Model-free control of a magnetically supported plate

被引:1
作者
Scherer, P. M. [1 ]
Othmane, A. [2 ]
Rudolph, J. [1 ]
机构
[1] Saarland Univ, Chair Syst Theory & Control Engn, Campus A5 1, D-66123 Saarbrucken, Germany
[2] Saarland Univ, Syst Modeling & Simulat, Campus A5 1, D-66123 Saarbrucken, Germany
关键词
Algebraic differentiators; Model-free control; Model-based control; DIFFERENTIATION;
D O I
10.1016/j.conengprac.2024.105950
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Established model -based methods often use a combination of state feedback and observer to control complex systems. They rely on detailed mathematical models that are often hard to derive. Nonetheless, such methods may achieve a high level of accuracy, which justifies the cumbersome modelling. An alternative approach is model -free control, in a form introduced by Fliess and Join, where the system is approximated in a short time interval by a low -order differential equation with unknown parts, a so-called ultra -local model. This control method is a powerful tool, but the parametrisation and the concrete implementation may require time, effort, and experience. The present paper investigates the systematic tuning of a model -free controller for a magnetically supported plate that is modelled as an unstable multiple -input multiple -output system. Furthermore, the incorporation of model information into the model -free controller is investigated. These adaptations ultimately improve results by simplifying parameter tuning and interpretation of estimates. Several experiments are carried out on a test bed to show the capabilities of the proposed algorithms for set point stabilisation and trajectory tracking. The effects of the different parameters in the model -free controllers are addressed, and excellent robustness with respect to actuator faults is demonstrated. Filters for estimating derivatives and unknown quantities are designed using an open -source toolbox.
引用
收藏
页数:14
相关论文
共 50 条
[21]   Adaptive Model-Free Control Based on an Ultra-Local Model With Model-Free Parameter Estimations for a Generic SISO System [J].
Safaei, Ali ;
Mahyuddin, Muhammad Nasiruddin .
IEEE ACCESS, 2018, 6 :4266-4275
[22]   A novel ADP based model-free predictive control [J].
Dong, Na ;
Chen, Zengqiang .
NONLINEAR DYNAMICS, 2012, 69 (1-2) :89-97
[23]   Optimal model-free backstepping control for a quadrotor helicopter [J].
Hossam Eddine Glida ;
Latifa Abdou ;
Abdelghani Chelihi ;
Chouki Sentouh ;
Seif-El-Islam Hasseni .
Nonlinear Dynamics, 2020, 100 :3449-3468
[24]   Stability margins and model-free control: A first look [J].
Fliess, Michel ;
Join, Cedric .
2014 EUROPEAN CONTROL CONFERENCE (ECC), 2014, :454-459
[25]   Inventory Control in Supply Chain: a Model-Free Approach [J].
Nya, Danielle Nyakam ;
Hachour, Samir ;
Abouaissa, Hassane .
IFAC PAPERSONLINE, 2022, 55 (10) :2755-2760
[26]   A novel ADP based model-free predictive control [J].
Na Dong ;
Zengqiang Chen .
Nonlinear Dynamics, 2012, 69 :89-97
[27]   Brake and velocity model-free control on an actual vehicle [J].
Polack, Philip ;
Delprat, Sebastien ;
D'Andrea-Novel, Brigitte .
CONTROL ENGINEERING PRACTICE, 2019, 92
[28]   Robust Model-Free Control Applied to a Quadrotor UAV [J].
Al Younes, Younes ;
Drak, Ahmad ;
Noura, Hassan ;
Rabhi, Abdelhamid ;
El Hajjaji, Ahmed .
JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS, 2016, 84 (1-4) :37-52
[29]   Tonal vibration suppression with a model-free control method [J].
Hu, Fang ;
Chen, Yong ;
Zhang, Zhiyi ;
Hua, Hongxing .
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART M-JOURNAL OF ENGINEERING FOR THE MARITIME ENVIRONMENT, 2012, 226 (M4) :360-370
[30]   Double Neuron Model-free Control for pH Processes [J].
Zhang, Li ;
Wang, Ning .
2010 CHINESE CONTROL AND DECISION CONFERENCE, VOLS 1-5, 2010, :2867-2871