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Springer Handbook of Robotics pp 163–194Cite as

Motion Control

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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

References

  1. C. Canudas de Wit, B. Siciliano, G. Bastin: Theory of Robot Control (Springer, London 1996)

    Book  MATH  Google Scholar 

  2. J.J. Craig: Adaptive Control of Mechanical Manipulators, Ph.D. Thesis (UMI Dissertation Information Service, Ann Arbor 1986)

    Book  Google Scholar 

  3. R.J. Schilling: Fundametals of Robotics: Analysis and Control (Prentice Hall, Englewood Cliffs 1989)

    Google Scholar 

  4. L. Sciavicco, B. Siciliano: Modeling and Control of Robot Manipulator (McGraw-Hill, New York 1996)

    MATH  Google Scholar 

  5. M.W. Spong, M. Vidyasagar: Robot Dynamics and Control (Wiley, New York 1989)

    Google Scholar 

  6. M.W. Spong, F.L. Lewis, C.T. Abdallah (Eds.): Robot Control (IEEE, New York 1989)

    MATH  Google Scholar 

  7. C.H. An, C.G. Atkeson, J.M. Hollerbach: Model–Based Control of a Robot Manipulator (MIT Press, Cambridge, 1988)

    Google Scholar 

  8. R.M. Murray, Z. Xi, S.S. Sastry: A Mathematical Introduction to Robotic Manipulation (CRC, Boca Raton 1994)

    MATH  Google Scholar 

  9. T. Yoshikawa: Foundations of Robotics (MIT Press, Cambridge 1990)

    Google Scholar 

  10. O. Khatib: A unified approach for motion and force control of robot manipulators: The operational space formulation, IEEE J. Robotics Autom. 3(1), 43–53 (1987)

    Article  Google Scholar 

  11. J.Y.S. Luh, M.W. Walker, R.P.C. Paul: Resolved–acceleration control of mechanical manipulator, IEEE Trans. Autom. Control 25(3), 468–474 (1980)

    Article  MATH  Google Scholar 

  12. S. Arimoto, F. Miyazaki: Stability and robustness of PID feedback control for robot manipulators of sensory capability. In: Robotics Research, ed. by M. Brady, R. Paul (MIT Press, Cambridge 1984) pp. 783–799

    Google Scholar 

  13. L.C. Fu: Robust adaptive decentralized control of robot manipulators, IEEE Trans. Autom. Control 37(1), 106–110 (1992)

    Article  MathSciNet  Google Scholar 

  14. H. Seraji: Decentralized adaptive control of manipulators: Theory, simulation, and experimentation, IEEE Trans. Robotics Autom. 5(2), 183–201 (1989)

    Article  Google Scholar 

  15. J.G. Ziegler, N.B. Nichols: Optimum settings for automatic controllers, Trans. ASME 64, 759–768 (1942)

    Google Scholar 

  16. Y. Choi, W.K. Chung: PID Trajectory Tracking Control for Mechanical Systems, Lecture Notes in Control and Information Sciences, Vol. 289 (Springer, New York 2004)

    Book  MATH  Google Scholar 

  17. R. Kelly: PD control with desired gravity compensation of robot manipulators: A review, Int. J. Robotics Res. 16(5), 660–672 (1997)

    Article  Google Scholar 

  18. M. Takegaki, S. Arimoto: A new feedback method for dynamic control of manipulators, Trans. ASME J. Dyn. Syst. Meas, Control 103, 119–125 (1981)

    MATH  Google Scholar 

  19. P. Tomei: Adaptive PD controller for robot manipulators, IEEE Trans. Robotics Autom. 7(4), 565–570 (1991)

    Article  Google Scholar 

  20. R. Ortega, A. Loria, R. Kelly: A semi-globally stable output feedback PI${}^{2}$ D regulator for robot manipulators, IEEE Trans. Autom. Control 40(8), 1432–1436 (1995)

    Article  MATH  Google Scholar 

  21. D. Angeli: Input-to-State stability of PD-controlled robotic systems, Automatica 35, 1285–1290 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  22. J.A. Ramirez, I. Cervantes, R. Kelly: PID regulation of robot manipulators: Stability and performance, Syst. Control Lett. 41, 73–83 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  23. R. Kelly: Global positioning of robot manipulators via PD control plus a class of nonlinear integral actions, IEEE Trans. Autom. Control 43(7), 934–937 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  24. Z. Qu, J. Dorsey: Robust tracking control of robots by a linear feedback law, IEEE Trans. Autom. Control 36(9), 1081–1084 (1991)

