Volume II, Issue VII, July 2013 IJLTEMAS ISSN 2278 - 2540 www.ijltemas.in Page 119 Adaptive Fuzzy Sliding Mode Controller for Indirect Vector Control of Induction Motor Drive Barkha Rajpurohit, Arti Gosain Anil Kumar Chaudhary Department of Electrical Engineering Assistant Professor Mandsaur Institute of Technology Dept. of Electrical Engineering Madhya Pradesh, India Mandsaur Institute of Technology [email protected][email protected]Abstract— In this paper a fuzzy sliding mode control is proposed for speed control of indirect field-oriented induction motor drive. First a indirect field-oriented control introduced briefly. Then a sliding mode control is investigated. The proposed control design uses a fuzzy logic technique for implementing a fuzzy hitting control law to remove completely the chattering phenomenon on a conventional sliding mode control. Here to adjust the fuzzy parameter for further assuring robust and optimal control performance, an adaptive algorithm which is derived in the sense of Lyapunov stability theorem is utilized. The proposed fuzzy sliding-mode controller is compared with sliding mode controller with external load perturbation using periodic speed command. The simulation results shows that fuzzy sliding mode controller is robust for tracking the periodic command free from chattering. Keywords-Indirect vector control;sliding mode controlg;fuzzy sliding mode control; speed control;induction motror I. INTRODUCTION Sliding mode controller (SMC) is one of the effective ways for controlling electric drive system. It is a robust control because the high-gain feedback control input cancels non- linearities, parameter uncertainties and external disturbance. It also offers a fast dynamic response and a stable control system [4].The first step of SMC design to select a sliding surface that models the desired closed-loop performance in state variable space. In the second step, design a hitting control law such that the system state trajectories are forced toward the sliding surface and stay on it. The system state trajectory in the period of time before reaching the sliding surface is called the reaching phase. Once the system trajectory reaches the sliding surface, it stays on it and slides along it to the origin. The system trajectory sliding along the sliding surface to the origin is the sliding mode. However this control strategy produces some drawbacks associated with large control chattering that may wear coupled mechanisms and excite unstable system dynamics. Though introducing a boundary layer may reduce the chatter amplitude, the stability inside the boundary layer cannot be guaranteed and poor selection of boundary layer will result in unstable tracking responses[2].In order to remedy this phenomenon an fuzzy sliding mode control is introduced in which a fuzzy hitting control law is embedded into SMC system to the sliding surface and an adaptive algorithm derived in the sense of the Lyapunov stability theorem is utilized to adjust the fuzzy parameter. This method can leads to stable close loop system with avoiding chattering problem. This paper presents a adaptive fuzzy sliding-mode control scheme (AFSMC).A indirect vector control is reported in section-II.A fuzzy sliding-mode control is discussed in section- III.Test results are discussed in Section-IV and finally some concluding remarks are stated in section-V. II. INDIRECT FIELD-ORIENTED INDUCTION MOTOR DRIVE The block diagram of an indirect field-oriented induction motor drive is shown in fig.1.Here the induction motor is fed by a hysteresis current controlled pulse width modulated (PWM) inverter. 1 Lm L m R r ψ r R r Fig.1.Indirect vector controlled Induction Motor Dive The torque component of current i qs * is generated by speed error with the help of PI or any intelligent controller. The flux
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Volume II, Issue VII, July 2013 IJLTEMAS ISSN 2278 - 2540
www.ijltemas.in Page 119
Adaptive Fuzzy Sliding Mode Controller for Indirect
Vector Control of Induction Motor Drive
Barkha Rajpurohit, Arti Gosain Anil Kumar Chaudhary
Department of Electrical Engineering Assistant Professor
Mandsaur Institute of Technology Dept. of Electrical Engineering
Madhya Pradesh, India Mandsaur Institute of Technology
disturbance,unmodelled and nonlinear dynamics adversely
affect the control performance of the drive system. Therefore
the control effort cannot ensure the favourable control
performance. Thus auxiliary control effort should be designed to
eliminate the effect of the unappreciable disturbances. The
auxiliary control effort is referred to as hitting control effort as
follows
(a) (b)
Fig.3.Membership function (a) Input fuzzy sets for S. (b)
Output fuzzy sets for Uf
Then a fuzzy hitting control law can be estimated by fuzzy
logic inference mechanism as follows:
U h (t ) = gh sgn(S (t )) (16)
Where 0 ≤ ω1 ≤1,0 ≤ω2 ≤1,and 0 ≤ω3 ≤1 are the firings
strengths of rules 1,2, and 3; respectively(r1=r),(r1=0)
and(r3=0) are the centre of total membership functions Where gh is a hitting control gain concerned with upper bound of uncertainties, and sgn(.) is a sign function.
Now, totlly sliding mode control law as follows
PE,ZE,and NE respectively, r is a fuzzy parameter. The
relation ω1+ω2+ω3=1 is valid according to the special case of
triangular membership functions. Moreover, the fuzzy hitting U SMC (t ) = U eq (t ) + U h (t ) (17) control effort Uf can be further analysed as the following four
conditions and only four conditions will occur for any value
But this controller gives unacceptable performance due to high
control activity, resulting in chattering of control variable and
system states. To reduce chattering a boundary layer in
generally introduced into SMC law, then the control law of
of S according to fig.3(a)
Condition 1: When rule 1 is triggered (S > Sa ; ω1=1; ω2=
ω3= 0)
equation (17) can be rewritten as U f (t) = r (20)
U h (t ) =
gh S (t )
S (t ) + γ
(18)
Condition 2: When rules 1 and 2 are triggered
simultaneously.(0 <S ≤ Sa;0 < ω1 , ω2 ≤1; ω3= 0)
ω2; ω3<1)
U f (t) = r3ω3 = −rω3 (22)
Condition 4: when rule 3 is triggered (S≤Sb; ω1= ω2=0; ω3=1)
Volume II, Issue VII, July 2013 IJLTEMAS ISSN 2278 - 2540
www.ijltemas.in Page 122
L ( t ) ( ω1 − ω2 B p n )
1 3
U (t) = U (t) + U (t) = U (t) + r(t )(ω − ω )
Choose a Lyapunov candidate function as
2 2
U (t) = −r
(23) s(t) + αBpn r%
(t)
f V (S(t), r%(t)) =
U f (t) = r3ω3 = −rω3 (21)
rewritten as
Condition 3: when rules 2 and 3 are triggered simultaneously ˆ
AFSMC eq ˆ
f eq ˆ
1 3
(29)
(Sb < S ≤ 0; ω1= 0;0 ≤
(30)
From all four possible conditions, it can be seen that b 2
S(t)( ω1- ω3) = S(t) (ω1- ω3) ≥0
Now, total fuzzy sliding mode control (FSMC) law can be
represented as
Where α is a positive constant. Take the derivative of
Vb (S(t), r%(t)) with respect to time, and using (14) and (29)
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