Speed and Flux Control of Induction Motors Using ... - IEEE Xplore

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Speed and Flux Control of Induction Motors Using Emotional Intelligent Controller G. R. Arab Markadeh, Ehsan Daryabeigi, Caro Lucas, and M. Azizur Rahman, Life Fellow, IEEE

Abstract—This paper presents a real-time implementation of an improved emotional controller for induction motor (IM) drives. The proposed controller is called brain-emotional-learning-based intelligent controller. The utilization of the new controller is based on the emotion-processing mechanism in the brain and is essentially an action selection, which is based on sensory inputs and emotional cues. This intelligent control is based on the limbic system of the mammalian brain. The controller is successfully implemented in real time using a PC-based three-phase 2.5-kW laboratory squirrel-cage IM. In this paper, a novel but simple model of the IM drive system is achieved by using the intelligent controller, which simultaneously controls the motor flux and speed. This emotional intelligent controller has a simple computational structure with high auto learning features. The proposed emotional controller has been experimentally implemented in a laboratory IM drive, and it shows good promise for niche industrial-scale utilization. Index Terms—Emotional learning, induction motor (IM), intelligent controller, medial brain, speed and flux control.

I. I NTRODUCTION

I

NDUCTION MOTORS (IMs) have emerged as the industrial workhorse of the modern society. These motors have been employed since more than 100 years ago because of their cheap price, robust structure, simple structure, low maintenance cost, high output, good reliability, high outputpower-to-volume ratio, and low operating noise. In spite of the many advantages, the control of the IM drive system is challenging due to slip and associated power losses [1], [2]. The advent of indirect vector control technique has made it possible to obtain good dynamic responses, as in a separate dc motor drive [3]. Since any vector control scheme cannot maintain a decoupling characteristic between torque and flux when the reference flux is changed such as in the flux weakening region, a number of methods have been developed to carry out alternative techniques for the control of the IM drive [1], [4]. Unlike in the

Manuscript received September 30, 2009; revised April 25, 2010; accepted November 5, 2010. Date of publication March 10, 2011; date of current version May 18, 2011. Paper 2009-IACC-308.R1, presented at the 2009 IEEE International Electric Machines and Drives Conference, Miami, FL, May 3–6 and approved for publication in the IEEE T RANSACTIONS ON I NDUSTRY A PPLICATIONS by the Industrial Automation and Control Committee of the IEEE Industry Applications Society. G. R. Arab Markadeh is with Shahrekord University, Shahrekord 881863414, Iran (e-mail: [email protected]; [email protected]). E. Daryabeigi is with Islamic Azad University, Najafabad branch, Esfahan, Iran (e-mail: [email protected]). C. Lucas, deceased, was with the University of Tehran, Tehran, Iran (e-mail: [email protected]). M. A. Rahman is with Memorial University of Newfoundland, St. John’s, NL A1B 1T2, Canada (e-mail: [email protected]). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TIA.2011.2125710

