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

• Published in: IEEE transactions on industrial electronics (1982) Vol. 68; no. 8; pp. 7294 - 7308
• Main Authors: Huang, Su-Dan, Hu, Zhi-Yong, Cao, Guang-Zhong, He, Jiangbiao, Jing, Gang, Liu, Yan
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.08.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Actuators; Adaptation models; Analytical models; Computational modeling; Constraint modelling; Control methods; Control theory; Dynamic models; Dynamical systems; Equations of state; Feedback control; Matrix methods; Model predictive control (MPC); Motion systems; Motors; neural network (NN); Neural networks; nonlinear dynamic model (NDM); Nonlinear dynamics; Nonlinearity; Optimization; planar motor; Planar motors; Position control; Prediction models; Predictive control; Predictive models; Saturation; State feedback
• ISSN: 0278-0046; 1557-9948
• DOI: 10.1109/TIE.2020.3009580

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.