Research Paper:
Hierarchical-Type Model Predictive Control and Experimental Evaluation for a Water-Hydraulic Artificial Muscle with Direct Data-Driven Adaptive Model Matching
Satoshi Tsuruhara*,
and Kazuhisa Ito**

*Graduate School of Engineering and Science, Shibaura Institute of Technology
307 Fukasaku, Saitama 337-8570, Japan
Corresponding author
**Department of Machinery and Control Systems, Shibaura Institute of Technology
Minuma, Japan
High-precision displacement control for water-hydraulic artificial muscles is challenging because of their strong hysteresis characteristics, which are difficult to be modeled precisely. Recently, data-driven control methods have attracted considerable attention because they do not explicitly use mathematical models, making the design much easier. In our previous work, we proposed a fictitious reference iterative tuning (FRIT)-based model predictive control (FMPC), which combines data-driven and model-based methods for the muscle, and showed its effectiveness because it can also consider input constraints. However, the problem in which control performance strongly depends on prior input-output data remains unsolved. Adaptive FRIT (A-FRIT) based on directional forgetting has also been proposed; however, achieving the desired transient performance is difficult because it cannot consider the input constraints, and there are no design parameters that directly determine the control performance. This paper proposes a novel data-driven adaptive model matching-based controller that combines MPC with the A-FRIT. The experimental results show that the proposed method can significantly improve the control performance and achieve high robustness against inappropriate initial experimental data while considering the input constraints in the design phase.
- [1] 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. https://doi.org/10.20965/ijat.2022.p0436
- [2] C. Zhang et al., “Fluid-Driven Artificial Muscles: Bio-Design, Manufacturing, Sensing, Control, and Applications,” Bio-Design and Manufacturing, Vol.4, pp. 123-145, 2021. https://doi.org/10.1007/s42242-020-00099-z
- [3] Z.-S. Hou and Z. Wang, “From model-based control to data-driven control: Survey, classification and perspective,” Inf. Sci., Vol.235, pp. 3-35, 2013. https://doi.org/10.1016/j.ins.2012.07.014
- [4] K. Prag, M. Woolway, and T. Celik, “Toward Data-Driven Optimal Control: A Systematic Review of the Landscape,” IEEE Access, Vol.10, pp. 32190-32212, 2022. https://doi.org/10.1109/ACCESS.2022.3160709
- [5] A. Sanfelici Bazanella, L. Campestrini, and D. Eckhard, “The Data-Driven Approach to Classical Control Theory,” Annu. Rev. Control, Vol.56, Article No.100906, 2023. https://doi.org/10.1016/j.arcontrol.2023.100906
- [6] S. Soma, O. Kaneko, and T. Fujii, “A New Method of Controller Parameter Tuning Based on Input-Output Data – Fictitious Reference Iterative Tuning (FRIT) –,” IFAC Proc. Volumes, Vol.37, No.12, pp. 789-794, 2004. https://doi.org/10.1016/S1474-6670(17)31566-5
- [7] M. C. Campi, A. Lecchini, and S. M. Savaresi, “Virtual Reference Feedback Tuning: A Direct Method for the Design of Feedback Controllers,” Automatica, Vol.38, No.8, pp. 1337-1346, 2002. https://doi.org/10.1016/S0005-1098(02)00032-8
- [8] Z. Hou and S. Xiong, “On Model-Free Adaptive Control and Its Stability Analysis,” IEEE Trans. on Automatic Control, Vol.64, No.11, pp. 4555-4569, 2019. https://doi.org/10.1109/TAC.2019.2894586
- [9] M. Fliess and C. Join, “Model-Free Control,” Int. J. of Control, Vol.86, No.12, pp. 2228-2252, 2013. https://doi.org/10.1080/00207179.2013.810345
- [10] J. Sarangapani, “Neural Network Control of Nonlinear Discrete-Time Systems,” CRC Press, 2018. https://doi.org/10.1201/9781420015454
- [11] A. M. Annaswamy, “Adaptive Control and Intersections with Reinforcement Learning,” Annual Review of Control, Robotics, and Autonomous Systems, Vol.6, pp. 65-93, 2023. https://doi.org/10.1146/annurev-control-062922-090153
- [12] S. Takada et al., “Data-Driven Tuning of Nonlinear Internal Model Controllers for Pneumatic Artificial Muscles,” 2014 4th Australian Control Conf. (AUCC), pp. 13-18, 2014. https://doi.org/10.1109/AUCC.2014.7358707
- [13] S. Tsuruhara and K. Ito, “Data-Driven Model-Free Adaptive Displacement Control for Tap-Water-Driven Artificial Muscle and Parameter Design Using Virtual Reference Feedback Tuning,” J. Robot. Mechatron., Vol.34, No.3, pp. 664-676, 2022. https://doi.org/10.20965/jrm.2022.p0664
