School of Mechanical Engineering, Yangzhou University, Yangzhou 225000, China
| Abstract: | A modified model-free adaptive control (MFAC) approach is proposed to address output fluctuation and overshoot problems existing in the prototype MFAC. Targeting a class of discrete-time nonlinear single-input single-output systems, historical input/output information is applied to construct explicit data model which is equivalent to the plant model in a real-time online manner. Control scheme is designed based on the data model such that model-free characteristic in control process is realized. A novel optimization index function is proposed by using the data model. An input compensation term is introduced on the basis of minimizing the tracking error in the prototype MFAC for restraining output response speed and softening control input. The input amplitude is reduced by the input compensation term for restraining output fluctuation and overshoot when the output change rate is too large. Adaptive matching between the output response and the control input is also realized. Numerical simulation results show that, compared with the prototype MFAC, the modified MFAC can significantly suppress the system output oscillation and overshoot under the same response time conditions. The output fluctuation of the closed-loop system is significantly reduced. The positive overshoot is reduced by about 10%, and the negative overshoot is effectively suppressed. The dynamic response of the closed-loop system is smoother and the operation is more stable. |
| Keywords: | Model-Free; Compensatory Input; Oscillation and Overshoot Suppression; Data Model |
| DOI: | 10.57237/j.mse.2026.01.002 |
| 1. | 国家自然科学基金 (52405428) |
| [1] | Aal O F A, Viola J, Chen Y Q. Bode’s ideal cut-off based virtual reference feedback tuning controller design [C]//2023 International Conference on Fractional Differentiation and Its Applications (ICFDA). IEEE, 2023: 1-5. https://doi.org/10.1109/ICFDA58234.2023.10153227 |
| [2] | 史乐珍, 王华. 基于非线性最小二乘迭代的分数阶 PD μ 控制器整定 [J]. 微电子学与计算机, 2018, 35(4): 103-107. https://doi.org/10.19304/j.cnki.issn1000-7180.2018.04.021 |
| [3] | 吴国华. 一种实用航班座控自适应随机逼近算法 [J]. 环球科学与工程, 2025: 21-32. https://doi.org/10.62836/gse.v2i5.547 |
| [4] | Déda T C, Wolf W R. Extremum seeking control applied to airfoil trailing-edge noise suppression [J]. AIAA Journal, 2022, 60(2): 823-843. https://doi.org/10.2514/1.J060634 |
| [5] | Maraoui S, Bouzrara K, Ragot J. Synthesis of a Proportional Integral Derivative control law based on the Meixner-like model [J]. Transactions of the Institute of Measurement and Control, 2019, 41(3): 780-792. https://doi.org/10.1177/0142331218770484 |
| [6] | Bejarbaneh E Y, Ahangarnejad A H, Bagheri A, et al. Optimal design of adaptive and proportional integral derivative controllers using a novel hybrid particle swarm optimization algorithm [J]. Transactions of the Institute of Measurement and Control, 2020, 42(8): 1492-1510. https://doi.org/10.1177/0142331219891571 |
| [7] | 严家政, 专祥涛. 基于强化学习的参数自整定及优化算法 [J]. 智能系统学报, 2021, 17(2): 341-347. https://doi.org/10.11992/tis.202012038 |
| [8] | 刘雷伟, 何婷, 王佑. 多变量系统的分散式补偿自抗扰控制方法与频域分析 [J]. 信息与控制, 2024, 53(4): 540-549. https://doi.org/10.13976/j.cnki.xk.2024.3098 |
| [9] | Izci D. Design and application of an optimally tuned PID controller for DC motor speed regulation via a novel hybrid Lévy flight distribution and Nelder–Mead algorithm [J]. Transactions of the Institute of Measurement and Control, 2021, 43(14): 3195-3211. https://doi.org/10.1177/01423312211019633 |
| [10] | van Eijk L F, Beer S, van Es R M J, et al. Frequency-domain properties of the hybrid integrator-gain system and its application as a nonlinear lag filter [J]. IEEE Transactions on Control Systems Technology, 2022, 31(2): 905-912. https://doi.org/10.1109/TCST.2022.3196878 |
| [11] | Zhu Y, Hou Z. Enhanced model free adaptive control by integrating with lazy learning [C]//2012 24th Chinese Control and Decision Conference (CCDC). IEEE, 2012: 2019-2024. https://doi.org/10.1109/CCDC.2012.6244325 |
| [12] | Fu Q. Iterative learning control for nonlinear heterogeneous multi-agent systems with multiple leaders [J]. Transactions of the Institute of Measurement and Control, 2021, 43(4): 854-861. https://doi.org/10.1177/0142331220941636 |
