TY - GEN
T1 - Semi-parametric adaptive control of discrete-time systems using extreme learning machine
AU - Zhou, Hao
AU - Ma, Hongbin
AU - Li, Nannan
AU - Yang, Chenguang
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/2
Y1 - 2017/7/2
N2 - In this paper, we investigate a novel semi-parametric adaptive design approach for discrete-time systems as a further study of the challenging work on dealing with both parametric and nonparametric uncertainties. An extended version of information concentration (IC) estimator other than traditional recursive identification algorithm is adopted to estimate unknown parameters with a priori knowledge considered. To best utilize input-output history data, an improved version of extreme learning machine is developed to approximate nonparametric part. According to accurate estimates of uncertainties, control signal is established and subsequent simulation examples indicate that the designed adaptive control strategy can guarantee the boundedness of all the closed-loop signals and achieves asymptotic tracking performance.
AB - In this paper, we investigate a novel semi-parametric adaptive design approach for discrete-time systems as a further study of the challenging work on dealing with both parametric and nonparametric uncertainties. An extended version of information concentration (IC) estimator other than traditional recursive identification algorithm is adopted to estimate unknown parameters with a priori knowledge considered. To best utilize input-output history data, an improved version of extreme learning machine is developed to approximate nonparametric part. According to accurate estimates of uncertainties, control signal is established and subsequent simulation examples indicate that the designed adaptive control strategy can guarantee the boundedness of all the closed-loop signals and achieves asymptotic tracking performance.
KW - Adaptive control
KW - Extreme learning machine
KW - Information concentration
KW - Nonparametric uncertainty
UR - http://www.scopus.com/inward/record.url?scp=85050571336&partnerID=8YFLogxK
U2 - 10.1109/ICMIC.2017.8321546
DO - 10.1109/ICMIC.2017.8321546
M3 - Conference contribution
AN - SCOPUS:85050571336
T3 - Proceedings of 2017 9th International Conference On Modelling, Identification and Control, ICMIC 2017
SP - 705
EP - 710
BT - Proceedings of 2017 9th International Conference On Modelling, Identification and Control, ICMIC 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 9th International Conference on Modelling, Identification and Control, ICMIC 2017
Y2 - 10 July 2017 through 12 July 2017
ER -