    Article  MathSciNet  MATH  Google Scholar 

  25. H. Berghuis, H. Nijmeijer: Robust control of robots via linear estimated state feedback, IEEE Trans. Autom. Control 39(10), 2159–2162 (1994)

    Article  MathSciNet  MATH  Google Scholar 

  26. Y. Choi, W.K. Chung, I.H. Suh: Performance and $\mathcal{H}_{\infty}$ optimality of PID trajectory tracking controller for Lagrangian systems, IEEE Trans. Robotics Autom. 17(6), 857–869 (2001)

    Article  Google Scholar 

  27. K. Aström, T. Hagglund: PID Controllers: Theory, Design, and Tuning (Instrument Society of America, Research Triangle Park 1995)

    Google Scholar 

  28. C.C. Yu: Autotuning of PID Controllers: Relay Feedback Approach (Springer, London 1999)

    Book  MATH  Google Scholar 

  29. F.L. Lewis, C.T. Abdallah, D.M. Dawson: Control of Robot Manipulators (Macmillan, New York 1993)

    Google Scholar 

  30. A. Isidori: Nonlinear Control Systems: An Introduction, Lecture Notes in Control and Information Sciences, Vol. 72 (Springer, New York 1985)

    Book  MATH  Google Scholar 

  31. H. Berghuis, H. Nijmeijer: A passivity approach to controller–observer design for robots, IEEE Trans. Robotics Autom. 9, 740–754 (1993)

    Article  Google Scholar 

  32. J.J. Slotine, W. Li: On the adaptive control of robot manipulators, Int. J. Robotics Res. 6(3), 49–59 (1987)

    Article  Google Scholar 

  33. G. Liu, A.A. Goldenberg: Comparative study of robust saturation–based control of robot manipulators: analysis and experiments, Int. J. Robotics Res. 15(5), 473–491 (1996)

    Article  Google Scholar 

  34. D.M. Dawson, M. Grabbe, F.L. Lewis: Optimal control of a modified computed–torque controller for a robot manipulator, Int. J. Robotics Autom. 6(3), 161–165 (1991)

    Google Scholar 

  35. D.M. Dawson, Z. Qu, J. Duffie: Robust tracking control for robot manipulators: Theory, simulation and implementation, Robotica 11, 201–208 (1993)

    Article  Google Scholar 

  36. A. Jaritz, M.W. Spong: An experimental comparison of robust control algorithms on a direct drive manipulator, IEEE Trans. Control Syst. Technol. 4(6), 627–640 (1996)

    Article  Google Scholar 

  37. A. Isidori: Nonlinear Control Systems, 3rd edn. (Springer, New York 1995)

    Book  MATH  Google Scholar 

  38. J.J. Slotine, W. Li: Applied Nonlinear Control (Prentice Hall, Englewood Cliffs 1991)

    MATH  Google Scholar 

  39. W.J. Rugh: Linear System Theory, 2nd edn. (Prentice Hall, Upper Saddle River 1996)

    MATH  Google Scholar 

  40. M.W. Spong, M. Vidyasagar: Robust microprocessor control of robot manipulators, Automatica 23(3), 373–379 (1987)

    Article  MATH  Google Scholar 

  41. H.K. Khalil: Nonlinear Systems, 3rd edn. (Prentice Hall, Upper Saddle River 2002)

    MATH  Google Scholar 

  42. M. Vidysagar: Nonlinear Systems Analysis, 2nd edn. (Prentice Hall, Englewood Ciffs 1993)

    Google Scholar 

  43. J.T. Wen: A unified perspective on robot control: The energy Lyapunov function approach, Int. J. Adapt. Control Signal Process. 4, 487–500 (1990)

    Article  MATH  Google Scholar 

  44. R. Ortega, M.W. Spong: Adaptive motion control of rigid robots: A tutorial, Automatica 25(6), 877–888 (1989)

    Article  MathSciNet  MATH  Google Scholar 

  45. N. Sadegh, R. Horowitz: Stability and robustness analysis of a class of adaptive contollers for robotic manipulators, Int. J. Robotics Res. 9(3), 74–92 (1990)

    Article  Google Scholar 

  46. S. Dubowsky, D.T. DesForges: The application of model-reference adaptive control to robotic manipulators, ASME J. Dyn. Syst. Meas. Control 37(1), 106–110 (1992)

    MATH  Google Scholar 

  47. S.H. Hsu, L.C. Fu: A fully adaptive decentralized control of robot manipulators, Automatica 42, 1761–1767 (2008)

    Article  MATH  Google Scholar 

  48. A. Balestrino, G. de Maria, L. Sciavicco: An adaptive model following control for robotic manipulators, ASME J. Dyn. Syst. Meas. Control 105, 143–151 (1983)

    Article  MATH  Google Scholar 

  49. S. Nicosia, P. Tomei: Model reference adaptive control algorithms for industrial robots, Automatica 20, 635–644 (1984)

    Article  MATH  Google Scholar 

  50. R. Horowitz, M. Tomizuka: An adaptive control scheme for mechanical manipulators-Compensation of nonlinearity and decoupling control, ASME J. Dyn. Syst. Meas. Control 108, 127–135 (1986)

    Article  MATH  Google Scholar 

  51. I.D. Laudau: Adaptive Control: The Model Reference Approach (Dekker, New York 1979)

    Google Scholar 

  52. R. Lozano, C. Canudas de Wit: Passivity based adaptive control for mechanical manipulators using LS type estimation, IEEE Trans. Autom. Control 35(12), 1363–1365 (1990)

    Article  MATH  Google Scholar 

  53. B. Brogliato, I.D. Laudau, R. Lozano: Passive least squares type estimation algorithm for direct adaptive control, Int. J. Adapt. Control Signal Process. 6, 35–44 (1992)

    Article  MATH  Google Scholar 

  54. R. Johansson: Adaptive control of robot manipulator motion, IEEE Trans. Robotics Autom. 6(4), 483–490 (1990)

    Article  Google Scholar 

  55. M.W. Walker: Adaptive control of manipulators containing closed kinematic loops, IEEE Trans. Robotics Autom. 6(1), 10–19 (1990)

    Article  Google Scholar 

  56. J.S. Reed, P.A. Ioannou: Instability analysis and robust adaptive control of robotic manipulators, IEEE Trans. Autom. Control 5(3), 74–92 (1989)

    Google Scholar 

  57. G. Tao: On robust adaptive control of robot manipulators, Automatica 28(4), 803–807 (1992)

    Article  MathSciNet  MATH  Google Scholar 

  58. H. Berghuis, R. Ogata, H. Nijmeijer: A robust adaptive controller for robot manipulators, Proc. IEEE Int. Conf. Robotics Autom. (ICRA) (1992) pp. 1876–1881

    Google Scholar 

  59. R. Johansson: Quadratic optimization of motion coordination and control, IEEE Trans. Autom. Control 35(11), 1197–1208 (1990)

    Article  MathSciNet  MATH  Google Scholar 

  60. Z. Qu, D.M. Dawson: Robust Tracking Control of Robot Manipulators (IEEE, Piscataway 1996)

    MATH  Google Scholar 

  61. P. Dorato, C. Abdallah, V. Cerone: Linear-Quadratic Control (Prentice Hall, Upper Saddle River 1995)

    MATH  Google Scholar 

  62. A. Locatelli: Optimal Control: An Introduction (Birkhäuser, Basel 2001)

    Book  MATH  Google Scholar 

  63. A. Isidori: Feedback control of nonlinear systems, Int. J. Robust Nonlin. Control 2, 291–311 (1992)

    Article  MATH  Google Scholar 

  64. A.J. der van Schaft: Nonlinear state space $\mathcal{H}_{\infty}$ control theory. In: Essays on Control: Perspective in Theory and its Applications, ed. by H.L. Trentelman, J.C. Willems (Birkhäuser, Basel 1993) pp. 153–190

    Chapter  Google Scholar 

  65. A.J. der van Schaft: $L_2$-gain analysis of nonlinear systems and nonlinear state feedback $\mathcal{H}_{\infty}$ control, IEEE Trans. Autom. Control 37(6), 770–784 (1992)

    Article  MathSciNet  MATH  Google Scholar 

  66. J. Park, W.K. Chung, Y. Youm: Analytic nonlinear $\mathcal{H}_{\infty}$ inverse-optimal control for Euler–Lagrange system, IEEE Trans. Robotics Autom. 16(6), 847–854 (2000)

