IJAT Vol.16 No.4 pp. 436-447
doi: 10.20965/ijat.2022.p0436


Model Predictive Displacement Control Tuning for Tap-Water-Driven Artificial Muscle by Inverse Optimization with Adaptive Model Matching and its Contribution Analyses

Satoshi Tsuruhara, Ryo Inada, and Kazuhisa Ito

Shibaura Institute of Technology
307 Fukasaku, Minuma, Saitama-shi, Saitama 337-8570, Japan

Corresponding author

December 6, 2021
January 17, 2022
July 5, 2022
adaptive model matching, inverse optimization, model predictive control, artificial muscle, water-hydraulic

The tap-water-driven McKibben artificial muscle has many advantages and is expected to be applied in mechanical systems that require a high degree of cleanliness. However, the muscle has strong asymmetric hysteresis characteristics that depend on the load, and these problems prevent its widespread use. In this study, a novel control method, model predictive control with a servomechanism based on inverse optimization with adaptive model matching, was developed. This control method was applied to the muscle by using a high-precision mathematical model employing an asymmetric Bouc-Wen model. The experimental results show that the proposed approach achieved a high tracking performance for a given reference frequency, with a mean absolute error of 0.13 mm in the steady-state response and with easier controller tuning. Furthermore, the contributions of the controller elements of the proposed method were evaluated. The results show that the contribution of the adaptive system was higher than that of the servo system. Furthermore, the effectiveness of adaptive model matching was verified.

