An Advanced Extremum Seeking Scheme for the Target Trajectory in Electromagnetic Micromirror System

Author(s):  
Wanyan Sun ◽  
Yonghong Tan

Abstract Now two problems result in bad control in the development of the electromagnetic micromirror system. One is that theoretical model in electromagnetic micromirror system is difficult to be determined; Another is that parameters in common control need to be tuned according to the experience. In this paper, cost function concept is proposed to determine the model order in slow-scan axis control of the electromagnetic micromirror. Then recursive least square scheme is built to off-line identify this model. Furthermore, an advanced extremum seeking scheme along with backtracking line search is exploited, which can automatically identify the best parameter value before each extremum search to improve the controllability based on this model for the target trajectory in slow-scan axis control. And the convergence of it is proved. Finally, the experiments and the simulations verify this method proposed valid.

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Wanyan Sun ◽  
Yonghong Tan

AbstractIn this paper, a simplified dynamic model is constructed to describe the main characteristic of electromagnetic micro-mirror. Then, based on the information provided by the derived simplified model, a model-guided extremum seeking control (MGESC) scheme with backtracking line search is developed, which can automatically estimate the best value of step-size at each search iteration to improve the performance of the control system for target tracking. Then, the convergence of the proposed MGES algorithm is proved. Finally, the experimental results and the simulations are presented to verify the proposed method.


Author(s):  
Omar Avalos ◽  
Erik Cuevas ◽  
Héctor G. Becerra ◽  
Jorge Gálvez ◽  
Salvador Hinojosa ◽  
...  

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 505
Author(s):  
Jianfeng Chen ◽  
Jiantian Sun ◽  
Shulin Hu ◽  
Yicai Ye ◽  
Haoqian Huang ◽  
...  

A variety of accurate information inputs are of great importance for automotive control. In this paper, a novel joint soft-sensing strategy is proposed to obtain multi-information under diverse vehicle driving scenarios. This strategy is realized by an information interaction including three modules: vehicle state estimation, road slope observer and vehicle mass determination. In the first module, a variational Bayesian-based adaptive cubature Kalman filter is employed to estimate the vehicle states with the time-variant noise interference. Under the assumption of road continuity, a slope prediction model is proposed to reduce the time delay of the road slope observation. Meanwhile, a fast response nonlinear cubic observer is introduced to design the road slope module. On the basis of the vehicle states and road slope information, the vehicle mass is determined by a forgetting-factor recursive least square algorithm. In the experiments, a contrasted strategy is introduced to analyse and evaluate performance. Results declare that the proposed strategy is effective and has the advantages of low time delay, high accuracy and good stability.


2012 ◽  
Vol 22 (6) ◽  
pp. 1145-1153 ◽  
Author(s):  
Maw-Lin Leou ◽  
Yi-Ching Liaw ◽  
Chien-Min Wu

Sign in / Sign up

Export Citation Format

Share Document