vector control scheme, the direct torque control (DTC) method does not require any current regulator, coordinate transformation, and conventional PWM inverter voltage. In addition to simplicity, the DTC of the IM allows a good torque control in steady-state and transient operating conditions. However, high torque pulsation is produced, which is reflected in speed estimation responses. It also increases acoustical noise [5], [6]. The DTC method presents some other disadvantages, such as the following: 1) difficulty to control torque and flux at very low speed; 2) relatively high noise level at low speed; and 3) lack of direct current control. For these reasons, intelligent methods are used to solve the problems of IM control for high-performance applications [7]–[23]. Artificial intelligence (AI) techniques, such as expert systems (ESs), fuzzy logic (FL), neural networks (NNs), or biologically inspired (BI) genetic algorithm (GA), have recently been applied in motor drives. The aim of AI is to model human or natural intelligence in a computer so that a computer can think intelligently like a human being. A system with embedded computational intelligence is often defined as an intelligent controller that has learning, self-organizing, or self-adapting capability. Computational intelligence has been progressively utilized to solve any usual and complex control problems [7]. Therefore, it is true that AI techniques are now being widely used in industrial process control, image processing, medicine, space and diagnostic technology, etc. While ES and FL techniques are rule based [8], [9] and tend to mimic the behavioral nature of the human brain, the NN is more generic in nature, which tends to pattern the biological NN directly [7]. The BI system is inspired by the biological disposition of animals and mimics biomechanisms [10]. From the beginning of the 1990s, the NN technology attracted the attention of a large part of the scientific community. Since then, the technology has been advancing rapidly, and its applications are expanding in different areas [9], [11]–[16]. The GAs as well as the evolutionary computation techniques are based on principles of genetics [17]. Basically, these GA methods solve optimization problems by a search process resulting in best (fittest) solutions (survivor). Among all the subbranches of AI, the NN and FL seem to have maximum uses in the high-performance motordrive area, which is evident in the numerous publications in the literature. According to Bose [7], there are many other feedforward and recurrent NN topologies, which require systematic exploration for their applications. Moreover, powerful intelligent control and estimation techniques can be developed using hybrid AI systems such as neuro–fuzzy, neuro–genetic, and neuro–fuzzy–genetic systems, etc.

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ARAB MARKADEH et al.: SPEED AND FLUX CONTROL OF IMs USING EMOTIONAL INTELLIGENT CONTROLLER

The sliding-mode-control method is used for speed control in electric motor drives. In [18] and [19], the comparison between fuzzy–NN and sliding-mode-control methods was presented. It was claimed that both methods ensure good characteristics, yet the fuzzy–NN hybrid requires less control effort. All these AI optimization methods increase their complexity and thus require fast computer processing burden for practical application of IM drives. Despite the versatility of bio-inspired and intelligent systems, many practical applications require large computational power to overcome the complexity and real-time constraints of these systems. In addition, dedicated systems are needed in many industrial applications to meet lower power and space requirements [7]. According to the review in [25], several attempts have been made to model the emotional behavior of the human brain [26], [27]. In [27], the computational models of the amygdala and context processing were introduced, which were named brainemotional-learning (BEL) model, which was not used in any practical area, particularly in engineering applications. Based on the cognitively motivated open-loop model, the BEL-based intelligent controller (BELBIC) was introduced for the first time by Lucas in 2004 [28], and during the past few years, this controller has been used in control devices for several industrial applications [29]–[36]. In [29], a modified version of the emotional controller was used in heating, ventilating, and airconditioning control problems that are multivariable, nonlinear, and nonminimum in type. In [30], this controller was applied to control the dynamics of an electrically heated micro heatexchanger plant, which acts as a nonlinear plant. The BELBIC was utilized for controlling an identified washing machine [31], and after that, this controller with multiple objectives and constraints was tuned for washing machines with evolutionary algorithms, in which the designer can trade off between energy consumption and other control objectives [32]. In [33], this controller was applied to an automotive suspension control system. Also, the BELBIC is applied to the position tracking and swing damping control of an overhead crane [34]. In [35], a BELBIC was designed and implemented on field-programmable gate arrays (FPGAs) for controlling a mobile crane in a model-free and embedded manner. The main features of that controller were its enhanced learning capability, provision of a model-free control algorithm, robustness, and ability to respond swiftly. For the first time, the implementation of the BELBIC method for electrical drive control was presented by Rahman et al. [36]. The speed control of a highly nonlinear interior permanentmagnet synchronous motor using the BELBIC method was compared with that of a proportional–integral–derivative (PID) controller [36]. The results show superior control characteristics, particularly very fast response, simple implementation, and robustness with respect to disturbances and parameter variations. Based on the aforementioned evidences of the emotional control approaches in computer-based control engineering, it can be said that the application of emotion in systems could easily overcome the problems of nonlinear control systems. The objective of this paper is to use a modified emotional controller for the simultaneous speed and flux control of a laboratory IM drive. This simultaneous speed and flux control is

