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Motion Control

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Springer Handbook of Robotics

Part of the book series: Springer Handbooks ((SHB))

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. 8.1. The joint and operational space control approaches, two different viewpoints on control of robot manipulators, are compared in Sect. 8.2. Independent joint control and proportional–integral–derivative (GlossaryTerm

PID

) control, widely adopted in the field of industrial robots, are presented in Sects. 8.3 and 8.4, respectively. Tracking control, based on feedback linearization, is introduced in Sect. 8.5. The computed-torque control and its variants are described in Sect. 8.6. Adaptive control is introduced in Sect. 8.7 to solve the problem of structural uncertainty, whereas the optimality and robustness issues are covered in Sect. 8.8. To compute suitable set point signals as input values for these motion controllers, Sect. 8.9 introduces reference trajectory planning concepts. Since most controllers of robot manipulators are implemented by using microprocessors, the issues of digital implementation are discussed in Sect. 8.10. Finally, learning control, one popular approach to intelligent control, is illustrated in Sect. 8.11.

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Abbreviations

1-D:

one-dimensional

D/A:

digital-to-analog

DC:

direct current

DOF:

degree of freedom

DSP:

digital signal processor

GAS:

global asymptotic stability

HJB:

Hamilton–Jacobi–Bellman

HJI:

Hamilton–Jacobi–Isaac

IOSS:

input-output-to-state stability

ISS:

input-to-state stability

MIMO:

multiple-input–multiple-output

MRAC:

model reference adaptive control

PD:

proportional–derivative

PID:

proportional–integral–derivative

PI:

propositional integral

SGAS:

semiglobal asymptotic stability

SGUUB:

semiglobal uniform ultimate boundedness

SISO:

single input single-output

UUB:

uniform ultimate boundedness

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Authors and Affiliations

  1. Robotics Laboratory, POSTECH, KIRO 410, San 31, Hyojadong, 790-784, Pohang, Korea

    Wan Kyun Chung

  2. Department of Electrical Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, 106, Taipei, Taiwan

    Li-Chen Fu

  3. Google Inc., 1600 Amphitheatre Parkway, CA 94043, Mountain View, USA

    Torsten Kröger

Authors
  1. Wan Kyun Chung
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  2. Li-Chen Fu
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  3. Torsten Kröger
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Correspondence to Wan Kyun Chung .

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, University of Naples Federico II, Via Claudio 21, 80125, Naples, Italy

    Bruno Siciliano

  2. Department of Computer Sciences, Artificial Intelligence Laboratory, Stanford University, 450 Serra Mall, CA 94305, Stanford, USA

    Oussama Khatib

Video-References

Video-References

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Gain change of the PID controller available from http://handbookofrobotics.org/view-chapter/08/videodetails/25

:

Safe human-robot cooperation available from http://handbookofrobotics.org/view-chapter/08/videodetails/757

:

Virtual whiskers – Highly responsive robot collision avoidance available from http://handbookofrobotics.org/view-chapter/08/videodetails/758

:

JediBot – Experiments in human-robot sword-fighting available from http://handbookofrobotics.org/view-chapter/08/videodetails/759

:

Different jerk limits of robot arm trajectories available from http://handbookofrobotics.org/view-chapter/08/videodetails/760

:

Sensor-based online trajectory generation available from http://handbookofrobotics.org/view-chapter/08/videodetails/761

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Chung, W.K., Fu, LC., Kröger, T. (2016). Motion Control. In: Siciliano, B., Khatib, O. (eds) Springer Handbook of Robotics. Springer Handbooks. Springer, Cham. https://doi.org/10.1007/978-3-319-32552-1_8

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