SIL SIMULATION OF MODEL-FREE METHOD FOR IMPROVING OF TIME VARYING DYNAMIC MEASUREMENTS

2021 ◽  
Vol 71 (2) ◽  
Author(s):  
Miroslava Baraharska ◽  
Tsonyo Slavov ◽  
Ivan Markovsky
Keyword(s):  
Time Varying ◽  
Dynamic Measurements ◽  
Model Free

MODEL-FREE METHOD FOR TIME VARYING DYNAMIC MEASUREMENTS IN CONTROL SYSTEM

2020 ◽  
Vol 70 (3) ◽  
pp. 51-60
Author(s):  
Miroslava Baraharska ◽  
Tsonyo Slavov ◽  
Ivan Markovsky
Keyword(s):  
Control System ◽  
Mass Measurement ◽  
Least Square Method ◽  
Information Criteria ◽  
Least Square ◽  
Time Varying ◽  
Dynamic Measurements ◽  
Recursive Least Square ◽  
Model Order Selection ◽  
Model Free

In this paper, a model-free method for time-varying dynamic measurements in a control system is presented. As an example, the dynamic mass-measurement process is examined. The method is based on the on-line estimation of time-varying parameters of autoregressive model by a recursive least square method with a constant trace of the covariance matrix. The model order selection is performed by Akaike’s information criteria. The performance of the method with respect to the variance of measurement noise is empirically tested by simulation experiments. For the aim of comparison, the Kalman filter for estimation of unknown measurement is designed. The simulation results show the advantage of the model-free method.

scholarly journals Model-free Adaptive Switching Control of Uncertain Time-Varying Plants

2011 ◽  
Vol 44 (1) ◽  
pp. 1273-1278 ◽  
Author(s):  
Giorgio Battistelli ◽  
João P. Hespanha ◽  
Edoardo Mosca ◽  
Pietro Tesi
Keyword(s):  
Switching Control ◽  
Time Varying ◽  
Model Free ◽  
Uncertain Time ◽  
Adaptive Switching Control

scholarly journals Data-Driven Model-Free Adaptive Control Based on Error Minimized Regularized Online Sequential Extreme Learning Machine

Energies ◽  
2019 ◽  
Vol 12 (17) ◽  
pp. 3241 ◽  
Author(s):  
Xiaofei Zhang ◽  
Hongbin Ma
Keyword(s):  
Adaptive Control ◽  
Extreme Learning Machine ◽  
Discrete Time ◽  
Control Method ◽  
Time Varying ◽  
Varying Parameters ◽  
Model Free ◽  
Time Varying Parameters ◽  
Learning Machine ◽  
Linearized Model

Model-free adaptive control (MFAC) builds a virtual equivalent dynamic linearized model by using a dynamic linearization technique. The virtual equivalent dynamic linearized model contains some time-varying parameters, time-varying parameters usually include high nonlinearity implicitly, and the performance will degrade if the nonlinearity of these time-varying parameters is high. In this paper, first, a novel learning algorithm named error minimized regularized online sequential extreme learning machine (EMREOS-ELM) is investigated. Second, EMREOS-ELM is used to estimate those time-varying parameters, a model-free adaptive control method based on EMREOS-ELM is introduced for single-input single-output unknown discrete-time nonlinear systems, and the stability of the proposed algorithm is guaranteed by theoretical analysis. Finally, the proposed algorithm is compared with five other control algorithms for an unknown discrete-time nonlinear system, and simulation results show that the proposed algorithm can improve the performance of control systems.

scholarly journals Data-Driven Adaptive Iterative Learning Method for Active Vibration Control Based on Imprecise Probability

Symmetry ◽  
2019 ◽  
Vol 11 (6) ◽  
pp. 746 ◽  
Author(s):  
Liang Bai ◽  
Yun-Wen Feng ◽  
Ning Li ◽  
Xiao-Feng Xue ◽  
Yong Cao
Keyword(s):  
Iterative Learning ◽  
Dynamic Responses ◽  
Data Driven ◽  
Structural Vibration ◽  
Time Varying ◽  
Imprecise Probability ◽  
Source Information ◽  
Structural Dynamic ◽  
Model Free ◽  
P Type

A data-driven adaptive iterative learning (IL) method is proposed for the active control of structural vibration. Considering the repeatability of structural dynamic responses in the vibration process, the time-varying proportional-type iterative learning (P-type IL) method was applied for the design of feedback controllers. The model-free adaptive (MFA) control, a data-driven method, was used to self-tune the time-varying learning gains of the P-type IL method for improving the control precision of the system and the learning speed of the controllers. By using multi-source information, the state of the controlled system was detected and identified. The square root values of feedback gains can be considered as characteristic parameters and the theory of imprecise probability was investigated as a tool for designing the stopping criteria. The motion equation was driven from dynamic finite element (FE) formulation of piezoelectric material, and then was linearized and transformed properly to design the MFA controller. The proposed method was numerically and experimentally tested for a piezoelectric cantilever plate. The results demonstrate that the proposed method performs excellent in vibration suppression and the controllers had fast learning speeds.

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