改进滑模观测器的BLDCM无模型自适应控制

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。

Model free adaptive control of BLDCM based on improved sliding mode observer

To solve the chattering and phase delay problems existing in conventional sliding mode observer (SMO) and the problem that the dynamic performance of the traditional PI control was not efficient, a novel switching function was proposed to improve the conventional SMO, which was realized by the idea of double boundary layer. The improved SMO was used into the brushless DC motor (BLDCM) direct torque control system, and achieved BLDCM sensorless control. Then, model free adaptive control (MFAC) based on linearization of entire format was applied into the design of BLDCM speed controller. The simulation results demonstrate that the back electromotive force (back EMF) observation error of the improved SMO is 4% less than that of the traditional SMO, and the dynamic performance of MFAC is better than that of the traditional PI control. The improved SMO could precisely estimate the back EMF of BLDCM, effectively weaken the chattering without the additional low pass filter, and simplify the structure of the system. MFAC could improve the robustness, the stability and the rapidity of the system.