Abstract
In this paper trajectory tracking control of a human arm moving in sagittal plane is investigated. The arm is described by a musculoskeletal model with two degrees of freedom and six muscles, and the control signal is applied directly in muscle space. To design the intelligent controller, an evolutionary diagonal recurrent neural network (EDRNN) is integrated with proper performance indices, which a genetic algorithm (GA) and evolutionary program (EP) strategy are effectively combined with the diagonal neural network (DRNN). The hybrid GA with EP strategy is applied to optimize the DRNN structure and a dynamic back-propagation algorithm (DBP) is used for training the network weights. The effectiveness of the control scheme is demonstrated through a simulated case study.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Schouten, A.C., de Vlugt, E., van der Helm, F.C.T., Brouwn, G.G.: Optimal Posture Control of a Musculo-Skeletal Arm Model. Biol. Cybern. 84, 143–152 (2001)
Brown, I.E., Cheng, E.J., Leob, G.: Measured and Modeled Properties of Mammalian Skeletal Muscle. II. the Effects of Stimulus Frequency on Force-Length and Force-Velocity Relationships. J. of Muscle Research and Cell Motility 20, 627–643 (1999)
Chaiyaratana, N., Zalzala, A.M.S.: Hybridisations of Neural Networks and Genetic Algorithms for Time-Optimal Control. In: Angeline, P.J. (ed.) Proceedings of the 1999 Congress on Evolutionary Computation, vol. 1, pp. 389–396. IEEE Press, New Jersey (1999)
Ku, C.-C., Lee, K.Y.: Diagonal Recurrent Neural Networks for Dynamic System Control. IEEE Trans. on Neural Networks 6, 144–156 (1995)
Franklin, D.W., Milner, T.E.: Adaptive Control of Stiffness to Stabilize Hand Position with Large Loads. Exp. Brain Res. 152, 211–220 (2003)
Cheng, E.J., Brown, I.E., Loeb, G.E.: Virtual Muscle: a Computational Approach to Understanding the Effects of Muscle Properties on Motor Control. Journal of Neuroscience Methods 101, 117–130 (2000)
Hussenin, S.B., Jamaluddin, H., Mailah, M., Zalzala, A.M.S.: A Hybrid Intelligent Active Force Controller for Robot Arms Using Evolutionary Neural Networks. In: Angeline, P.J. (ed.) Proceedings of the 2000 Congress on Evolutionary Computation, vol. 1, pp. 117–130. IEEE Press, New Jersey (2000)
Nakazono, K., Katagiri, M., Kinjo, H., Yamamoto, T.: Force and Position Control of Robot Manipulator Using Neurocontroller with GA based Training. In: Nakauchi, Y. (ed.) Proceedings of 2003 IEEE International Symposium on Computational Intelligence in Robotics and Automation, vol. 3, pp. 1354–1357. IEEE Press, New Jersey (2003)
Katayama, M., Inoue, S., Kawato, M.: A Strategy of Motor Learning Using Adjustable Parameters for Arm Movement. In: Chang, H.K., Zhang, Y.T. (eds.) Proceeding of 20th IEEE EMBS. IEEE Engineering in Medicine and Biology Society, vol. 20, pp. 2370–2373. IEEE Press, New Jersey (1998)
Lan, N.: Analysis of an Optimal Control Model of Multi-Joint Arm Movements. Biol. Cybern. 207–117 (1997)
Sanner, R.M., Kosha, M.: A Mathematical Model of the Adaptive Control of Human Arm Motions. Biol. Cybern. 80, 369–382 (1999)
liu, S., Wang, Y., Huang, J.: Neural network based posture control of a human arm model in the sagittal plane. In: Wang, J., Yi, Z., Zurada, J.M., Lu, B.-L., Yin, H. (eds.) ISNN 2006. LNCS, vol. 3973, pp. 792–798. Springer, Heidelberg (2006)
Stroeve, S.H.: Impedance Characteristics of a Neuromusculoskeletal Model of the Human Arm. I. Posture Control. Biol. Cybern. 81, 475–494 (1999)
Li, W., TOdorov, E.: Iterative Quadratic Regulator Design for Nonliear Biological Movement system. In: Filipe, J. (ed.) Proceedings of the 1st International Conference on Informatica in Control, Automation and Robotics, vol. 1, pp. 222–229. Kluwer Academic Publisher, Netherlands (2004)
Li, W., Todorov, E., Pan, X.: Hierarchical Optimal Control of Redundant Biomechanical Systems. In: Hudson, D., Liang, Z.P. (eds.) Proceeding of 26th IEEE EMBS. IEEE Engineering in Medicine and Biology Society, vol. 26, pp. 4618–4621. IEEE Press, New Jersey (2004)
Todorov, E., Li, W.: A Generalized Iterative LQG Method for Locally-optiml Feedback Control of Constrained Nonlinear Stochastic Systems. In: Suhada, J., Balakrishnan, S.N. (eds.) Proceeding of 2005 ACC. American Control Conference, vol. 1, pp. 300–306. IEEE Press, New Jersey (2005)
Papaxanthis, C., Pozzo, T., McIntyre, J.: Arm End-point Trajectories Under Normal and Micro-gravity Environments. Acta Astronautica 43, 153–161 (1998)
Papaxanthis, C., Pozzo, T., Schieppati, M.: Trajectories of arm pointing movements on the sagittal plane vary with both direction and speed. Exp. Brain Res. 148, 498–503 (2003)
Kambara, H., Kim, K., Shin, D., et al.: Motor Control-Learning Model for Reaching Movements. In: 2006 International Joint Conference on Neural Networks, pp. 555–562 (2006)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Liu, S., Wang, Y., Zhu, Q. (2007). An Evolutionary Neural Network Based Tracking Control of a Human Arm in the Sagittal Plane. In: Kang, L., Liu, Y., Zeng, S. (eds) Advances in Computation and Intelligence. ISICA 2007. Lecture Notes in Computer Science, vol 4683. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74581-5_35
Download citation
DOI: https://doi.org/10.1007/978-3-540-74581-5_35
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74580-8
Online ISBN: 978-3-540-74581-5
eBook Packages: Computer ScienceComputer Science (R0)