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

17 Scopus citations

Abstract

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
Volume68
Issue number8
DOIs
StatePublished - Aug 2021

Bibliographical note

Publisher Copyright:
© 1982-2012 IEEE.

Keywords

  • 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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