In order to realize human-friendly, lightweight and flexible actuators, we are developing a drive control system using pneumatic rubber artificial muscles. Conventional actuators using pneumatic rubber artificial muscles are often used to control the internal pressure of the artificial muscles in a relatively high pressure state, but they lack flexibility. On the other hand, in this research, we identify a dynamic model covering the low pressure range (about 2 atm) to high pressure range (about 6 atm) and develop a drive controller (unit) that realizes flexible angle/torque control. We are also working on the detection of age-related deterioration and failure of rubber and other materials due to long-term use. We hope that this will lead to the development of easy-to-use power assist devices and rehabilitation equipment.

Papers Selected:

1. T. Itto and K. Kogiso: Hybrid modeling of McKibben pneumatic artificial muslce systmes, IEEE Joint International Conference on Industrial Technology & IEEE Southeastern Symposium on System Theory, pp. 57-62, 2011. [ieeexplore]

2. K. Kogiso, K. Sawano, T. Itto, and K. Sugimoto: Identification procedure for McKibben pneumatic artificial muscle systems, IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3714-3721, 2012. [ieeexplore]

3. K. Kogiso, R. Naito, and K. Sugimoto: Application of game-theroetic learning to gray-box modeling of McKibben pneumatic artificial muslce systems, IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3714-3721, 2013. [ieeexplore]

4. K. Kogiso, R. Naito, and K. Sugimoto: Gray-box identification of McKibben pneumatic arfiticial muscle using interpolation of load-dependent paramters, IEEE/ASME International Conference on Advanced Intelligent Mechatronics, pp.1228-1234, 2013. [ieeexplore]

5. K. Urabe and K. Kogiso: Application of hybrid model predictive control to McKibben pneumatic artificial muscle system, SICE International Symposium on Control Systems, 514-5, 2015.

6. K. Urabe and K. Kogiso: Hybrid nonlinear model of McKibben pneumatic artificial muscle systems incorporating a pressure-dependent Coulomb friction coefficient, IEEE Multi-conference on Systems and Control, pp. 1571-1578, 2015. [ieeexplore]

7. R. Kadoya and K. Kogiso: Invariant-length PAM model considering virtual weight and PI compensation, SICE International Symposium on Control Systems, 4A2-1, 2016. 

8. T. Kodama, A. Okabe, and K. Kogiso: Simultaneous estimation of contraction ratio and parameter of McKibben pneumatic artificial muscle model using log-normalized unscented Kalman filter, IEEE International Conference on Cyber-Physical Systems, Networks, and Applications, pp. 44-48, 2016. [ieeexplore]

9. A. Okabe and K. Kogiso: Application of particle swarm optimization to parameter estimation of McKibben pneumatic artificial muscle model, IEEE International Conference on Cyber-Physical Systems, Networks, and Applications, pp. 49-54, 2016. [ieeexplore]

10. A. Okabe, T. Ishikawa, K. Kogiso, and Y. Nishiyama: Efficient PSO-based algorithm for parameter estimation of McKibben PAM model, IEEE Conference on Control Technology and Applications, pp. 1414-1419, 2017. [ieeexplore]

11. T. Ishikawa, Y. Nishiyama, and K. Kogiso: Parameter extraction for identifying product type of McKibben pneumatic artificial muscles, IEEE Conference on Control Technology and Applications, pp. 1935-1940, 2017. [ieeexplore]

12. T. Kodama and K. Kogiso: Applications of UKF and EnKF to estimation of contraction ratio of McKibben pneumatic artificial muscles, American Control Conference, pp. 5217-5222, 2017. [ieeexplore]

13. T. Ishikawa, Y. Nishiyama, and K. Kogiso: Characteristic extraction for model parameters of McKibben pneumatic artificial muscles, SICE Journal of Control, Measurement, and System Integration, Vol. 11, No. 4, pp. 357-364, 2018. [doi]

14. K. Yokoyama and K. Kogiso: PID position control of McKibben pneumatic artificial muscle using only pressure feedback, American Control Conference, pp. 3362-3367, 2018. [ieeexplore]

15. T. Ishikawa, K. Kogiso, and K. Hamamoto: Fault analysis of aging McKibben pneumatic artificial muscle in terms of its model parameters, IEEE Conference on Control Technology and Applications, pp. 398-403, 2018. [ieeexplore]

16. A. Okabe and K. Kogiso: Efficient algorithm for constructing a load-dependent McKibben pneumatic artificial muscle model, IEEE/ASME International Conference on Advanced Intelligent Mechatronics, pp. 962-967, 2018. [ieeexplore]

17. T. Shin, T. Ibayashi, and K. Kogiso, Detailed dynamic model of antagonistic PAM system and its experimental validation: Sensorless angle and torque control with UKF, IEEE/ASME Transactions on Mechatronics, Volume ??, Issue ??, pp. ???-???, 2021. [ieeexplore]

Acknowledgement

This work was supported by the 25th Mazda Research Grant 09KK-296 (2009), JSPS Grants-in-Aid for Young Scientists 25709014 (2013-2015), and Grant-in-Aid for Scientific Research C 18K04012 (2018-2020).