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

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.

Automated summary

In this study, a new predictive position control method based on a nonlinear dynamic model was proposed for time-varying position tracking of planar motors. The model parameters were determined using a self-designed neural network and experimental data, leading to the development of a nonlinear multistep predictive model subject to input constraint. An explicit state feedback control law was approximately derived through solving an unconstrained optimization problem, showing the effectiveness of the proposed method in simulation and experimental results.