Input-Constrained-Nonlinear-Dynamic-Model-Based Predictive Position Control of Planar Motors

Su Dan Huang, Zhi Yong Hu, Guang Zhong Cao, Jiangbiao He, Gang Jing, Yan Liu

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


In this article, a predictive position control method based on a novel input-constrained nonlinear dynamic model (NDM) is proposed for time-varying position tracking of planar motors. The motivation lies in the possible utility of this method for motion systems. This method uses NDM subject to input constraint to deal with actuator saturation rather than uses a constrained optimization problem, such that it differs from conventional model predictive control. The NDM is represented in state-space equations (SSEs) to describe dynamic behaviors of the system constituted by the planar motor and an input saturation module. In contrast to linear SSEs, this model has the same linear vector-matrix form; the difference is that it applies saturation functions of states to replace states of state equation in linear SSEs for representing nonlinearity. By employing a self-designed neural network, the parameters of this model are determined via experimental sample data. With this model, a nonlinear multistep predictive model subject to input constraint is developed. Additionally, an explicitly analytical state feedback control law is approximately deduced by solving an unconstrained optimization problem subject to the nonlinear predictive model. Finally, simulation and experimental results show the effectiveness of the proposed method.

Original languageEnglish
Article number9145788
Pages (from-to)7294-7308
Number of pages15
JournalIEEE Transactions on Industrial Electronics
Issue number8
StatePublished - Aug 2021

Bibliographical note

Funding Information:
Manuscript received November 8, 2019; revised April 6, 2020 and June 4, 2020; accepted June 26, 2020. Date of publication July 21, 2020; date of current version April 27, 2021. This work was supported in part by the National Natural Science Foundation of China under Grant 51907128, Grant 51677120, and Grant U1813212, in part by the Natural Science Foundation of Guangdong Province, China under Grant 2017A030310460, and in part by the Shenzhen Government Fund under Grant JCYJ20190808142211388, Grant JCYJ20180305124348603, Grant JSGG20191126151001800, and Grant JCYJ20170817100841792. (Corresponding author: Guang-Zhong Cao.) Su-Dan Huang, Zhi-Yong Hu, and Guang-Zhong Cao are with the Guangdong Key Laboratory of Electromagnetic Control and Intelligent Robots, Shenzhen Key Laboratory of Electromagnetic Control, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China (e-mail:;;

Publisher Copyright:
© 1982-2012 IEEE.


  • Model predictive control (MPC)
  • neural network (NN)
  • nonlinear dynamic model (NDM)
  • planar motor
  • position control

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering


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