Adaptive Inverse-Dynamic and Neuro-Inverse-Dynamic Active Vibration Control of a Single-Link Flexible Manipulator

Abstract

This paper presents investigations into the development of adaptive inverse-dynamic and neuro-inverse-dynamic control strategies for a flexible manipulator system employing a combined collocated and non-collocated control structure. Collocated control is utilized to track the position of the system while the non-collocated inverse and neuro-inverse control are utilized to reduce the vibration of the system. The controllers are developed in two phases: a collocated position control loop using proportional-derivative feedback control is developed and combined first with an adaptive inverse non-collocated control loop using a recursive least-squares algorithm and then with a neuro-inverse model using a multi-layered perceptron neural network. The problem of instability of the non-collocated control loop arising from the non-minimum phase characteristics of the plant is solved in the former case by reflecting the non-invertible plant zeros into the stability region. In the case of the neuro-inverse model, the problem of instability of the control loop is accounted for through the neuro-inverse learning process. The performances of both the proposed control strategies are assessed within a simulation environment of a single-link flexible manipulator and it is demonstrated that a significant reduction in the level of structural vibration of the system is achieved with both techniques. The significance of the neuro-inverse model approach in achieving stable control is demonstrated.

Keywords

active vibration control adaptive inverse control flexible manipulator neuro-inverse control joint-based control non-minimum phase

Get full access to this article

View all access options for this article.

References

Azad

A. K. M.

Analysis and design of control mechanisms for flexible manipulator systems. PhD Thesis, Department of Automatic Control and Systems Engineering, The University of Sheffield, Sheffield, 1994.

Book

W. J.

Majette

Controller design for flexile distributed parameter mechanical arm via combined state-space and frequency domain techniques. Trans. ASME, J. Dynamic Systems, Measmt Control, 1983, 105 (4), 245–254.

BBC, 2003a; http://news.bbc.co.uk/1/low/uk/scotland/1815315.stm

Gharooni

Heller

Tokhi

M. O.

A new hybrid spring orthosis for controlling hip and flexion in the swing phase. IEEE Trans. Neural Systems Rehabil. Engng, 2001, 9 (1), 106–107.

Yamano

Konno

Uchiyama

Experiments on capturing a floating object by two flexible manipulators. In Proceedings of the IEEE International Conference on

Robotics and Automation, San Francisco, California, USA, 2000, pp. 482–487 (IEEE, New York).

Jamshidi

Akbarzadeh

T. M.

Vadiee

Experimental test-beds for intelligent robot controllers based on soft-computing approaches with application to waste management. Technical Report WERC 1207, New Mexico Waste-Management Education and Research Consortium, USA, 1998.

Piedboeuf

J.-C.

Farooq

Bayoumi

M. M.

Labinaz

Argoun

M. B.

Modelling and control of flexible manipulators - revisited. Proceedings of the 36th Midwest Symposium on

Circuits and Systems, Detroit, Michigan, USA, 1993, pp. 1480–1483 (IEEE, New York).

Yurkovich

Flexibility effects on performance and control. Robot Control, 1992, Part 8, pp. 321–323 (IEEE, New York).

10.

Menq

C.-H.

Chen

J.-S.

Dynamic model and payload-adaptive control of a flexible manipulator. In Proceedings of the IEEE Conference on

Robotics and Automation, Philadelphia, Pennsylvania, USA, 1998, pp. 448–453 (IEEE, New York).

11.

Poerwanto

Dynamic simulation and control of flexible manipulator systems. PhD thesis, Department of Automatic Control and Systems Engineering, The University of Sheffield, Sheffield, 1998.

12.

Talebi

H. A.

Khorasani

Patel

R. V.

Neural network based control schemes for flexible-link manipulator: Simulations and experiments. Neural Networks, 1998, 11, 1357–1377.

13.