    Article  Google Scholar 

  67. B.S. Chen, T.S. Lee, J.H. Feng: A nonlinear $\mathcal{H}_{\infty}$ control design in robotics systems under parametric perturbation and external disturbance, Int. J. Control 59(12), 439–461 (1994)

    Article  MATH  Google Scholar 

  68. J. Park, W.K. Chung: Design of a robust $\mathcal{H}_{\infty}$ PID control for industrial manipulators, ASME J. Dyn. Syst. Meas. Control 122(4), 803–812 (2000)

    Article  Google Scholar 

  69. R.H. Castain, R.P. Paul: An on-line dynamic trajectory generator, Int. J. Robotics Res. 3(1), 68–72 (1984)

    Article  Google Scholar 

  70. T. Kröger: On-Line Trajectory Generation in Robotic Systems, Springer Tracts in Advanced Robotics, Vol. 58 (Springer, Berlin, Heidelberg 2010)

    Book  MATH  Google Scholar 

  71. D. Simon, C. Isik: A trigonometric trajectory generator for robotic arms, Int. J. Control 57(3), 505–517 (1993)

    Article  MathSciNet  MATH  Google Scholar 

  72. L. Biagiotti, C. Melchiorri: Trajectory Planning for Automatic Machines and Robots (Springer, Berlin, Heidelberg 2008)

    Google Scholar 

  73. J.E. Bobrow: Optimal robot path planning using the minimum-time criterion, IEEE J. Robotics Autom. 4(4), 443–450 (1988)

    Article  Google Scholar 

  74. K.G. Shin, N.D. McKay: Minimum-time control of robotic manipulators with geometric path constraints, IEEE Trans. Autom. Control 30(5), 531–541 (1985)

    Article  MATH  Google Scholar 

  75. F. Pfeiffer, R. Johanni: A concept for manipulator trajectory planning, Proc. Int. IEEE Conf. Robotics Autom. (ICRA) (1986) pp. 1399–1405

    Google Scholar 

  76. W. Khalil, E. Dombre: Trajectory generation. In: Modeling, Identification and Control of Robots, ed. by W. Khalil, E. Dombre (Butterworth-Heinemann, Oxford 2004)

    MATH  Google Scholar 

  77. A.I. Kostrikin, Y.I. Manin: Linear Algebra and Geometry (Gordon and Breach Sci. Publ., Amsterdam 1997)

    MATH  Google Scholar 

  78. M.E. Kahn, B. Roth: The near-minimum-time control of open-loop articulated kinematic chains, ASME J. Dyn. Syst. Meas. Control 93, 164–172 (1971)

    Article  Google Scholar 

  79. M. Brady: Trajectory planning. In: Robot Motion: Planning and Control, ed. by M. Brady, J.M. Hollerbach, T.L. Johnson, T. Lozano-Pérez, M.T. Mason (MIT Press, Cambridge 1982)

    Google Scholar 

  80. R.P.C. Paul: Manipulator cartesian path control. In: Robot Motion: Planning and Control, ed. by M. Brady, J.M. Hollerbach, T.L. Johnson, T. Lozano-Pérez, M.T. Mason (MIT Press, Cambridge 1982)

    Google Scholar 

  81. R.H. Taylor: Planning and execution of straight-line manipulator trajectories. In: Robot Motion: Planning and Control, ed. by M. Brady, J.M. Hollerbach, T.L. Johnson, T. Lozano-Pérez, M.T. Mason (MIT Press, Cambridge 1982)

    Google Scholar 

  82. C.-S. Lin, P.-R. Chang, J.Y.S. Luh: Formulation and optimization of cubic polynomial joint trajectories for industrial robots, IEEE Trans. Autom. Control 28(12), 1066–1074 (1983)

    Article  MATH  Google Scholar 

  83. J.M. Hollerbach: Dynamic scaling of manipulator trajectories, ASME J. Dyn. Syst. Meas. Control 106(1), 102–106 (1984)

    Article  MATH  Google Scholar 

  84. K.J. Kyriakopoulos, G.N. Sridis: Minimum jerk path generation, Proc. IEEE Int. Conf. Robotics Autom. (ICRA) (1988) pp. 364–369

    Google Scholar 

  85. J.-J.E. Slotine, H.S. Yang: Improving the efficiency of time-optimal path-following algorithms, IEEE Trans. Robotics Autom. 5(1), 118–124 (1989)