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achieved by quick auto learning and adaptively proper tracking of reference speed and is quite independent of system parameters, which results in performance improvement. II. IM M ODEL The mathematical model of a squirrel-cage IM drive can be described by the following in a stationary d–q reference frame as [37]: ⎡ ⎤ ⎤ ⎡ vqs 0 Lm p 0 Rs + Ls p 0 Rs + Ls p 0 Lm p ⎥ ⎢ vds ⎥ ⎢ ⎣ ⎦ ⎦=⎣ vqr −ωr Lm Rr + Lr p −ωr Lr Lm p vdr ωr Lm Lm p ωr Lr Rr + Lr P ⎡ ⎤ iqs ⎢i ⎥ × ⎣ ds ⎦ (1) iqr idr ⎤ ⎡ ϕqs   1 1 0 − Xls 0 iqs ⎢ϕ ⎥ = Xls · ⎣ ds ⎦ (2) 1 ids ϕmq 0 0 − X1ls Xls ϕmd ⎤ ⎡ ϕqr   1 1 0 − Xlr 0 iqr ⎢ϕ ⎥ = Xlr · ⎣ dr ⎦ (3) 1 1 idr ϕmq 0 0 − Xlr Xlr ϕmd   Xm (iqs + iqr ) ϕmq = (4) ϕmd Xm (ids + idr ) 3P (iqs λds − ids λqs ) (5) Te = 22 dω J = Te − Tl − B · ω (6) dt where the d–q axis variables (vds , vqs ), (ids , iqs ), and (φds , φqs ) are the stator voltage, stator current, and stator flux components, respectively. The d–q axis variables (vdr , vqr ), (idr , iqr ), and (ϕdr , ϕqr ) are the rotor voltage, rotor current, and rotor flux, respectively. ωr is the angular speed of the IM, and P represents the pole numbers. Rs , Rr , Ls , Lr , and Lm are the stator and rotor resistances and the stator self-, rotor self-, and mutual inductances, respectively. Also, Xs , Xr , and Xm are the stator self-, rotor self-, and mutual impedances, respectively. Finally, Te , Tl , B, and J are the electromagnetic and load torques, friction coefficient, and moment of inertia, respectively. III. C OMPUTATIONAL M ODEL OF BELBIC S YSTEM Motivated by the success in the functional modeling of emotions in control engineering applications [25]–[36], the main purpose of this paper is to use a structural model based on the limbic system of the mammalian brain and its learning process for the control of an IM drive system. The network connection structure of the mammalian brain developed by Moren and Balkenius [26], [27] is utilized in this paper as a computational model that mimics the amygdala, orbitofrontal cortex, thalamus, sensory input cortex, and, generally, those parts of the brain thought to be responsible for processing emotions. Fig. 1 shows the pertinent pictures of the human brain. Fig. 2 shows a graphical depiction of the modified sensory signal and learning network connection model inside the brain. The

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Fig. 2. Graphical depiction of the developed network model of the BEL process (BELBIC).

Fig. 1. Sectional view of the human brain for emotion processing.

neurobiological aspects of the amygdala, orbitofrontal cortex, thalamus, hippocampus, and associated areas are relevant for the functional and computational perspectives of the emotional responses. The small almond-shaped subcortical area of the amygdala in Fig. 1 is well placed to receive stimuli from all sensory cortices and other sensory areas of the hippocampus in the brain [34]. There are two approaches to intelligent and cognitive control, namely, direct and indirect approaches. In the indirect approach, the intelligent system is utilized for tuning the parameters of the controller. One can adopt the direct approach via using the computational model as a feedback control system for the speed control of an IM. The intelligent computational model termed BELBIC is used as the controller block [28]. For the sake of simplicity, the BELBIC is called emotional controller in this paper. The model of the proposed BELBIC input and output structure is shown in Fig. 2. The BELBIC technique is essentially an action-generation mechanism based on sensory inputs and emotional cues. In an IM drive, the choice of the sensory inputs (feedback signals) is selected for control judgment, whereas the choice of the emotional cues depends on the performance objectives in IM drive applications. In general, these are vector-valued quantities. For the sake of illustration, one sensory input and one emotional signal (stress) have been considered in this paper. The emotional learning occurs mainly in the amygdala. It has been suggested that the relation between a stimulus and its

