Neural network-based H∞ tracking control for robotic systems

Access Full Text

Neural network-based H∞ tracking control for robotic systems

For access to this article, please select a purchase option:

Buy article PDF
£12.50
(plus tax if applicable)
Add to cart
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)
Add to cart

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IEE Proceedings - Control Theory and Applications — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

IEE
Published

An adaptive H∞ tracking control design is proposed for robotic systems under plant uncertainties and external disturbances. Three important control design techniques, i.e. nonlinear H∞ tracking theory, variable structure control algorithm and neural network control design, are combined to construct a hybrid adaptive-robust tracking control scheme which ensures that the joint positions track the desired reference signals. It is shown that an H∞ tracking control is achieved, in the sense that all variables of the closed-loop system are bounded and the effect due to the external disturbance on the tracking error can be attenuated to any pre-assigned level. The solution of H∞ control performance relies only on an algebraic Riccati-like matrix equation. A simple design algorithm is proposed such that the proposed adaptive neural network-based H∞ tracking controller can easily be implemented. A simulation example demonstrates the effectiveness of the proposed control algorithm.

Inspec keywords: robust control; Riccati equations; nonlinear control systems; uncertain systems; manipulators; variable structure systems; neurocontrollers; H∞ control; control system synthesis; adaptive control; closed loop systems; matrix algebra; position control

Other keywords: variable structure control algorithm; neural network-based H∞ tracking control; robotic systems; plant uncertainties; adaptive H∞ tracking control design; nonlinear H∞ tracking theory; hybrid adaptive-robust tracking control scheme; external disturbances; algebraic Riccati-like matrix equation

Subjects: Control system analysis and synthesis methods; Multivariable control systems; Nonlinear control systems; Neurocontrol; Manipulators; Spatial variables control; Self-adjusting control systems; Optimal control; Neural computing techniques; Algebra; Stability in control theory

References

    1. 1)
      • Y.C. CHANG , B.S. CHEN . A nonlinear adaptive H∞ tracking control design in robotic systems via neural networks. IEEE Trans., Control Syst. Technol. , 13 - 29
    2. 2)
      • C. ABDALLAH , D. DAWSON , P. DORATO , M. JAMSHIDI . Survey of robust control of rigid robots. IEEE Control Syst. Mag. , 24 - 30
    3. 3)
      • A.J. VAN DER SCHAFT . L2-gain analysis of nonlinear systems and nonlinear state feedback H∞ control. IEEE Trans., Automatic Control , 770 - 784
    4. 4)
      • L. BEHERA , S. CHAUDHURY , M. GOPAL . Neuro-adaptive hybrid controller for robot-manipulator tracking control. IEE Proc. Control Theory Appl. , 270 - 275
    5. 5)
      • R. ORTEGA , M.W. SPONG . Adaptive motion control of rigid robots: a tutorial. Automatica , 877A - 888
    6. 6)
      • J.T. SPOONER , K.M. PASSINO . Stable adaptive control using fuzzy systems and neural networks. IEEE Trans., Fuzzy Syst. , 339 - 359
    7. 7)
      • J.J.E. SLOTINE . (1991) , Applied nonlinear control.
    8. 8)
      • H. YU , L.D. SENEVIRATNE , S.W.E. EARLES . Exponentially stable robust control law for robot manipulators. IEEE Proc. Control Theory Appl. , 389 - 395
    9. 9)
      • K.S. NARENDRA , K. PARTHASARATHY . Identification and control of dynamical systems using neural networks. IEEE Trans., Neural Netw. , 4 - 27
    10. 10)
      • H.K. KHALIL . Adaptive output feedback control of nonlinear systems represented by input-output models. IEEE Trans., Automatic Control , 177 - 188
    11. 11)
      • R.M. SANNER , J.J.E. SLOTINE . Gaussian networks for direct adaptive control. IEEE Trans., Neural Netw. , 837 - 863
    12. 12)
      • M.W. SPONG , M. VIDYASAGAR . (1989) , Robot dynamics and control.
    13. 13)
      • F.L. LEWIS , K. LIU , A. YESILDIREK . Neural net robot controller with guaranteed tracking performance. IEEE Trans., Neural Netw. , 703 - 715
    14. 14)
      • R. CARELLI , E.F. CAMACHO , D. PATIÑO . A neural network based feedforward adaptive controller for robots. IEEE Trans., Syst., Man Cybern. , 1281 - 1288
    15. 15)
      • M.M. POLYCARPOU . Stable adaptive neural control scheme for nonlinear systems. IEEE Trans., Automatic Control , 447 - 451
    16. 16)
      • Y.H. KIM , F.L. LEWIS . Neural network output feedback control of robot manipulators. IEEE Trans., Robotics Automation , 301 - 309
    17. 17)
      • M. SAAD , L.A. DESSAINT , P. BIGRAS , K. AL-HADDAD . Adaptive versus neural adaptive control: application to robotics. Int. J. Adaptive Control Sig. Process. , 223 - 236
    18. 18)
      • T. BASAR , P. BERHARD . (1990) , -optimal control and related minimax problems.
    19. 19)
      • K. HORNIK , M. STINCHCOMBE , H. WHITE . Multilayer feedforward networks are universal approximations. Neural Netw. , 359 - 366
http://iet.metastore.ingenta.com/content/journals/10.1049/ip-cta_20000257
Loading

Related content

content/journals/10.1049/ip-cta_20000257
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading