TY - GEN
T1 - Anti-disturbance iterative learning tracking control for general non-Gaussian stochastic systems
AU - Yi, Yang
AU - Guo, Lei
AU - Wang, Hong
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2015/3/2
Y1 - 2015/3/2
N2 - In this paper, a class of general non-Gaussian stochastic systems with disturbances are studied. Based on the disturbance observer (DO) design method, an anti-disturbance iterative learning control (ILC) algorithm is proposed by establishing the statistic information tracking control (SITC) framework. Different from the existing stochastic control methods, the driven information for control feedback is the output statistic information sets (SISs) relying on sample data of the non-Gaussian stochastic output, rather than the output PDFs. A novel model-free ILC optimization problem is addressed by combining the DO design with ILC algorithm. The controller design can be achieved based on the convex optimization to ensure the configured system stability and convergence of the tracking error to zero. Meanwhile, the satisfactory disturbance estimation and rejection performance can also be guaranteed. In the simulation, a typical 3-parameter Weibull distribution is considered to demonstrate the effectiveness and the practical significance of the proposed algorithm.
AB - In this paper, a class of general non-Gaussian stochastic systems with disturbances are studied. Based on the disturbance observer (DO) design method, an anti-disturbance iterative learning control (ILC) algorithm is proposed by establishing the statistic information tracking control (SITC) framework. Different from the existing stochastic control methods, the driven information for control feedback is the output statistic information sets (SISs) relying on sample data of the non-Gaussian stochastic output, rather than the output PDFs. A novel model-free ILC optimization problem is addressed by combining the DO design with ILC algorithm. The controller design can be achieved based on the convex optimization to ensure the configured system stability and convergence of the tracking error to zero. Meanwhile, the satisfactory disturbance estimation and rejection performance can also be guaranteed. In the simulation, a typical 3-parameter Weibull distribution is considered to demonstrate the effectiveness and the practical significance of the proposed algorithm.
KW - Disturbance observer (DO)
KW - Iterative learning control (ILC)
KW - Non-Gaussian stochastic systems
KW - Statistic information sets (SISs)
KW - Stochastic distribution control (SDC)
UR - https://www.scopus.com/pages/publications/84932096954
U2 - 10.1109/WCICA.2014.7052735
DO - 10.1109/WCICA.2014.7052735
M3 - 会议稿件
AN - SCOPUS:84932096954
T3 - Proceedings of the World Congress on Intelligent Control and Automation (WCICA)
SP - 327
EP - 334
BT - Proceeding of the 11th World Congress on Intelligent Control and Automation, WCICA 2014
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 11th World Congress on Intelligent Control and Automation, WCICA 2014
Y2 - 29 June 2014 through 4 July 2014
ER -