Fig. 3. Structure of the computational model mimicking some parts of the mammalian brain.

emotional consequences takes place in the amygdala part of the brain [38]. The amygdala is a part of the brain that must be responsible for processing emotions and must correspond with the orbitofrontal cortex, thalamus, and sensory input cortex in the network model. The amygdala and the orbitofrontal cortex have a networklike structure, and within the computational model of each of them, there is one connection in lieu of each sensory input. Also, there is another connection for thalamus input within the amygdala. The value of this input is equal to the maximum value of the sensory inputs. The equivalent network connection in Fig. 2 is described by the control structure of human brain in the following Fig. 3. There is one A node for every stimulus S, including one for the thalamic stimulus. There is also one O node for each of the stimuli, except for the thalamic node. There is one output node E that is common for all the outputs of the model. The E node simply sums the outputs from the A nodes and then subtracts the inhibitory outputs from the O nodes.

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The result is the output of the closed-loop model. In other words, the output E of the emotional controller can be obtained from the following:



E= Aj + Ath − Oj . (7) j

j

The internal area outputs are computed pursuant to Ath = Vth · {max(Sj ) = Sth }

(8)

Aj = Sj Vj

(9)

Oj = Sj Wj

(10)

Scj = Sj ⊗ [e−k·t ]

(11)

where Aj and Oj are the values of amygdala output and the output of the orbitofrontal cortex at each time, Vj is the gain in the amygdala connection, Wj is the gain in the orbitofrontal connection, Sj and SCj are sensory and sensory-cortex outputs, respectively, and j is the jth input. Variations of Vj and Wj can be calculated as   

ΔVi = α max 0, Sci R − (12) Ai i

ΔVth = αth (max (0, Sth (R − Ath ))) .

(13)

Moreover, likewise, the E  node sums the outputs from A except Ath and then subtracts from inhibitory outputs from the O nodes



E = Aj − Oj (14) j

j

ΔWi = β (Sci (E  − R))

(15)

where (α, αth ) and β are the learning steps in the amygdala and orbitofrontal cortex, respectively. R is the value of the emotional cue function at each time. The learning rule of the amygdala is given in (13), which cannot decrease. It means that it does not forget the information in the amygdala, whereas idiomatically inhibiting (forgetting) is the duty of the orbitofrontal cortex (12). Eventually, the model output is obtained from (7). Fig. 4 shows the BELBIC controller configuration. The used functions in the emotional cue R and sensory input S blocks can be given by the following: R = f (E, e, y, yd )

(16)

S = g(y, yd , e).

(17)

In this paper, functions f and g are given by

d g = k1 e + k2 e + k3 e · dt dt f = K1 |e| + K2 |e · y| + K3 |yp |

(18) (19)

where e, yp , and y are the system error, controller output, and system output, respectively. Also, k1 and K1 , k2 and K2 , as well as k3 and K3 are gains like in the PID controller, which

Fig. 4. Control system configuration using BELBIC.

must be tuned for designing a satisfactory controller given in the Appendix. Eventually, initial values for α and β in O and A and functions R and S should be selected for emotional signal generation [28]. In this paper, the proposed controller is modified by separating the learning process of the thalamic stimulus from the sensory cortex stimuli in the amygdala (12), (13). A simple lowpass filter is used for modeling the thalamus. The neurophysiological speed response in the sensory cortex is faster than that in the thalamus [26], [27].

