改进滑模观测器的BLDCM无模型自适应控制

发布时间：2018-10-23 17:54

【摘要】：为了解决传统滑模观测器(SMO)的抖振与相位延迟问题以及传统PI控制动态性能差的问题。首先提出了一种新型的切换函数对传统滑模观测器进行了改进,这种新型的切换函数是用双边界层的思想实现的,并且将改进的滑模观测器运用到无刷直流电机(BLDCM)直接转矩控制系统中,实现了无刷直流电机无位置传感器控制,其次将全格式线性化无模型自适应控制(MFAC)算法应用于无刷直流电机调速控制器的设计中。仿真结果表明,改进的滑模观测器的反电动势观测误差比传统滑模观测器小了4%左右,并且无模型自适应控制的动态性能要优于传统PI控制。改进的滑模观测器能够准确地估计无刷直流电机的反电动势,有效地削弱抖振,无需额外增加低通滤波器,简化了系统结构。无模型自适应控制算法提高了系统的鲁棒性、稳定性和快速性。
[Abstract]:In order to solve the problem of buffeting and phase delay of traditional sliding mode observer (SMO) and the problem of poor dynamic performance of traditional PI control. Firstly, a new switching function is proposed to improve the traditional sliding mode observer. This new switching function is realized with the idea of double boundary layer. The improved sliding mode observer is applied to the (BLDCM) direct torque control system of brushless DC motor to realize sensorless control of brushless DC motor. Secondly, the full-format linearization model-free adaptive control (MFAC) algorithm is applied to the design of brushless DC motor speed controller. The simulation results show that the observed error of the improved sliding mode observer is about 4% less than that of the traditional sliding mode observer, and the dynamic performance of the adaptive control without model is better than that of the traditional PI control. The improved sliding mode observer can accurately estimate the back EMF of brushless DC motor, effectively weaken buffeting and simplify the system structure without adding low pass filter. The model-free adaptive control algorithm improves the robustness, stability and rapidity of the system.
【作者单位】：河南理工大学电气工程与自动化学院自动化系;
【基金】：河南省重点科技攻关项目(102102210197) 河南省高等学校矿山信息化重点学科开放实验室开放基金河南理工大学博士基金(B2010-23)项目资助
【分类号】：TM33

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