Data-driven Nonparametric Model Adaptive Precision Control for Linear Servo Systems

Nowadays, high-precision motion controls are needed in modern manufacturing industry. A data-driven nonparametric model adaptive control (NMAC) method is proposed in this paper to control the position of a linear servo system. The controller design requires no information about the structure of linear servo system, and it is based on the estimation and forecasting of the pseudo-partial derivatives (PPD) which are estimated according to the voltage input and position output of the linear motor. The characteristics and operational mechanism of the permanent magnet synchronous linear motor (PMSLM) are introduced, and the proposed nonparametric model control strategy has been compared with the classic proportional-integral-derivative (PID) control algorithm. Several real-time experiments on the motion control system incorporating a permanent magnet synchronous linear motor showed that the nonparametric model adaptive control method improved the system s response to disturbances and its position-tracking precision, even for a nonlinear system with incompletely known dynamic characteristics.