In this paper, a force-tracking impedance controller with an on-line neural-network compensator is shown to be able to track a reference force in the presence of unknown environmental dynamics. The controller can be partitioned into three parts. The computed torque method is used to linearize and decouple the dynamics of a manipulator. An impedance controller is then added to regulate the mechanical impedance between the manipulator and its environment. In order to track a reference force signal, an on-line neural network is used to compensate the effect of unknown parameters of the manipulator and environment.
This is a preview of subscription content, log in to check access.
Buy single article
Instant access to the full article PDF.
Price includes VAT for USA
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
This is the net price. Taxes to be calculated in checkout.
HoganN.: Impedance control: an approach to manipulation, Part 1, Theory, ASME J. Dynamic system, Measurement, and Control 107 (1985), 1–7.
HoganN.: Impedance control: an approach to manipulation, Part 2, Implementation, ASME J. Dynamic System, Measurement, and Control 107 (1985), 8–16.
HoganN.: Impedance control: an approach to manipulation, Part 3, Application, ASME J. Dynamic System, Measurement, and Control 107 (1985), 17–24.
KazerooniH., SheridanT. B., and HouptP. K.: Robust compliant motion for manipulators, Part 1, The fundamental concepts of compliant motion, IEEE J. Robotics and Automation RA-2 (1986), 83–92.
KazerooniH., SheridanT. B., and HouptP.K.: Robust compliant motion for manipulators, Part 2, Design method, IEEE J. Robotics and Automation RA- 2 (1986) 93–105.
KellyR., CarelliR., AmesteguiM., and OrtegaR.: Adaptive impedance control of robot manipulators. Int. J. Robotics and Automation 4(3) (1989), 134–141.
Koo, C. Y. and Wang, S. P. T.: Nonlinear robust hybrid control of robotic manipulators, ASME J. Dynamic System, Measurement, and Control 112 48–54.
MasonM. T.: Compliance and force control for computer controlled manipulators, IEEE Trans. System, Man, and Cybernetics SMC-11 (1981), 418–432.
Raibert, J. K.: Active stiffness control of a manipulator in cartesian coordinates, in Proc. IEEE Conf. Decision and Control, 1980, pp. 95–100.
Lin, S. T. and Yae, K. H.: Identification of unknown payload and environmental parameters for robot compliant motion, in Proc. Amer. Control Conf., 1992, pp. 2952–2956.
NarendraK. S. and ParhasarathyK.: Identification and control of dynamic systems using neural network, IEEE Trans. Neural Network 1 (1990), 4–27.
BartoA. G., SuttonR. S., and AndersonC. W.: Neuron-like adaptive elements that can solve difficult learning control problems, IEEE Trans. System, Man, and Cybernetics SMC-13 (1983), 834–846.
Franklin, J. A.: Reinforcement of robot motor skills through reinforcement learning, in Proc. IEEE Conf. Decision and Control, 1988, pp. 1096–1101.
Psaltis, D., Sideris, A., and Yamamura, A. A.: A multi-layered neural network controller, IEEE Control System Magazine (April 1988), 17–20.
Kawato, M.: Computational schemes and neural network models for formatoon and control of multijoint arm trajectory Neural Networks for Control, MIT Press, 1990.
Okuma, S., Ishiguro, A., Furuhashi, T., and Uchikawa, Y.: A neural network compensator for uncertainties of robot manipulators, Proc. IEEE Conf. Decision and Control, 1990.
FukudaT., KuriharaT., ShibataT., TokitaM., and MitsuokaT.: Application of neural network-based servo controller to position, force, and stabbing control by robotic manipulators, JSME Int. J. Series III 34 (2) (1991), 303–309.
Fukuda, T., Shibata, T., Tokita, M., and Mitsuoka, T.: Neuromophic control: application and learning, IEEE Trans. Industrial Electronics 39(6) (1992).
ZuradaJ. M.: Introduction to Artificial Neural Systems, West Info Access, Singapore, 1992.
About this article
Cite this article
Lin, S., Lee, J. Impedance control with on-line neural-network compensator for robot contact tasks. J Intell Robot Syst 15, 389–399 (1996). https://doi.org/10.1007/BF00437603
- Impedance control
- force control
- neural network
- robot contact motion