外文翻譯---具有積分滑?？刂频膬?nèi)埋式永磁同步電動(dòng)機(jī)基于線性矩陣不等式的模糊控制-全文預(yù)覽
【正文】 of semiconductor, IPMSM supplied by converter source has been widely studied [1] [2]. The development of microputer made the vector control system of IPMSM well controlled by single chip. IPMSM possesses special features for adjustablespeed drives which distinguish it from other classes of ac machines, especially surface permanent mag synchronous motor. The main criteria of high performance drives are fast and accurate speed response, quick recovery of speed from any disturbances and insensitivity to parameter variations [3]. In order to achieve high performances, the vector control of IPMSM drive is employed [4][6]. Control techniques bee plicated due to the nonlinearities of the developed torque for nonzero value of daxis current. Many researchers have focused their attention on forcing the daxis current equals to zero in the vector control of IPMSM drive, which essentially makes the motor model linear [4],[7]. However, in realtime the electromagic torque is nonlinear in nature. In order to incorporate the nonlinearity in a practical IPMSM drive, a control technique known as maximum torque per ampere (MTPA) is devised which provides maximum torque with minimum stator current [3]. This MTPA strategy is very important from the limitation of IPMSM and inverter rating points of view, which optimizes the drive efficiency. The problem associated MTPA control technique is that its implementation in real time bees plicated because there exists a plex relationship between daxis and qaxis currents. Thus, one of the main objectives of this paper is to make a new efficient control method for IPMSM and its calculation easy and efficient. The LMI fuzzy H??control has been applied and solved the nonlinearity of the IPMSM model to a set of linear model. To increase the robustness for disturbances, an ISMC technique is added to the H??controller. By ISMC, the proposed controller gives performances of the H??control system without disturbances which satisfy the matching condition. It has a good patible with linear controllers. TS fuzzy control [8] is based on the mathematical model which is the bination of local linear models depending on the operating points. Linear controllers are designed for each linear model and they are bined as a controller and make it possible to use linear control theories for nonlinear systems. Linear controls via parallel distributed pensation (PDC) and linear matrix inequality (LMI) is a most popular method considering the stability of the system with PDC [9]. H??LMI TS fuzzy controller is considered as a practical H??controller which eliminates the effects of external disturbance below a prescribed level, so that a desired H??control performance can be guaranteed [1012]. In this paper, the robustness of SMC [13] is added to the H??LMI TS fuzzy controller for the control of IPMSM. We can divide the disturbances in the IPMSM into two parts. First part is that SMC can deal with and other part is dealt by H??LMI fuzzy controller. By using ISMC, the robustness of SMC and H??performance can be bined. Integral sliding mode control (ISMC) is a kind of SMC which has sliding mode dynamics with the same order of the controlled system and can have the properties of the other control method. II. H??TS FUZZY CONTROL AND ISMC A. H??TS fuzzy control Consider a nonlinear system as follows. x(t)?f (x)?g(x)u(t)?w(t) (1) where ||w(t)| ≤ Wb and Wb is the boundary of disturbance. Depending on the operating points, the nonlinear system can be expressed as follows. The ith model is that in the case z1(t) is Mi1 and … and z p(t) is Mip , (2) And H??TS fuzzy feedback controller is ui???kiX(t) (3) where i=1,2, … ,r and Mij is the fuzzy set and r is the number of model rules Given a pair of (x(t),u(t)), the fuzzy systems are inferred as follows: where and μi(z(t)) is the membership for every fuzzy rule. From (1) we get (7) Take (6) into (7), we can get the closed loop system equations. If we set A?present the error boundary of every rule and satisfy the following condition: In the same way we get: (9) Based on these, the approximat
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