Stadenny

Belanger

Robot manipulator control by acceleration feedback: Stability, design and performance issues. In Proceedings of the IEEE Conference on

Decision and Control, Athens, Greece, 1996, pp. 80–85 (IEEE, New York).

14.

Kotnik

P. T.

Yorkovich

Özgüner

Ü.

Acceleration feedback for control of a flexible manipulator arm. J. Robotics Systems, 1988, 5 (3), 181–196.

15.

Tokhi

M. O.

Azad

A. K. M.

Control of flexible manipulator systems. Proc. Instn Mech. Engrs, Part I: Systems and Control Engineering, 1996, 210, 113–130.

16.

Chen

Wen

J. T.

A neural controller for the tracking control of flexible arms. In Proceedings of the IEW International Conference on

Neural Networks, 1993, pp. 749–754.

17.

Newton

R. T.

Neural network control of a single-link flexible manipulators. IEEE Control System Mag., 1993, 12, 14–22.

18.

Donne

J. D.

Özgüner

Ü.

Neural control of a flexible-link manipulator. In Proceedings of the IEEE International Conference on

Neural Networks, 1994, pp. 2327–2332 (IEEE, New York).

19.

Talebi

H. A.

Patel

R. V.

Khorasani

Experimental evaluation of neural network based controllers for tracking the tip position of a flexible-link manipulator. In Proceedings of the IEEE International Conference on

Robotics and Automation, Albuquerque, New Mexico, USA, 1997, pp. 3300–3305 (IEEE, New York).

20.

Song

Koivo

A. J.

Neural network model based control of a flexible link manipulator. In Proceedings of the IEEE International Conference on

Robotics and Automation, Leuven, Belgium, 1998, pp. 812–817 (IEEE, New York).

21.

Yesildierk

Vandegrift

M. W.

Lewis

F. L.

A neural network controller for flexible-link robots. In Proceedings of the IEEE International Symposium on

Intelligent Control, Columbus, Ohio, USA, 1994, pp. 63–68 (IEEE, New York).

22.

S. S.

Lee

T. H.

Tan

E. G.

Adaptive neural network control of flexible link robots based on singular perturbation. In Proceedings of the IEEE International Conference on

Control Applications, Hardford, Connecticut, USA, 1997, pp. 365–369 (IEEE, New York).

23.

Talebi

H. A.

Patel

R. V.

Khorasani

Inverse dynamics control of flexible-link manipulators using neural networks. In Proceedings of the IEEE International Conference on

Robotics and Automation, Leuven, Belgium, 1998, pp. 806–811 (IEEE, New York).

24.

Hagan

M. T.

Menhaj

M. B.

Training feedforward networks with Marquardt algorithm. IEEE Trans. Neural Networks, 1994, 5 (6), 989–993.

25.

Marquardt

D. W.

An algorithm for least-squares estimation of nonlinear parameters. J. Soc. Ind. Appl. Math., 1963, 11 (2), 431–441.

26.

Tokhi

M. O.

Azad

A. K. M.

Real-time finite difference simulation of a single link flexible manipulator system incorporating hub inertia and payload. Proc. Instn Mech. Engrs, Part I: J. Systems and Control Engineering209 (I1), 21–33.

27.

Tokhi

M. O.

Poerwanto

Azad

A. K. M.

Dynamic simulation of flexible manipulator systems incorporating hub inertia, payload and structural damping. Mach. Vibr., 1995, 4, 106–124.

28.

Blum

E. K.

L. K.

Approximation theory and feedforward theory. Neural Networks, 1991, 4, 511–515.

29.

Luo

F.-L.

Unbehauen

Applied Neural Networks for Signal Processing, 1997 (Cambridge University Press, Cambridge).

30.

Omatu

Khalid

Yusof

Neuro-control and Its Applications, 1996 (Springer-Verlag, London).

31.

Battiti

First and second order methods for learning: Between steepest descent and Newton's method. Neural Computation, 1992, 4 (2), 141–166.