Efficient PSO-based algorithm for parameter estimation of McKibben PAM model
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[1] George Nikolakopoulos,et al. Piecewise Affine Modeling and Constrained Optimal Control for a Pneumatic Artificial Muscle , 2014, IEEE Transactions on Industrial Electronics.
[2] Y. Rahmat-Samii,et al. Particle swarm optimization in electromagnetics , 2004, IEEE Transactions on Antennas and Propagation.
[3] Robert M. Sanner,et al. Nonlinear Control of Robotic Manipulators Driven by Pneumatic Artificial Muscles , 2016, IEEE/ASME Transactions on Mechatronics.
[4] Kiminao Kogiso,et al. Hybrid modeling of McKibben pneumatic artificial muscle systems , 2011, 2011 IEEE International Conference on Industrial Technology.
[5] K. Tadano,et al. Achieving Haptic Perception in Forceps’ Manipulator Using Pneumatic Artificial Muscle , 2013, IEEE/ASME Transactions on Mechatronics.
[6] Kenji Sugimoto,et al. Application of game-theoretic learning to gray-box modeling of McKibben pneumatic artificial muscle systems , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.
[7] Jun Morimoto,et al. Torque and variable stiffness control for antagonistically driven pneumatic muscle actuators via a stable force feedback controller , 2015, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
[8] Yongji Wang,et al. Modeling of McKibben Pneumatic artificial muscles using optimized Echo State Networks , 2010, 2010 8th World Congress on Intelligent Control and Automation.
[9] Kenji Sugimoto,et al. Identification procedure for McKibben pneumatic artificial muscle systems , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.
[10] Corinna Cortes,et al. Support-Vector Networks , 1995, Machine Learning.
[11] Bernhard E. Boser,et al. A training algorithm for optimal margin classifiers , 1992, COLT '92.
[12] Gen Endo,et al. A walking assistive device with intention detection using back-driven pneumatic artificial muscles , 2015, 2015 IEEE International Conference on Rehabilitation Robotics (ICORR).
[13] Hiromi Mochiyama,et al. Admittance and Impedance Representations of Friction Based on Implicit Euler Integration , 2006, IEEE Transactions on Robotics.
[14] Kiminao Kogiso,et al. Applications of UKF and EnKF to estimation of contraction ratio of McKibben pneumatic artificial muscles , 2017, 2017 American Control Conference (ACC).
[15] Kiminao Kogiso,et al. Application of Particle Swarm Optimization to Parameter Estimation of a McKibben Pneumatic Artificial Muscle Model , 2016, 2016 IEEE 4th International Conference on Cyber-Physical Systems, Networks, and Applications (CPSNA).
[16] T. Tjahjowidodo,et al. A New Approach to Modeling Hysteresis in a Pneumatic Artificial Muscle Using The Maxwell-Slip Model , 2011, IEEE/ASME Transactions on Mechatronics.
[17] Kiminao Kogiso,et al. Hybrid nonlinear model of McKibben pneumatic artificial muscle systems incorporating a pressure-dependent Coulomb friction coefficient , 2015, 2015 IEEE Conference on Control Applications (CCA).
[18] Kyoung Kwan Ahn,et al. A new approach for modelling and identification of the pneumatic artificial muscle manipulator based on recurrent neural networks , 2007 .
[19] Kiminao Kogiso,et al. Parameter extraction for identifying product type of mckibben pneumatic artificial muscles , 2017, 2017 IEEE Conference on Control Technology and Applications (CCTA).
[20] James Kennedy,et al. Particle swarm optimization , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.