Muscle Emulation with DC Motor and Neural Networks for Biped Robots

This paper shows how to use a DC motor and its PID controller, to behave analogously to a muscle. A model of the muscle that has been learned by a NNARX (Neural Network Auto Regressive eXogenous) structure is used. The PID parameters are tuned by an MLP Network with a special indirect online learning algorithm. The calculation of the learning algorithm is performed based on a mathematical equation of the DC motor or with a Neural Network identification of the motor. For each of the two algorithms, the output of the muscle model is used as a reference for the DC motor control loop. The results show that we succeeded in forcing the physical system to behave in the same way as the muscle model with acceptable margin of error. An implementation in the knees of a simulated biped robot is realized. Simulation compares articular trajectories with and without the muscle emulator and shows that with muscle emulator, articular trajectories become closer to the human being ones and that total power consumption is reduced.

[1]  C. S. Lee,et al.  Fuzzy logic versus a PID controller for position control of a muscle-like actuated arm , 2008 .

[2]  Gerasimos G. Rigatos.  Adaptive Fuzzy Control with Output Feedback for H∞ Tracking of SISO Nonlinear Systems , 2008, Int. J. Neural Syst..

[3]  George K. I. Mann,et al.  Design and Tuning of Standard Additive Model Based Fuzzy PID Controllers for Multivariable Process Systems , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[4]  J C Jarvis,et al.  A radial basis function model of muscle stimulated with irregular inter-pulse intervals. , 1995, Medical engineering & physics.

[5]  Alexandru Stancu,et al.  Nonlinear System Identification Based on Internal Recurrent Neural Networks , 2009, Int. J. Neural Syst..

[6]  Zidong Wang,et al.  Global Synchronization in an Array of Discrete-Time Neural Networks with Nonlinear Coupling and Time-Varying Delays , 2009, Int. J. Neural Syst..

[7]  Ching-Hung Lee,et al.  Calculation of PID controller parameters by using a fuzzy neural network. , 2003, ISA transactions.

[8]  Masami Saito,et al.  A new motor model representing the stretch-induced force enhancement and shortening-induced force depression in skeletal muscle. , 2005, Journal of biomechanics.

[9]  Shinn-Ying Ho,et al.  Optimizing fuzzy neural networks for tuning PID controllers using an orthogonal simulated annealing algorithm OSA , 2006, IEEE Transactions on Fuzzy Systems.

[10]  René V. Mayorga,et al.  A Radial Basis Function Network Approach for the Computation of Inverse Continuous Time Variant Functions , 2007, Int. J. Neural Syst..

[11]  Tad McGeer,et al.  Passive Dynamic Walking , 1990, Int. J. Robotics Res..

[12]  Hans-Georg Zimmermann,et al.  Recurrent Neural Networks are Universal approximators , 2007, Int. J. Neural Syst..

[13]  George K. I. Mann,et al.  Two-level tuning of fuzzy PID controllers , 2001, IEEE Trans. Syst. Man Cybern. Part B.

[14]  Narasimhan Sundararajan,et al.  Neural Adaptive Control for Vibration Suppression in Composite Fin-Tip of Aircraft , 2008, Int. J. Neural Syst..