| [13] | 李瑞敏, 唐瑾. 过饱和交叉口交通信号控制动态规划优化模型 [J]. 交通运输工程学报, 2015, 15(6): 101-109. https://doi.org/10.19818/j.cnki.1671-1637.2015.06.013 |
| [14] | 侯忠生, 金尚泰. 无模型自适应控制: 理论与应用 [M]. 科学出版社, 2013. |
| [15] | Weng Y, Nan D, Wang N, et al. Compound robust tracking control of disturbed quadrotor unmanned aerial vehicles: A data-driven cascade control approach [J]. Transactions of the Institute of Measurement and Control, 2022, 44(4): 941-951. https://doi.org/10.1177/01423312211043675 |
| [16] | Jiang G, Hou Z. A data-driven approach for trajectory-based aircraft operation with controlled time of arrival and along-track wind effects [J]. Transactions of the Institute of Measurement and Control, 2020, 42(12): 2166-2177. https://doi.org/10.1177/0142331220909004 |
| [17] | Liu S, Hou Z, Zhang X, et al. Model‐free adaptive control method for a class of unknown MIMO systems with measurement noise and application to quadrotor aircraft [J]. IET Control Theory & Applications, 2020, 14(15): 2084-2096. https://doi.org/10.1049/iet-cta.2020.0073 |
| [18] | Hou Z, Jin S. A novel data-driven control approach for a class of discrete-time nonlinear systems [J]. IEEE Transactions on Control Systems Technology, 2010, 19(6): 1549-1558. https://doi.org/10.1109/TCST.2010.2093136 |
| [19] | Hou Z, Liu S, Tian T. Lazy-learning-based data-driven model-free adaptive predictive control for a class of discrete-time nonlinear systems [J]. IEEE transactions on neural networks and learning systems, 2016, 28(8): 1914-1928. https://doi.org/10.1109/TNNLS.2016.2561702 |
| [20] | Zhao L, He W, Lv F. Model-free adaptive control for parafoil systems based on the iterative feedback tuning method [J]. IEEE Access, 2021, 9: 35900-35914. https://doi.org/10.1109/ACCESS.2021.3050275 |
| [21] | Xu Y, Wang X, Zhai Y, et al. Precise variable spraying system based on improved genetic proportional-integral-derivative control algorithm [J]. Transactions of the Institute of Measurement and Control, 2021, 43(14): 3255-3266. https://doi.org/10.1177/01423312211022446 |
| [22] | Wu X, Wang M, Shahidehpour M, et al. Model-free adaptive control of STATCOM for SSO mitigation in DFIG-based wind farm [J]. IEEE Transactions on power systems, 2021, 36(6): 5282-5293. https://doi.org/10.1109/TPWRS.2021.3082951 |
| [23] | Qiu X, Wang Y, Zhang H, et al. Resilient model free adaptive distributed LFC for multi-area power systems against jamming attacks [J]. IEEE Transactions on Neural Networks and Learning Systems, 2021, 34(8): 4120-4129. https://doi.org/10.1109/TNNLS.2021.3123235 |
| [24] | Yin H, Ren Y, Wang L, et al. Model free adaptive traffic signal control for four-phase intersections [C]//2022 IEEE 11th Data Driven Control and Learning Systems Conference (DDCLS). IEEE, 2022: 752-756. https://doi.org/10.1109/DDCLS55054.2022.9858479 |
| [25] | Lu F L, Wang J F, Fan C J, et al. A model-free adaptive control of welding pool dynamics during arc welding [C]//2008 IEEE Conference on Cybernetics and Intelligent Systems. IEEE, 2008: 591-595. https://doi.org/10.1109/ICCIS.2008.4670923 |
| [26] | 任凯, 高传强, 张伟伟. 翼型激波抖振的无模型自适应控制 [J]. 空气动力学学报, 2021, 39(6): 149-155. https://doi.org/10.7638/kqdlxxb-2021.0297 |
| [27] | Corradini M L, Ippoliti G, Orlando G. Data-driven model-free adaptive control with prescribed performance: a rigorous sliding-mode based approach [J]. IFAC-PapersOnLine, 2020, 53(2): 4001-4006. https://doi.org/10.1016/j.ifacol.2020.12.2266 |
| [28] | Liu D, Yang G H. Performance-based data-driven model-free adaptive sliding mode control for a class of discrete-time nonlinear processes [J]. Journal of Process Control, 2018, 68: 186-194. https://doi.org/10.1016/j.jprocont.2018.06.006 |
| [29] | Bu X, Wang Q, Hou Z, et al. Data driven control for a class of nonlinear systems with output saturation [J]. ISA transactions, 2018, 81: 1-7. https://doi.org/10.1016/j.isatra.2018.07.009 |
We invite active, qualified and high profile scientists and researchers to join as Editorial Board Members.
Join UsScholars with a strong interest in reviewing are invited to join the reviewer panel to ensure the quality of the research to be published.
Join Us