永磁同步伺服电机的变结构智能控制

第 22 卷第 7 期 2002 年 7 月 文章编号 0258-8013 2002 07-0013-05
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国 电 机 工 程 学 Proceedings of the CSEE
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Vol.22 No.7 Jul.2002 © 2002 Chin. Soc. for Elec.Eng.
永磁同步伺服电机的变结构智能控制
Yd +⊗ _ Y(k) VSC U(k) • B + +
in which is the diagonal matrix, and the diagonal matrix element i > 0. T is the sampling period. Implementing the above controlling strategy is based on the measurability of the state parameter X, whereas not all the state parameters in most real controlling systems can be directly measured. Thus, the state observer for system (1) is obtaariable structure control (VSC) falls into
two types: the continuous system and the discrete time system. Various control algorithms have been proposed in the study of VSC of a discrete time
ˆ(k + 1) = AX ˆ(k ) + BU ˆ(k ) + L[Y (k ) − Y ˆ(k )] X ˆ ˆ Y (k ) = CX (k ) S ˆ ˆ (k ) = KX (k )
(3)
⊗
X(k+1)
Z-1 A
•
C
• + Y(k)
Z
Y(k+1)
Its controlling strategy is
张昌凡 １ 王耀南 ２， 何 静 １
1.株洲工学院电气工程系 湖南 株洲 412008; 2. 湖南大学电气与信息工程学院 湖南 长沙 410082
VARIABLE STRUCTURE INTELLIGENT CONTROL FOR PM SYNCHRONOUS SERVO MOTOR DRIVE
the observation error e is comparatively high when the observer starts functioning. From the sliding mode condition, the system designed on the basis of equation (4) is movable only within the switch zone with a bandwidth of T, but can not converge to zero [6]. As a result, the increase of the value of may lead to stronger chattering and bigger static error. A neural network is, therefore, introduced to check the inaccuracy of measurement and to adjust on, so as to reduce the chattering of line the value of the system. The structure of the whole system is illustrated in Fig.1
where X(k) Rn U(k) Rm Y (k) Rl (C, A) is observable, Y(k) is the systemic output and observable. Apply the VSC strategy to it:
万方数据
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国 电
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第 22 卷
Define the switching function S=KX and assume that KB is non-singular, thus the VSC signal is obtained by using the discrete reaching law [4], which is described as follows U(k) (KB)-1[ K(I A)X(k) T sgnS(k)] (2)
ˆ (k) , X
~ X(k) NNM • • Z-1
∆X(k+1)
~ Y(k+ 1) ~ + X(k+1) • C +⊗
e(k+1) (A LC)e(k) (5) ˆ ˆ ˆ ˆ Where X , Y , S and U denote the observer’s states associated with the states X, Y , S and U. Since (A, C) is observable and measurable, there must exist a L matrix which restricts all the eigenvalues of (A LC) in a unit circle, and thus makes equation(5) converge to zero and be free of connection with the system, with the initial value of ˆ(k ) . the observer, as well as with U in equation (4) should satisfy the entry condition of a sliding mode. ˆ(k + 1) = KX ˆ(k + 1) S The choice of
2
DESCRIPTION OF THE SYSTEM
Consider the following discrete time system
X (k + 1) = AX (k ) + BU (k ) Y (k ) = CX (k )
(1)
基金项目 国家 ８６３ 高技术基金项目 ８６３－５１１－９８４５－００２ ； 湖南省自然科学基金项目 ０１ＪｊＹ３０２９ Project Supported by National 863 Foundation of China 863-5119845-002 ; Hunan Natural Science Foundation of China(01Jjy3029).
system
[1~3]
. Researches show that, in a discrete
system,the fewer the switching times of VSC output, the less the chattering and the higher the degree of accuracy. However, the VSC of a discrete system so far proposed is based on the supposed knowledge of the systemic state, while the state of a system, in general, is only partially known. And the controllers are designed only on the basis of the output or the estimated state of the system. Therefore, it is undoubtedly significant to study how to realize the VSC of a discrete time system. This paper designs a linear observer for the system certainty, and supplements it with a dynamic neural network to compensate its uncertainty. An online neural network parameter regulator is also designed to guarantee a higher degree of observation comprehensiveness and a lower degree of chattering. The practical operating results in PMSM illustrate the effectiveness and practicability of the proposed approach.
ABSTRACT The variable structure control (VSC) of discrete time systems based on intelligent control is presented in this paper. A novel approach is proposed for the state estimation. A linear observer is firstly designed. Then a neural network is used for compensating uncertainty. The parameter of VSC is adjusted on line by a neural network. The practical operating results in permanent magnet synchronous motor (PMSM) illustrate the effectiveness and practicability of the proposed approach. KEY WORDS variable structure control observer networks 摘 要 intelligent control position control 提出了一种基于智能控制的离散变结构系统 该 neural