外文翻译---具有积分滑模控制的内埋式永磁同步电动机基于线性矩阵不等式的模糊控制(编辑修改稿)内容摘要:
rall scheme of the HLMI TS fuzzy control system is as follows. HLMI TS fuzzy based ISMC controller designed as following steps. . utilize the equilibrium point to calculate the error system. System (22) can be presented by state form as: ( 23) where x1(t) iq , x2(t) id , x3(t) wr ,u10(t) vq and u20(t) vd . Based on (23), a reference system can be given as: ( 24) where f means the required value. Then the following error dynamic system is derived. ( 25) where etxtxf t. determine for membership function. For x1 minimum case: For x1 maximum case: For x2 minimum case: For x2 maximum case: The fuzzy rules are as the follows: x1 is minimal and x2 is minimal: M1(t) E1(t)G1(t) ( 26) x1 is minimal and x2 is maximal: M2(t) E1(t)G2(t) (27) x1 is maximal and x2 is minimal: M3(t) E2(t)G1(t) (28) x1 is maximal and x2 is maximal: M4(t) E2(t)G2(t) (29) . obtain the matrixes A and B. Equation (25) can be of the following form: and the value of ( x1lim , x2lim ) is based on the rule1 to rule 4, it gets to be x1min,x1max,x2min and x2max . . calculate controller parameters K using LMI toolbox based on Theorem 1. By LMI, the error systemcontrol input is defined by (6) as ( 31) where k j is a 1by 3 matrix. Use inequality (13) and Matlab LMI toolbox to calculate out the parameters k j . So that, HTS fuzzy controller of the system is where u1 f and u2 f are reference inputs. . Design ISMC for system. Based on the SMC matching condition the system with disturbance is as follows: ( 32) where d(t) is the noise or disturbance. The sliding surface is defined as: ( 33) x1r and x2r are required output values, x1n and x2n are states of nominal system: ( 34) Assume u1(t)u10(t)u1s(t) and u2(t)u20(t)u2s(t) .Derivate of sliding surfaces are: ( 35) where e1n(t)x1(t)x1n(t) , e2n(t)x2(t)x2n(t) , un(t) is the nominal control input and us1 and us2 are sliding control inputs. The sliding controller finally is given out as: ( 36) where d1max and d1max are the maximal absolute values of disturbance. IV. SIMULATION RESULTS Use the controller design process in above sections with the parameters of . Simulation results are: . IPMSM PARAMETERS. . result of iq with parameter uncertainty and disturbance. of id with parameter uncertainty and disturbance. Required output values are From the result of and , we can see that some kind of disturbance can not be solved only by HLMI TS fuzzy. Combination with ISMC solves this perfectively. V. CONCLUSIONS The Fuzzy LMI controller is used for IPMSM. It uses the linear models for each operating points. It is shown that only four operating points are enough for the proposed control method. The controller of this paper gives good control performance with only four membership functions which are determined easily. Hfuzzy LMI solved the initial big input for IPMSM from ISMC, while ISMC solved the problem of Hfuzzy which is so dependent on fuzzy rules. The final results show that the bination control is efficient and perfect. 具有积分滑模控制的 内埋式永磁同步电动机 基于线性矩阵不等式的模糊控制 王发光 , Seung Kyu Park, Ho Kyun Ahn 韩国昌原国立大学电机工程学系 近期摘要, 内埋式永磁同步电动机被广泛的用于各种各样的应用中,例如电动汽车和压缩机。 它对宽负荷变化、高速度条件、稳定性、快速的反应有较高的要求,最重要的是它使用简便,效率高。 但是对内埋式永磁同步电动机的控制要比表面式永磁同步电动机困难,这是由于它的非零 d轴电流产生的非线性而造成的,而 d轴电流在表面式永磁同步电动机中是可以为零的。 在本论文中,内埋式永磁同步电动机通过运用有以确定工作点为基础的线性模型的线性控制和模糊控制的结合而变得非常有效率。 线性矩阵不等式( LMI)的积分 滑模控制也能够用于确保鲁棒性,论文中的隶属函数可以很容易的确定和实施。 索引条目 —— 模糊控制、 控制、积分滑模控制、内埋式永磁同步电动机(IPMSM)、线性矩阵不等式。 I. 导论 从 20世纪八十年代开始,伴随着半导体技术的发展,由转换器电源供应的内埋式永磁同步电动机已经被广泛的研究。 微机技术的发展使得单芯片控制的内埋式永磁同步电动机的矢量控制系统相当的熟练,内埋式永磁同步电动机对于调速驱动器所具有的特别的特征使它区别于别的种类的交流电机,特别是表面式永磁同步电动机。 高性能驱动器的主要标准是快速、精确的速度响应、从任何干扰速度的快速恢复和对参数变化的不敏感性。 为了达到高性能,采用了内埋式永磁同步电动机的矢量控制。 由于 d轴电流的非零值而造成的超前转矩的非线性使得对内埋式永磁同步电动机的控制技术 变得复杂。 在内埋式永磁同步电动机的矢量控制方面很多研究者把重点放在强迫 d轴电流接近于零,这本质上是使点击模型线性化。 但是,本质上在实时中它的电磁转矩是非线性的。 为了在一个现实的内埋式永磁同步电动机中纳入非线性,一种叫做每安培最大转矩 (MTPA)的控制技术被设计出来,它能够用最小的定子电流提供最大的转矩。 这种策略从内埋式永磁同步电动机和逆变器等分级的限制的观点来看是非常的重要,它能够优化驱动器的效率。 相关 MTPA控制技术的困难是由于 d轴和 q轴电流存在复杂的关系使得在实时执行时变得复杂。 因此,本论文的一个主要目 的是为内埋式永磁同步电动机作出一种高效的控制方法,而且它的计算需简单、高效。 线性矩阵不等式模糊。外文翻译---具有积分滑模控制的内埋式永磁同步电动机基于线性矩阵不等式的模糊控制(编辑修改稿)
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