Modeling of McKibben Pneumatic artificial muscles using optimized Echo State Networks

In conventional techniques for modeling Pneumatic artificial muscle, there are difficulties such as poor knowledge of the process, inaccurate process or complexity of the resulting mathematical model. Trying to solve these problems, this study investigates the method of establishing the model using a novel network—Echo State Network (ESN). We introduce the mechanism of this net and apply it to our modeling work. The relevant parameters of the net were optimized using Particle Swarm Optimization (PSO). Then we get the simulation results which reveal that it can get quite satisfactory results.

[1]  A. PearlmutterB..  Gradient calculations for dynamic recurrent neural networks , 1995 .

[2]  Herbert Jaeger,et al.  The''echo state''approach to analysing and training recurrent neural networks , 2001 .

[3]  H. F. Schulte.  The characteristics of the McKibben artificial muscle , 1961 .

[4]  Nikolaos G. Tsagarakis,et al.  Improved modelling and assessment of pneumatic muscle actuators , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[5]  Barak A. Pearlmutter.  Gradient calculations for dynamic recurrent neural networks: a survey , 1995, IEEE Trans. Neural Networks.

[6]  Amir F. Atiya,et al.  New results on recurrent network training: unifying the algorithms and accelerating convergence , 2000, IEEE Trans. Neural Networks Learn. Syst..

[7]  Blake Hannaford,et al.  Accounting for elastic energy storage in McKibben artificial muscle actuators , 2000 .

[8]  Henry Markram,et al.  Real-Time Computing Without Stable States: A New Framework for Neural Computation Based on Perturbations , 2002, Neural Computation.

[9]  Blake Hannaford,et al.  Dual stable point model of muscle activation and deactivation , 2004, Biological Cybernetics.

[10]  Ching-Ping Chou,et al.  Static and dynamic characteristics of McKibben pneumatic artificial muscles , 1994, Proceedings of the 1994 IEEE International Conference on Robotics and Automation.

[11]  Günther Palm,et al.  Real-Time Emotion Recognition from Speech Using Echo State Networks , 2008, ANNPR.