Open AccessData‐driven parameter tuning for rational feedforward controller: Achieving optimal estimation via instrumental variable
Author(s) -
Huang Weicai,
Yang Kaiming,
Zhu Yu,
Lu Sen
Publication year - 2021
Publication title -
iet control theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.059
H-Index - 108
eISSN - 1751-8652
pISSN - 1751-8644
DOI - 10.1049/cth2.12093
Subject(s) - instrumental variable , control theory (sociology) , feed forward , variable (mathematics) , estimation theory , computer science , variance (accounting) , controller (irrigation) , control variable , mathematical optimization , mathematics , control (management) , algorithm , control engineering , engineering , artificial intelligence , machine learning , mathematical analysis , agronomy , accounting , business , biology
Feedforward control has been widely used to improve the tracking performance of precision motion systems. This paper develops a new data‐driven feedforward tuning approach associated with rational basis functions. The aim is to obtain the global optimum with optimal estimation accuracy. First, the instrumental variable is employed to ensure the unbiased estimation of the global optimum. Then, the optimal instrumental variable which leads to the highest estimation accuracy is derived, and a new refined instrumental variable method is exploited to estimate the optimal instrumental variable. Moreover, the estimation accuracy of the optimal parameter is further improved through the proposed parameter updating law. Simulations are conducted to test the parameter estimation accuracy of the proposed approach, and it is demonstrated that the global optimum is unbiasedly estimated with optimal parameter estimation accuracy in terms of variance with the proposed approach. Experiments are performed and the results validate the excellent performance of the proposed approach for varying tasks.