IV. IM-D RIVE C ONTROL S YSTEM D ESIGN The block diagram of the new control system incorporating the emotional controller (BELBIC) for the IM drive is shown in Fig. 5. The emotional control system receives the error signals between the command speed and rotor flux and the actual motor speed and flux linkage as part of the inputs according to (12)–(16). In addition, it generates the output signals following (7)–(11). In the control system in Fig. 5, two emotional intelligent controllers (BELBIC) are used, such that the first (BELBIC1) receives motor speed error as input and then ∗ generates vqe as output and the second (BELBIC2) receives ∗ motor flux error as input and then generates vde as output directly. All of these are in the rotating reference frame oriented to the rotor flux, and these are transferred to the stationary ∗ ∗ reference frame and generate vds and vqs . In order to generate the logic signals to control the inverter switches, the spacevector pulsewidth modulation (SV-PWM) technique is used. According to the aforementioned control procedure, it is evident that proper control is achieved without rigorous requirements to other conventional controllers (PI, PID controllers, etc.) in generating command voltages and quite independent of motor parameters. Unlike in conventional PI controllers, the proposed emotional control technique is auto learning and model free, and the controller coefficients are adaptive, which facilitates the vector control of the IM drive to be controlled independent of parameter variations.

V. R EAL -T IME E XPERIMENTATION The proposed emotional controller (BELBIC) for the IM drive is experimentally implemented using a prototype PC-based setup. The experimental setup system shown in Fig. 6 consists of the following elements: a 2.5-kW three-phase squirrel-cage IM and a 2.5-kW dc generator as a loading unit, a three-phase voltage-source inverter and its isolation board, a 24-b digital input–output card, a 32-channel ADVANECH A/D converter card, an FPGA board, and a PC-586 computer

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Fig. 5. Control system structure of IM drive using BELBIC.

Fig. 6. Hardware implementation of the proposed controller board. TABLE I PARAMETERS OF EXPERIMENTAL IM DRIVE

Fig. 7. Simulation results of the IM speed control. Test 1: (a) Motor speed, (b) electromagnetic torque, (c) rotor flux, and (d) steady-state stator current in phase a.

as a host for commanding signal, estimating parameters, and viewing the waveforms. The pertinent parameters of the experimental IM are shown in Table I.

The IM is supplied by a three-phase 5-kW voltage-source inverter with a symmetrical two-level SV-PWM using a Xilinx FPGA with switching frequency of 5 kHz. The FPGA board communicates with the PC via the digital I/O AXIOM

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Fig. 8. Simulation results of the IM flux control. Test 2: (a) Motor speed, (b) electromagnetic torque, (c) rotor flux, and (d) stator current in phase a. TABLE II REFERENCE COMMANDS FOR TEST 1

AX5500P. The use of the FPGA system makes it possible to relate parts of the control system using hardware, which unloads the processor from parts of the assigned tasks. The FPGA in the experimental setup realizes the following functions: pattern generation for control of insulated-gate bipolar transistor (IGBT) switches based on support-vector-machine technique, provision of dead time in the switching pattern of power switches in the three-phase voltage-source inverter, sampling time of A/D card setting, shutdown of inverter in the case of faults or emergency, and data transmission between PC and drive system [39].

Fig. 9. Simulation results of the simultaneous IM speed and flux control. Test 3: (a) Motor speed, (b) error of speed, (c) electromagnetic torque, (d) rotor flux, (e) error of rotor flux, (f) stator current—iq.

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Fig. 9. (Continued.) Simulation results of the simultaneous IM speed and flux control. Test 3: (g) stator current—id, and (h) stator current in phase a. TABLE III REFERENCE COMMANDS FOR TEST 2

TABLE IV REFERENCE COMMANDS FOR TEST 3 Fig. 10. Experimental result of the IM speed control using emotional controller (BELBIC). Test 1: (a) Rotor speed, (b) electromagnetic torque, and (c) stator flux.