Cite this article as:
S. Tsuruhara, R. Inada, and K. Ito, “Model Predictive Displacement Control Tuning for Tap-Water-Driven Artificial Muscle by Inverse Optimization with Adaptive Model Matching and its Contribution Analyses,” Int. J. Automation Technol., Vol.16, No.4, pp. 436-447, 2022.
Data files:
  1. [1] G. Andrikopoulos, G. Nikolakopoulos, and S. Manesis, “A Survey on Applications of Pneumatic Artificial Muscles,” Proc. of the 19th Mediterranean Conf. on Control and Automation, pp. 1439-1446, 2011.
  2. [2] S. Miyakawa, “Aqua Drive System: A Technology Using Tap Water and Its Applications,” Proc. of the 8th JFPS Int. Symp. on Fluid Power, pp. 26-37, 2011.
  3. [3] W. Kobayashi, S. Dohta, T. Akagi, and K. Ito, “Analysis and modeling of tap-water/pneumatic drive McKibben type artificial muscles,” Int. J. of Mechanical Engineering and Robotics Research, Vol.6, No.6, pp. 463-466, 2017.
  4. [4] M. Moučka, “Model Reference Adaptive Control of Pneumatics Artificial Muscle,” Proc. of the 22nd Int. Conf. on Process Control (PC19), pp. 156-160, 2019.
  5. [5] A. P. Arrese, A. Mendizabal, J. Arenas, R. Prestamero, and J. Landaluze, “Modeling in Modelica and Position Control of a 1-DoF Set-up Powered by Pneumatic Muscles,” Mechatronics, Vol.20, No.5, pp. 535-552, 2010.
  6. [6] Y. Kawahara, T. Kosaki, and S. Li, “LS-SVM Based Modeling and Model Predictive Control for a Water-Hydraulic Artificial Muscle Actuator,” SICE J. of Control, Measurement, and System Integration, Vol.13, No.3, pp. 114-121, 2020.
  7. [7] J. E. Slightam, M. L. Nagurka, and E. J. Barth, “Sliding Mode Impedance Control of a Hydraulic Artificial Muscle,” Proc. of the ASME Int. Dynamic Systems and Control Conf., Vol.1, V001T13A003, 2018.
  8. [8] J. E. Slightam and M. L. Nagurka, “Theoretical Control-Centric Modeling for Precision Model-Based Sliding Mode Control of a Hydraulic Artificial Muscle Actuator,” J. of Dynamic Systems, Measurement and Control, Vol.143, No.5, 051010, 2021.
  9. [9] D. X. Ba, K. K. Ahn, and N. T. Tai, “Adaptive Integral-Type Neural Sliding Mode Control for Pneumatic Muscle Actuator,” Int. J. Automation Technol., Vol.8, No.6, pp. 888-895, 2014.
  10. [10] T. Kosaki, Y. Kawahara, and S. Li, “A Sliding Mode Controller Using an LS-SVM Model for a Water-Hydraulic Artificial Rubber Muscle,” J. Robot. Mechatron., Vol.32, No.5, pp. 903-910, 2020.
  11. [11] C. J. Lin, R. Ruey, S, Kai, and C. T. Chen, “Hysteresis Modelling and Tracking Control for a Dual Pneumatic Artificial Muscle System using Prandtl Ishlinskii Model,” Mechatronics, Vol.28, pp. 35-45, 2015.
  12. [12] T. Kosaki and M. Sano, “Control of a Parallel Manipulator Driven by Pneumatic Muscle Actuators Based on a Hysteresis Model,” J. of Environment and Engineering, Vol.6, No.2, pp. 316-327, 2011.
  13. [13] H. Aschemann and D. Schinedele, “Comparison of Model-Based Approaches to the Compensation of Hysteresis in the Force Characteristic of Pneumatic Muscles,” IEEE Trans. on Industrial Electronics, Vol.61, No.7, pp. 3620-3629, 2014.
  14. [14] G. Andrikopoulos, G. Nikolakopoulos, and S. Manesis, “Pneumatic Artificial Muscles: A Switching Model Predictive Control Approach,” Control Engineering Practice, Vol.21, No.12, pp. 1653-1664, 2013.
  15. [15] J. Wu, J. Huang, Y. Wang, and K. Xing, “Nonlinear Disturbance Observer-based Dynamic Surface Control for Trajectory Tracking of Pneumatic Muscle System,” IEEE Trans. on Control Systems Technology, Vol.22, No.2, pp. 440-455, 2014.
  16. [16] K. Xing, J. Huang, Y. Wang, J. Wu, Q. Xu, and J. He, “Tracking Control of Pneumatic Artificial Muscle Actuators based on Sliding Mode and Non-linear Disturbance Observer,” IET Control Theory and Applications., Vol.4, Issue 10, pp. 2058-2070, 2010.
  17. [17] W. Kobayashi, K. Ito, and S. Yamamoto, “Displacement Control of Water Hydraulic McKibben Muscles with Load Compensation,” JFPS Int. J. of Fluid Power System, Vol.8, Issue 2, pp. 107-112, 2014.
  18. [18] G. Wang, G. Chen, and F. Bai, “Modeling and Identification of Asymmetric Bouc-Wen Hysteresis for Piezoelectric Actuator via A Novel Differential Evolution Algorithm,” Sensors and Actuators A: Physical, Vol.235, pp. 105-118, 2015.
  19. [19] R. Inada, K. Ito, and S. Ikeo, “Adaptive Model Predictive Tracking Control of Tap-Water Driven Muscle Using Hysteresis Compensation with Bouc-Wen Model,” Proc. of 16th Scandinavian Int. Conf. on Fluid Power (SICFP2019), B3.3, 2019.
  20. [20] R. Inada, K. Ito, and S. Ikeo, “Modeling and Hysteresis Compensation Using Asymmetric Bouc-Wen Model for Tap-water Driven Muscle and Its Application to Adaptive Model Predictive Tracking Control,” Proc. of 15th Int. Conf. on Fluid Control, Measurements and Visualization (FLUCOME2019), 144, 2019.
  21. [21] K. Ito and R. Inada, “Model Predictive Displacement Control Tuning of Tap Water Driven Muscle with Adaptive Model Matching –Numerical Study –,” Proc. of the 2020 Bath/ASME Symp. on Fluid Power and Motion Control (FPMC2020), FPMC2020-2711, 2020.
  22. [22] S. Tsuruhara, R. Inada, and K. Ito, “Model predictive displacement control tuning for tap-water-driven muscle by inverse optimization with adaptive model matching,” Proc. of the 10th Int. Conf. on Fluid Power Transmission and Control-ICFP2021, pp. 286-292, 2021.
  23. [23] Z. Wei, B. L. Xiang, and R. X. Ting, “Online Parameter Identification of the Asymmetrical Bouc-Wen Model for Piezoelectric Actuators,” Precision Engineering, Vol.38, No.4, pp. 921-927, 2014.
  24. [24] D. S. Cairano and A. Bemporad, “Model Predictive Control Tuning by Controller Matching,” IEEE Trans. on Automatic Control, Vol.55, No.1, pp. 185-190, 2010.
  25. [25] R. Mantri, A. A. Stoorvogel, and A. Saberi, “Output Regulation for Linear Discrete-time Systems Subject to Input Saturation,” Int. J. of Robust and Nonlinear Control, Vol.7, No.11, pp. 1003-1021, 1996.
  26. [26] N. Wada, “Model Predictive Tracking Control for Constrained Linear Systems Using Integrator Resets,” IEEE Trans. on Automatic Control, Vol.60, No.11, pp. 3113-3118, 2015.
  27. [27] J. Lofberg, “YALMIP: A Toolbox for Modeling and Optimization in MATLAB,” IEEE Int. Conf. on Robotics and Automation, New Orleans, pp. 284-289, 2004.
  28. [28] J. Mattingley and S. Boyd, “CVXGEN: A Code Generator for Embedded Convex Optimization,” Optimization and Engineering, Vol.13. pp. 1-27, 2011.

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Last updated on Aug. 05, 2022