Motion Control

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 (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.

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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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Robotics LaboratoryPOSTECHPohangKorea
  2. 2.Department of Electrical EngineeringNational Taiwan UniversityTaipeiTaiwan
  3. 3.Google Inc.Mountain ViewUSA

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