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Optimized Pseudo-Linearization-Based Model Predictive Controller Design: Direct Data-Driven Approach (2310.19367v4)
Published 30 Oct 2023 in eess.SY and cs.SY
Abstract: To reduce the typical time-consuming routines of plant modeling for model-based controller designs, the fictitious reference iterative tuning (FRIT) has been proposed and has proven to be effective in many applications. However, it is generally difficult to select a reference model properly without information on the plant, which significantly affects the control performance and sometimes leads to considerable performance degradation. To address this problem, we propose a pseudo-linearization (PL) method using FRIT and design a new controller for nonlinear systems that combines data-driven and model-based control. This design considers the input constraints using model predictive control. The effectiveness of the proposed method was evaluated according to several practical references using numerical simulations for nonlinear classes and experiments involving artificial muscles with hysteresis characteristics.
- Z. S. Hou and Z. Wang, “From model–based control to data-driven control: Survey, classification and perspective,” Information Sciences, vol. 235, pp. 3–35, 2013.
- W. Tang and P. Daoutidis, “Data-driven control: Overview and perspectives,” in American Control Conference (ACC), 2022, pp. 1048–1064.
- D. Soudbakhsh et al., “Data-driven control: Theory and applications,” in American Control Conference (ACC), 2023, pp. 1922–1939.
- J. C. Spall and J. A. Cristion, “Model-free control of nonlinear stochastic systems with discrete-time measurements,” IEEE Transactions on Automatic Control, vol. 43, no. 9, pp. 1198–1210, 1998.
- M. Campi, A. Lecchini, and S. Savaresi, “Virtual reference feedback tuning: A direct method for the design of feedback controllers,” Automatica, vol. 38, no. 8, pp. 1337–1346, 2002.
- S. Soma, O. Kaneko, and T. Fujii, “A new method of controller parameter tuning based on input-output data – fictitious reference iterative tuning (frit) –,” in IFAC Proceedings Volumes, vol. 37, no. 37, 2004, pp. 789–794.
- Z. Hou and S. Xiong, “On model-free adaptive control and its stability analysis,” IEEE Transactions on Automatic Control, vol. 64, no. 11, pp. 4555–4569, 2019.
- H. Hjalmarsson, S. Gunnarsson, and M. Gevers, “A convergent iterative restricted complexity control design scheme,” in Proceedings of 1994 33rd IEEE Conference on Decision and Control, vol. 2, 1994, pp. 1735–1740.
- M. Fliess and C. Join, “Model-free control,” International Journal of Control, vol. 86, pp. 2228–2252, 2013.
- H. Yang and S. Li, “A data-driven predictive controller design based on reduced hankel matrix,” in 10th Asian Control Conference (ASCC), 2015, pp. 1–7.
- Y. Fujimoto, “Categorization of data-driven feedback tuning methods: Forward, inverse, and factorization approaches,” in IFAC Proceedings Volumes, 2023, pp. 10 890–10 894, to be appeared.
- S. Masuda, M. Kano, and Y. Yasuda, “A fictitious reference iterative tuning method with simultaneous delay parameter tuning of the reference model,” in 2009 International Conference on Networking, Sensing and Control, 2009, pp. 422–427.
- D. Piga, S. Formentin, and A. Bemporad, “Direct data-driven control of constrained systems,” IEEE Transactions on Control Systems Technology, vol. 26, no. 4, pp. 1422–1429, 2018.
- S. Fujii, M. Miyakoshi, S. Wakitani et al., “Vehicle yaw rate control system design based on smart mbd,” in IFAC Proceedings Volumes, vol. 54, 2021, pp. 508–513.
- M. Sekine, S. Tsuruhara, and K. Ito, “Mpc for artificial muscles using frit based optimized pseudo linearization model,” in IFAC Proceedings Volumes, to be appeared, pp. 7855–7860.
- T. Yamamoto, K. Takao, and T. Yamada, “Design of a data-driven pid controller,” IEEE Transactions on Control Systems Technology, vol. 17, no. 1, pp. 29–39, 2009.
- J. Mattingley and S. Boyd, “Cvxgen: A code generator for embedded convex optimization,” Optimization and Engineering, vol. 13, pp. 1–27, 2011.
- 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.
- 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.
- 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,” International Journal of Automation Technology, vol. 16, no. 4, pp. 436–447, 2022.
- C. Zhang, P. Zhu, Y. Lin et al., “Fluid-driven artificial muscles: bio-design, manufacturing, sensing, control, and applications,” Bio-Design and Manufacturing, vol. 4, pp. 123–145, 2021.
- S. Miyakawa, “Aqua drive system: A technology using tap water and its applications,” in Proceedings of the 8th JFPS International Symposium on Fluid Power, 2011, pp. 26–37.
- W. Kobayashi, S. Dohta, T. Akagi, and K. Ito, “Analysis and modeling of tap-water/pneumatic drive mckibben type artificial muscles,” International Journal of Mechanical Engineering and Robotics Research, vol. 6, no. 6, pp. 463–466, 2017.
- J. E. Slightam and M. L. Nagurka, “Theoretical control-centric modeling for precision model-based sliding mode control of a hydraulic artificial muscle actuator,” Journal of Dynamic Systems, Measurement, and Control, vol. 143, pp. 1–10, 2021.
- S. Takada, O. Kaneko, T. Nakamura, and S. Yamamoto, “Data-driven tuning of nonlinear internal model controllers for pneumatic artificial muscles,” in 2014 4th Australian Control Conference (AUCC), 2014, pp. 13–18.
- 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,” Journal of Robotics and Mechatronics, vol. 34, no. 3, pp. 664–676, 2022.