The following publications are possibly variants of this publication:
- Hybrid nonlinear model of McKibben pneumatic artificial muscle systems incorporating a pressure-dependent Coulomb friction coefficientKentaro Urabe, Kiminao Kogiso. IEEEcca 2015: 1571-1578 [doi]
- Gray-box identification of McKibben pneumatic artificial muscle using interpolation of load-dependent parametersKiminao Kogiso, Ryo Naito, Kenji Sugimoto. aimech 2013: 1228-1234 [doi]
- Identification procedure for McKibben pneumatic artificial muscle systemsKiminao Kogiso, Kenta Sawano, Takashi Itto, Kenji Sugimoto. iros 2012: 3714-3721 [doi]
- Application of game-theoretic learning to gray-box modeling of McKibben pneumatic artificial muscle systemsKiminao Kogiso, Ryo Naito, Kenji Sugimoto. iros 2013: 5795-5802 [doi]
- Application of Particle Swarm Optimization to Parameter Estimation of a McKibben Pneumatic Artificial Muscle ModelAtsushi Okabe, Kiminao Kogiso. cpsna 2016: 49-54 [doi]
- Fault Analysis of Aging McKibben Pneumatic Artificial Muscle in Terms of its Model ParametersTakahiro Ishikawa, Kiminao Kogiso, Kenichi Hamamoto. ccta 2018: 398-403 [doi]
- Simultaneous Estimation of Contraction Ratio and Parameter of McKibben Pneumatic Artificial Muscle Model Using Log-Normalized Unscented Kalman FilterTakashi Kodama, Atushi Okabe, Kiminao Kogiso. cpsna 2016: 44-48 [doi]
- Parameter extraction for identifying product type of mckibben pneumatic artificial musclesTakahiro Ishikawa, Yu Nishiyama, Kiminao Kogiso. ccta 2017: 1935-1940 [doi]