The Permanent Magnet Linear Motor Control Based on Data-driven Control Theory

摘要：

Data-driven control methods have no relations with any structural information, and it is designed only by the I/O data of the controlled system. The model-free direct adaptive nonlinear predictive control (MFDANPC) algorithm of linearization of tight format of a class of SISO of a generalized predictive control (GPC) based on data-driven control theory is applied to permanent magnet linear motor speed and position control .The design of controller is based directly on estimate and prediction of pseudo-partial-derivatives (PPD) derived on-line from the input and output information of the motor motion model using a novel parameter estimation algorithm, predicted by approach for multi-degree prediction. Stability, validity and robustness against exogenous disturbance are proved for nonlinear systems with vaguely known dynamics by the simulation examples and real experiments.

关键词： Data-driven control Adaptive Predictive Control Model-free Adaptive Control Nonlinear Systems Linear Motor

作者: Rongmin Cao Huixing Zhou Zhongsheng Hou

作者单位: School of Industry, China Agricultural University, Beijing 100083, China School of Automation, Beiji School of Industry, China Agricultural University, Beijing 100083, China Advanced Control Systems Laboratory, Beijing Jiaotong University, Beijing 100044, China

会议类型: 国际会议

会议名称: The 22nd China Control and Decision Conference(2010年中国控制与决策会议)

会议地点: 徐州

会议语种:英文

页码: 3164-3169

在线出版日期: 2010-05-26（万方平台首次上网日期，不代表论文的发表时间）