Influence of Design Reference on Tracking Performance of Feedback Control

Qiqi Zhao , Zhichang Qin , Jianqiao Sun

Transactions of Tianjin University ›› 2018, Vol. 24 ›› Issue (1) : 66 -72.

PDF
Transactions of Tianjin University ›› 2018, Vol. 24 ›› Issue (1) : 66 -72. DOI: 10.1007/s12209-017-0100-z
Research Article

Influence of Design Reference on Tracking Performance of Feedback Control

Author information +
History +
PDF

Abstract

In this paper, we present an investigation on the tracking performances of feedback control as a function of reference signals. We use multi-objective optimal designs of feedback controls as a fair basis for comparing different control designs, and examine step, ramp, and periodic signals at various frequencies. Through comparing the tracking performances of controls designed with different reference signals, we find that the controls designed with ramp signals perform better in tracking step and ramp references than those designed with step signals. To track periodic signals, we find that the controls designed with periodic signals at the same frequency generally provide the best performance, and those designed with step and ramp signals perform comparably.

Keywords

Reference signal / Tracking performance / Feedback control / Multi-objective optimization

Cite this article

Download citation ▾
Qiqi Zhao, Zhichang Qin, Jianqiao Sun. Influence of Design Reference on Tracking Performance of Feedback Control. Transactions of Tianjin University, 2018, 24(1): 66-72 DOI:10.1007/s12209-017-0100-z

登录浏览全文

4963

注册一个新账户 忘记密码

References

[1]

Yu CC. Autotuning of PID controllers, 2006, 2 London: Springer.

[2]

Doyle JC, Francis BA, Tannenbaum AR. Feedback control theory, 1992, New York: Macmillan.

[3]

Liu GP, Whidborne JF, Duan GR (2002) Multiobjective design using various control techniques. In: Proceedings of IEEE international symposium on computer aided control system design, Glasgow, UK, pp 1–6 2002

[4]

Caramia M, Dell’Olmo P. Multi-objective management in freight logistics: increasing capacity, service level and safety with optimization algorithms, 2008, London: Springer.

[5]

Oliveira PBDM, Pires EJS, Cunha JB, et al. Multi-objective particle swarm optimization design of PID controllers. Lecture notes in computer science 5518, 2009, Germany: Springer 1222-1230.

[6]

Coello CAC, Lechuga MS (2002) MOPSO: a proposal for multiple objective particle swarm optimization. In: Proceedings of the 2002 congress on evolutionary computation. Honululu, USA, pp 1051–1056

[7]

Poli R, Kennedy J, Blackwell T. Particle swarm optimization. Swarm Intell, 2007, 1: 33-57.

[8]

Coello CAC, Pulido GT, Lechuga MS. Handling multiple objectives with particle swarm optimization. IEEE Trans Evol Comput, 2004, 8: 256-279.

[9]

Coello CAC, Lamont GB, Veldhuizen DAV. Evolutionary algorithms for solving multi-objective problems, 2002, New York: Kluwer

[10]

Sardahi Y (2016) Multi-objective optimal design of control systems. Dissertation, University of California, Merced

[11]

Xiong FR, He MX, Naranjani Y, et al. Multiobjective optimization of non-uniform beam for minimum weight and sound radiation. Trans Tianjin Univ, 2017, 23: 380-393.

AI Summary AI Mindmap
PDF

151

Accesses

0

Citation

Detail

Sections
Recommended

AI思维导图

/