摘要
模型预测控制(MPC)是一种有效的控制方法,但由于其计算量较大,难以在永磁同步电机(PMSM)控制中应用。为了研究MPC在PMSM中的应用方法,提出了一种基于自动微分(AD)理论的永磁同步电机电流环模型预测控制算法。采用AD理论,对PMSM的数学模型在d-q坐标系下进行泰勒级数展开,建立了比传统查表或差分法更为精准的数学模型。通过计算PMSM泰勒级数灵敏度,将预测控制问题转化为无约束条件下的二阶最优问题,实现了模型预测控制。仿真结果表明,利用AD理论的PMSM电流环预测控制算法可以将系统状态量精确控制在允许范围内,并且减小了每步的平均运算时间。
Model predictive control is an effective control method. However, the heavy computational bur den prevents its application in permanent magnet synchronous motor （PMSM） control. To solve the prob lem an automatic differentiation （AD） based model predictive control （MPC） algorithm for PMSM was proposed in this paper. A PMSM model in dq axis was established by Taylor series expansion using AD. The new model was more accurate than the conventional one obtained from difference or looking up table. MPC was completed by computing the sensitivity of PMSM Taylor series and converting predictive control problem into unconstrained optimization problem. Simulation results verify that the proposed algorithm re duces the computational time significantly.
出处
《电机与控制学报》
EI
CSCD
北大核心
2012年第10期38-43,共6页
Electric Machines and Control
基金
浙江省自然科学基金(LY12F03021)
宁波市自然科学基金(2011A610128)
关键词
永磁同步电机
模型预测控制
自动微分
算法
泰勒级数
permanent magnet synchronous motors
model predictive control
automatic differentiation
algorithms
Taylor series
