This paper modifies the prototype model-free adaptive control (PMFAC) approach in which system output fluctuation and overshoot (SOFO) is easy to occur. Targeting a class of nonlinear discrete-time single-input single-output systems, historical input/output sensor data are utilized to online construct equivalent explicit data model for control design, making the controller model-free. Then, a novel compensation input term including a compensation gain and a signum function parameterized by incremental output is proposed to soften system inputs, which can in turn restrain the SOFO. Numerical simulations and a speed control experiment for a direct current motor verify that the modified approach can restrain the SOFO successfully and reduce response time compared with the PMFAC.
BejarbanehEYAhangarnejadAHBagheriA, et al. (2020) Optimal design of adaptive and proportional integral derivative controllers using a novel hybrid particle swarm optimization algorithm. Transactions of the Institute of Measurement and Control42(8): 1492–1510.
2.
BuXHWangQFHouZ, et al. (2018) Data driven control for a class of nonlinear systems with output saturation. ISA Transactions81: 1–7.
3.
BurakEBBüilentOMetinUS (2023) Precise control of gimbal systems with model reference sliding mode controller. Journal of Guidance, Control, and Dynamics46(8): 1620–1626.
4.
CorradiniMLIppolitiGOrlandoG (2020) Data-driven model-free adaptive control with prescribed performance: A rigorous sliding-mode based approach. IFAC-PapersOnLine53(2): 4001–4006.
5.
DongLYangGH (2018) Performance-based data-driven model-free adaptive sliding mode control for a class of discrete-time nonlinear processes. Journal of Process Control68(2018): 186–194.
6.
EijkLBeerSRobinMG, et al. (2023) Frequency-domain properties of the hybrid integrator-gain system and its application as a nonlinear lag filter. IEEE Transactions on Control Systems Technology31(2): 905–912.
7.
FuQ (2020) Iterative learning control for nonlinear heterogeneous multi-agent systems with multiple leaders. Transactions of the Institute of Measurement and Control43(4): 1–8.
8.
HouZJinS (2013) Model Free Adaptive Control: Theory and Applications. Berlin and Heidelberg: Springer.
9.
HouZSJinST (2011) A novel data-driven control approach for a class of discrete-time nonlinear systems. IEEE Transactions on Control Systems Technology19(6): 1549–1558.
10.
HouZSLiuSDTianTT (2017) Lazy-learning-based data-driven model-free adaptive predictive control for a class of discrete-time nonlinear systems. IEEE Transactions on Neural Networks and Learning Systems28(8): 1–15.
11.
HuQLLiBZhangYM (2014) Nonlinear proportional-derivative control incorporating closed-loop control allocation for spacecraft. Journal of Guidance, Control, and Dynamics37(3): 799–812.
12.
IzciD (2021) 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. Transactions of the Institute of Measurement and Control43(14): 3195–3211.
13.
JakubOMadeleineML (2023) Iterative feedback tuning of proportional-integral controller parameters for adaptive deep brain stimulation. In: 11th international IEEE/EMBS conference on neural engineering, Baltimore, MD, 24–27 April, pp. 1–4. New York: IEEE.
14.
JiangGHouZS (2020) A data-driven approach for trajectory-based aircraft operation with controlled time of arrival and along-track wind effects. Transactions of the Institute of Measurement and Control42(12): 2166–2177.
15.
JiangHRLinC (2010) Application issues of virtual reference feedback tuning and non-iterative correlation-based tuning. In: Proceedings of the 2010 IEEE international conference on information and automation, Harbin, China, 20–23 June, pp. 1194–1199. New York: IEEE.
16.
LiuSDHouZSZhangX, et al. (2020) Model-free adaptive control method for a class of unknown MIMO systems with measurement noise and application to quadrotor aircraft. IET Control Theory and Applications14(15): 2084–2096.
17.
LüFLWangJFFanCJ, et al. (2008) A model-free adaptive control of welding pool dynamics during arc welding. In: 2008 IEEE conference on cybernetics and intelligent systems, Chengdu, China, 21–24 September 2016, pp. 591–595. New York: IEEE.
18.
MaraouiSBouzraraKJoséR (2019) Synthesis of a proportional integral derivative control law based on the meixner-like model. Transactions of the Institute of Measurement and Control41(3): 780–792.
19.
OsamaFAJairoVChenYQ (2023) Bode’s ideal cut-off based virtual reference feedback tuning controller design. In: International conference on fractional differentiation and its applications, Ajman, United Arab Emirates, 14–16 March, pp. 1–15. New York: IEEE.
20.
QiPYZhaoXW (2019) Flight control for very flexible aircraft using model-free adaptive control. Journal of Guidance, Control, and Dynamics43(3): 1–12.
21.
QiuXJWangYCZhangH, et al. (2023) Resilient model free adaptive distributed lfc for multi-area power systems against jamming attacks. IEEE Transactions on Neural Networks and Learning Systems34(8): 4120–4129.
22.
RachaelTKDanielERKlasnjaP, et al. (2022) Model personalization in behavioral interventions using model-on-demand estimation and discrete simultaneous perturbation stochastic approximation. In: American control conference, Atlanta, GA, 08–10 June, pp. 671–676. New York: IEEE.
23.
TarcísioCDWilliamRW (2022) Extremum seeking control applied to airfoil trailing-edge noise suppression. AIAA Journal60(2): 823–843.
24.
WengYNanDWangN, et al. (2022) Compound robust tracking control of disturbed quadrotor unmanned aerial vehicles: A data-driven cascade control approach. Transactions of the Institute of Measurement and Control44(4): 941–951.
25.
WonghongT (2023) Intelligent control by unfalsified control theory. In: 2023 International electrical engineering congress (iEECON), Krabi, Thailand, 11–15 & 19–21 January, pp. 93–96. New York: IEEE.
26.
WuXWangMTShahidehpourM, et al. (2021) Model-free adaptive control of STATCOM for SSO mitigation in DFIG-based wind farm. IEEE Transactions on Power Systems36(6): 1–12.
27.
XuJLinNChiRH (2021a) Improved high-order model free adaptive control. In: 10th Data driven control and learning systems conference, Suzhou, China, 14–16 May, pp. 704–708. New York: IEEE.
28.
XuYWangXZhaiY, et al. (2021b) Precise variable spraying system based on improved genetic proportional-integral-derivative control algorithm. Transactions of the Institute of Measurement and Control43(14): 3255–3266.
29.
YinHRenYWangL, et al. (2022) Model free adaptive traffic signal control for four-phase intersections. In: 2022 IEEE 11th Data driven control and learning systems conference (DDCLS), Chengdu, China, 03–05 August, pp. 752–756. New York: IEEE.
30.
ZhangGPZhuQX (2021) Event-triggered optimal control for nonlinear stochastic systems via adaptive dynamic programming. Nonlinear Dynamics105: 387–401.
31.
ZhaoLGHeWLLvFK (2021) Model-free adaptive control for parafoil systems based on the iterative feedback tuning method. IEEE Access9: 35900–35914.
32.
ZhuYMHouZS (2012) Enhanced model free adaptive control by integrating with lazy learning. In: 2012 24th Chinese control and decision conference (CCDC), Taiyuan, China, 23–25 May, pp. 2019–2024. New York: IEEE.
