional–integral–derivative (PID) control, widely adopted in the field of industrial robots, are presented in Sections 6.3 and 6.4, respectively. Tracking control, based on feedback linearization, is introduced in Sect. 6.5. The computed-torque control and its variants are described in Sect. 6.6. Adaptive control is introduced in Sect. 6.7 to solve the problem of structural uncertainty, whereas the optimality and robustness issues are covered in Sect. 6.8. Since most controllers of robot manipulators are implemented by using microprocessors, the issues of digital implementation are discussed in Sect. 6.9. Finally, learning control, one popular approach to intelligent control, is illustrated in Sect. 6.10.

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Abbreviations

DOF:

degree of freedom

GAS:

global asymptotic stability

HJB:

Hamilton–Jacobi–Bellman

HJI:

Hamilton–Jacobi–Isaac

IOSS:

input-output-to-state stability

ISS:

input-to-state stability

MIMO:

multi-input multi-output

MRAC:

model reference adaptive control

PD:

proportional-derivative

PI:

policy iteration

PID:

proportional–integral–derivative

ROM:

read-only memory

SGAS:

semiglobal asymptotic stability

SGUUB:

semiglobal uniform ultimate boundedness

SISO:

single-input single-output

UUB:

uniform ultimate boundedness

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Author information

Authors and Affiliations

  1. Department of Mechanical Engineering, POSTECH, San 31 Hyojading, 790-784, Pohang, Korea

    Wankyun Chung Prof

  2. Department of Electrical Engineering, National Taiwan University, 106, Taipei, Taiwan, R.O.C.

    Li-Chen Fu PhD

Authors
  1. Wankyun Chung Prof
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  2. Li-Chen Fu PhD
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  3. Su-Hau Hsu
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Corresponding authors

Correspondence to Wankyun Chung Prof or Li-Chen Fu PhD .

Editor information

Editors and Affiliations

  1. PRISMA Lab, Dipartimento di Informatica e Sistemistica, Universitá degli Studi di Napoli Federico II, 80125, Napoli, Italy, Via Claudio 21

    Bruno Siciliano Prof.

  2. Artificial Intelligence Laboratory, Department of Computer Science, Stanford University, 94305-9010, Stanford, CA, USA

    Oussama Khatib Prof.

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© 2008 Springer-Verlag

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Chung, W., Fu, LC., Hsu, SH. (2008). Motion Control. In: Siciliano, B., Khatib, O. (eds) Springer Handbook of Robotics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30301-5_7

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  • DOI: https://doi.org/10.1007/978-3-540-30301-5_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-23957-4

  • Online ISBN: 978-3-540-30301-5

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