Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
An NC Code Based Machining Movement Simulation Method for a Parallel Robotic Machine Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Fuzzy PD-Type Iterative Learning Control of a Single Pneumatic Muscle Actuator

Authors : Da Ke, Qingsong Ai, Wei Meng, Congsheng Zhang, Quan Liu

Published in: Intelligent Robotics and Applications

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Pneumatic muscles actuator (PMA) is widely used in the field of rehabilitation robot for its good flexibility, light weight and high power/mass ratio as compared to traditional actuator. In this paper, a fuzzy logic-based PD-type iterative learning controller (ILC) is proposed to control the PMA to track a predefined trajectory more precisely during repetitive movements. In order to optimize the parameters of the learning law, fuzzy logic control is introduced into ILC to achieve smaller errors and faster convergence. A simulation experiment was first conducted by taking the PMA model fitted by support vector machine (SVM) as controlled target, which showed that the proposed method achieved a better tracking performance than traditional PD-type ILC. A satisfactory control effect was also obtained when fuzzy PD-type ILC was applied to actual PMA control experiment. Result showed that it takes 25 iterations for the maximum error of trajectory converges to a minimum of about 0.2.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter A Method of Computed-Torque Deviation Coupling Control Based on Friction Compensation Analysis
next chapter Cascade Control for SEAs and Its Performance Analysis
Literature
1.
Meng, W., Liu, Q., Zhou, Z., et al.: Recent development of mechanisms and control strategies for robot-assisted lower limb rehabilitation. Mechatronics 31, 132–145 (2015)CrossRef
2.
Liu, Y., Wang, T., Fan, W., et al.: Model-free adaptive control for the ball-joint robot driven by PMA group. Robot 35(2), 129–134 (2013). (in Chinese)CrossRef
3.
Shao, Z., Xiang, Z.: Iterative learning control for non-linear switched discrete-time systems. IET Control Theory Appl. 11(6), 883–889 (2017)CrossRefMathSciNet
4.
Arimoto, S., Kawamura, S., Miyazaki, F.: Better operation of robots by learning. J. Field Robot. 1(2), 123–140 (1984)
5.
Wang, Y., Chien, C.: A fuzzy iterative learning control for nonlinear discrete-time systems with unknown control directions. In: 54th IEEE Conference on Decision and Control, pp. 3081–3086. Institute of Electrical and Electronics Engineers Inc, Osaka (2015)
6.
Czajkowski, A., Patan, M., Patan, K.: Design of iterative learning control by the means of state space neural networks. In: 3rd Conference on Control and Fault-Tolerant Systems, pp. 293–298. IEEE Computer Society, Barcelona (2016)
7.
Liu, B., Zhou, W.: Adaptive iterative learning control for a class of MIMO discrete-time nonlinear systems. In: 11th International Conference on Computer Science and Education, pp. 832–838. Institute of Electrical and Electronics Engineers Inc, Nagoya (2016)
8.
Meng, W., Xie, S., Liu, Q., et al.: Robust iterative feedback tuning control of a compliant rehabilitation robot for repetitive ankle training. IEEE/ASME Trans. Mechatron. 22(1), 173–184 (2016)CrossRef
9.
Schindele, D., Aschemann, H.: Norm-optimal iterative learning control for a high-speed linear axis with pneumatic muscles. In: 8th IFAC Symposium on Nonlinear Control System, pp. 463–468. IFAC Secretariat, Bologna (2010)
10.
Schindele, D., Aschemann, H.: ILC for a fast linear axis driven by pneumatic muscle actuators. In: 2011 International Conference on Mechatronics, pp. 967–972. IEEE Computer Society, Istanbul (2011)
11.
Schindele, D., Aschemann, H.: P-type ILC with phase lead compensation for a pneumatically driven parallel robot. In: 2012 American Control Conference, pp. 5484–5489. Institute of Electrical and Electronics Engineers Inc, Montreal (2012)
12.
Sugar, T., He, J., Koeneman, E., et al.: Design and control of RUPERT: a device for robotic upper extremity repetitive therapy. IEEE Trans. Neural Syst. Rehabil. Eng. 15(3), 336–346 (2007)CrossRef
13.
Balasubramanian, S., Wei, R., He, J.: Rupert closed loop control design. In: 2008 Engineering in Medicine and Biology Society. 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 3467–3470. Institute of Electrical and Electronic Engineers Computer Society, Vancouver (2008)
14.
Wei, R., Balasubramanian, S., Xu, L., et al.: Adaptive iterative learning control design for RUPERT IV. In: 2nd Biennial IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics, pp. 647–652. Institute of Electrical and Electronic Engineers Computer Society, Scottsdale (2008)
15.
Dursun, E., Durdu, A.: Position control by using PD type fuzzy logic: Experimental study on rotary servo system. In: 8th Conference on Electronics, Computers and Artificial Intelligence, pp. 1–6. Institute of Electrical and Electronics Engineers Inc. Ploiesti (2016)
16.
Cao, J., Xie, S.Q., Zhang, M., Das, R.: A new dynamic modelling algorithm for pneumatic muscle actuators. In: Zhang, X., Liu, H., Chen, Z., Wang, N. (eds.) ICIRA 2014. LNCS, vol. 8918, pp. 432–440. Springer, Cham (2014). doi:10.​1007/​978-3-319-13963-0_​44
17.
Yao, B.: Research on the Position Control Strategy of Pneumatic Muscle Actuator (PMA) and the PMA-actuated Parallel Robot. Wuhan University of Technology, Wuhan (2012)
Metadata
Title
Fuzzy PD-Type Iterative Learning Control of a Single Pneumatic Muscle Actuator
Authors
Da Ke
Qingsong Ai
Wei Meng
Congsheng Zhang
Quan Liu
Copyright Year
2017
Book
Intelligent Robotics and Applications

Print ISBN: 978-3-319-65291-7

Electronic ISBN: 978-3-319-65292-4

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-65292-4_70

Premium Partner

    Image Credits