- [14] Q. Ai et al., “High-Order Model-Free Adaptive Iterative Learning Control of Pneumatic Artificial Muscle With Enhanced Convergence,” IEEE Trans. on Industrial Electronics, Vol.67, No.11, pp. 9548-9559, 2020. https://doi.org/10.1109/TIE.2019.2952810
- [15] 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. https://doi.org/10.20965/ijat.2014.p0888
- [16] Y. Cui, T. Matsubara, and K. Sugimoto, “Pneumatic Artificial Muscle-Driven Robot Control Using Local Update Reinforcement Learning,” Advanced Robotics, Vol.31, No.8, pp. 397-412, 2017. https://doi.org/10.1080/01691864.2016.1274680
- [17] S. Ishihara, R. Narikawa, and T. Ohtsuka, “Automated Loading Operation for Mass-Production Hydraulic Excavators by Nonlinear Model Predictive Control,” IFAC-PapersOnLine, Vol.56, No.2, pp. 5793-5798, 2023. https://doi.org/10.1016/j.ifacol.2023.10.554
- [18] M. P. Polverini, S. Formentin, L. Merzagora, and P. Rocco, “Mixed Data-Driven and Model-Based Robot Implicit Force Control: A Hierarchical Approach,” IEEE Trans. Control Syst. Technol., Vol.28, No.4, pp. 1258-1271, 2020. https://doi.org/10.1109/TCST.2019.2908899
- [19] D. Piga, S. Formentin, and A. Bemporad, “Direct Data-Driven Control of Constrained Systems,” IEEE Trans. Control Syst. Technol., Vol.26, No.4, pp. 1422-1429, 2018. https://doi.org/10.1109/TCST.2017.2702118
- [20] S. Wakitani, M. Sako, T. Yamamoto, Y. Ohno, H. Kishi, N. Yumoto, and K. Koiwai, “Design of a Hierarchical-Type Control System Based on Smart MBD Approach and its Application to Hydraulic Excavator,” J. Robot. Mechatron., Vol.36, No.4, pp. 909-917, 2024. http://doi.org/10.20965/jrm.2024.p0909
- [21] M. Kano et al., “Extended Fictitious Reference Iterative Tuning and Its Application to Chemical Processes,” 2011 Int. Symp. on Advanced Control of Industrial Processes (ADCONIP), pp. 379-384, 2011.
- [22] M. Sekine, S. Tsuruhara, and K. Ito, “MPC for Artificial Muscles Using FRIT Based Optimized Pseudo Linearization Model,” IFAC-PapersOnLine, Vol.56, No.2, pp. 7264-7269, 2023. https://doi.org/10.1016/j.ifacol.2023.10.336
- [23] M. Sekine, S. Tsuruhara, and K. Ito, “Optimized Design of Pseudo-Linearization-Based Model Predictive Controller: Direct Data-Driven Approach,” IET Control Theory & Applications, Vol.19, No.1, Article No.e12786, 2024. https://doi.org/10.1049/cth2.12786
- [24] Y. Wakasa, K. Tanaka, and Y. Nishimura, “Online Controller Tuning via FRIT and Recursive Least-Squares,” IFAC Proc. Volumes, Vol.45, No.3, pp. 76-80, 2012. https://doi.org/10.3182/20120328-3-IT-3014.00013
- [25] S. Tsuruhara and K. Ito, “Adaptive FRIT-Based Recursive Robust Controller Design Using Forgetting Factors,” 2024 32nd Mediterranean Conf. on Control and Automation (MED), pp. 119-124, 2024. https://doi.org/10.1109/MED61351.2024.10566181
- [26] S. Tsuruhara and K. Ito, “Direct Data-Driven Adaptive Model Matching Based Model Predictive Displacement Control for a Water-Hydraulic Artificial Muscle and Robustness Evaluation to Characteristics Change,” The 12th JFPS Int. Symp. on Fluid Power in Hiroshima 2024, 1D1-04, 2024.
- [27] R. Inada, S. Tsuruhara, K. Ito, and S. Ikeo, “Precise Displacement Control of Tap-Water-Driven Muscle Using Adaptive Model Predictive Control with Hysteresis Compensation,” JFPS Int. J. of Fluid Power System, Vol.15, No.3, pp. 78-85, 2022. https://doi.org/10.5739/jfpsij.15.78
- [28] G. C. Goodwin and K. S. Sin, “Adaptive Filtering Prediction and Control,” Courier Corporation, 2014.
- [29] I. D. Landau et al., “Adaptive Control: Algorithms, Analysis, and Applications,” Springer Science & Business Media, 2011. https://doi.org/10.1007/978-0-85729-664-1
- [30] L. Cao and H. Schwartz, “A Directional Forgetting Algorithm Based on the Decomposition of the Information Matrix,” Automatica, Vol.36, No.11, pp. 1725-1731, 2000. https://doi.org/10.1016/S0005-1098(00)00093-5
- [31] B. Lai and D. S. Bernstein, “Generalized Forgetting Recursive Least Squares: Stability and Robustness Guarantees,” IEEE Trans. on Automatic Control, Vol.69, No.11, pp. 7646-7661, 2024. https://doi.org/10.1109/TAC.2024.3394351
- [32] G. Tao, “Adaptive Control Design and Analysis,” John Wiley & Sons, Vol.37, 2003. https://doi.org/10.1002/0471459100
- [33] S. Masuda, M. Kano, and Y. Yasuda, “A Fictitious Reference Iterative Tuning Method with Simultaneous Delay Parameter Tuning of the Reference Model,” Proc. of the Int. Conf. on Networking, Sensing and Control, pp. 422-427, 2009. https://doi.org/10.1109/ICNSC.2009.4919313
This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.