    Article  Google Scholar 

  86. Z. Shiller, H.-H. Lu: Computation of path constrained time optimal motions with dynamic singularities, ASME J. Dyn. Syst. Meas. Control 114(1), 34–40 (1992)

    Article  MATH  Google Scholar 

  87. P. Fiorini, Z. Shiller: Time optimal trajectory planning in dynamic environments, Proc. IEEE Int. Conf. Robotics Autom. (ICRA) (1996) pp. 1553–1558

    Chapter  Google Scholar 

  88. O. Dahl, L. Nielsen: Torque limited path following by on-line trajectory time scaling, Proc. IEEE Int. Conf. Robotics Autom. (ICRA) (1989) pp. 1122–1128

    Google Scholar 

  89. B. Cao, G.I. Dodds, G.W. Irwin: Time-optimal and smooth constrained path planning for robot manipulators, Proc. IEEE Int. Conf. Robotics Autom. (ICRA) (1994) pp. 1853–1858

    Google Scholar 

  90. B. Cao, G.I. Dodds, G.W. Irwin: A practical approach to near time-optimal inspection-task-sequence planning for two cooperative industrial robot arms, Int. J. Robotics Res. 17(8), 858–867 (1998)

    Article  Google Scholar 

  91. D. Constantinescu, E.A. Croft: Smooth and time-optimal trajectory planning for industrial manipulators along specified paths, J. Robotics Syst. 17(5), 233–249 (2000)

    Article  MATH  Google Scholar 

  92. S. Macfarlane, E.A. Croft: Jerk-bounded manipulator trajectory planning: Design for real-time applications, IEEE Trans. Robotics Autom. 19(1), 42–52 (2003)

    Article  Google Scholar 

  93. R.L. Andersson: A Robot Ping-Pong Player: Experiment in Real-Time Intelligent Control (MIT Press, Cambridge 1988)

    Google Scholar 

  94. R.L. Andersson: Aggressive trajectory generator for a robot ping-pong player, IEEE Control Syst. Mag. 9(2), 15–21 (1989)

    Article  Google Scholar 

  95. J. Lloyd, V. Hayward: Trajectory generation for sensor-driven and time-varying tasks, Int. J. Robotics Res. 12(4), 380–393 (1993)

    Article  Google Scholar 

  96. K. Ahn, W.K. Chung, Y. Yourn: Arbitrary states polynomial-like trajectory (ASPOT) generation, Proc. IEEE 30th Annu. Conf. Ind. Electron. Soc. (2004) pp. 123–128

    Google Scholar 

  97. X. Broquère, D. Sidobre, I. Herrera-Aguilar: Soft motion trajectory planner for service manipulator robot, Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst. (IROS) (2008) pp. 2808–2813

    Google Scholar 

  98. R. Haschke, E. Weitnauer, H. Ritter: On-line planning of time-optimal, jerk-limited trajectories, Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst. (IROS) (2008) pp. 3248–3253

    Google Scholar 

  99. J.J. Craig: Introduction to Robotics: Mechanics and Control (Prentice Hall, Upper Saddle River 2003)

    Google Scholar 

  100. K.S. Fu, R.C. Gonzalez, C.S.G. Lee: Robotics: Control, Sensing, Vision and Intelligence (McGraw-Hill, New York 1988)

    Google Scholar 

  101. M.W. Spong, S.A. Hutchinson, M. Vidyasagar: Robot Modeling and Control (Wiley, New York 2006)

    Google Scholar 

  102. S. Arimoto: Mathematical theory or learning with application to robot control. In: Adaptive and Learning Control, ed. by K.S. Narendra (Plenum, New York 1986) pp. 379–388

    Google Scholar 

  103. S. Kawamura, F. Miyazaki, S. Arimoto: Realization of robot motion based on a learning method, IEEE Trans. Syst. Man. Cybern. 18(1), 126–134 (1988)

    Article  Google Scholar 

  104. G. Heinzinger, D. Frewick, B. Paden, F. Miyazaki: Robust learning control, Proc. IEEE Int. Conf. Decis. Control (1989)

    Google Scholar 

  105. S. Arimoto: Robustness of learning control for robot manipulators, Proc. IEEE Int. Conf. Decis. Control (1990) pp. 1523–1528

    Google Scholar 

  106. S. Arimoto, T. Naiwa, H. Suzuki: Selective learning with a forgetting factor for robotic motion control, Proc. IEEE Int. Conf. Decis. Control (1991) pp. 728–733

    Google Scholar 

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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

:

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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