The inverter is designed specifically for this experiment, using six power switches packed with 40-A 1200-V SEMIKRON IGBTs, and the driving system has been designed using fast and intelligent IGBT drivers, HCPL 316J, which guarantee separation between the inverter and control system. The dclink voltage and stator currents and voltages are measured by Hall-type LEM sensors. In addition, the motor speed is also measured using an AUTONICs optic incremental encoder with 2000 pulses per round. All measured electrical signals are filtered by analog second-order low-pass filters with cutoff frequency of 2.5 kHz and converted to digital signal by means of an 11-b A/D converter with 10-μs conversion time. VI. R ESULTS AND D ISCUSSION In order to evaluate this emotional controller and, hence, to assess the effectiveness and control capability of the proposed BELBIC scheme, the performances of the proposed control scheme for the IM drive are investigated in simulation and experimental tests at different operating conditions. Digital computer simulations have been performed using Matlab/Simulink [40]. The simulated responses are shown in

Figs. 7–9. In all cases, the IM drive system has been successfully started and operated according to the flowing sequence of tests. Test 1: The speed and flux commands are given in Table II. The control system operated properly according to Fig. 7. The rotor speed has successfully tracked the command speed while the rotor flux is fixed at its reference value. Rotor speed, electromagnetic torque, rotor flux, and stator current are shown in Fig. 7(a)–(d), respectively. Test 2: The speed and flux commands are given in Table III. Fig. 8 shows the operating responses of the IM drive system using the emotional controller (BELBIC). It can be seen that the proposed controller gives regulated responses in terms of fast tracking, small overshoot, and zero steady-state errors. The rotor flux has successfully followed the rotor command flux, while the rotor speed increases linearly to its rated value. Fig. 8(a) shows the actual speed that converges to +400 r/min at t = 2 s, and the electromagnetic torque is shown in Fig. 8(b). Also, the rotor flux value shown in Fig. 8(c) converges to its reference properly. Test 3: The speed and flux commands are given in Table IV. Fig. 9 shows the simulation responses for speed, speed error, electromagnetic torque, flux, q-axis stator current, d-axis stator current, and stator phase “a” of the IM drive system using the emotional controller (BELBIC) for Test 3. It can be seen that the proposed controller gives a tuned response in terms of fast tracking without any overshoot and zero steady-state errors

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Fig. 11. Experimental result of the IM flux control using BELBIC. Test 2: (a) Speed rotor, (b) electromagnetic torque, and (c) stator flux.

by simultaneous changes in the rotor reference flux and speed commands. This does not have any adverse effects on the other. Fig. 9(a) shows the actual speed that converges to its reference quickly. Fig. 9(c) shows the electromagnetic torque response. Fig. 9(d) shows that the estimated rotor flux converges to its reference value properly. The experimental results for the same operating conditions of the simulation tests are shown in Figs. 10–12 for Tests 1, 2, and 3, respectively. These experimental results are very close and similar to those obtained by simulation results, as shown in Fig. 7–9, respectively. By comparing the simulation and experimental test results, it can be stated that the intelligent control based on emotional learning can achieve the required performances for the laboratory IM drive. Another important advantage of the proposed emotional intelligent controller is that it is relatively easy to tune the gain parameters of the controllers effectively and efficiently for high-performance IM drive systems. VII. C ONCLUSION This paper has presented a real-time implementation of an alternate but improved emotional controller for a three-phase laboratory IM drive. The implementation of the emotional controller shows good control performance in terms of robustness and adaptability. There exists a close agreement between the experimental and simulation results. A simple

Fig. 12. Experimental result of the simultaneous IM speed and flux control using BELBIC. Test 3: (a) Rotor speed, (c) electromagnetic torque, (d) rotor flux, (e) stator current in phase a, and (f) steady-state stator current in phase a.

structure of BELBIC with its fast auto learning, model-free, and good tracking features maybe used instead of conventional parameter-dependent methods. The proposed emotional

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intelligent technique can be easily adopted for niche mechatronics and industrial applications. A PPENDIX TABLE V G AIN PARAMETERS FOR BELBIC

The pertinent gain parameters of the equivalent PID controller having k1 , K1 , k2 , K2 , k3 , K3 coefficients as well as the learning step a of the Amygdala and the learning step β of the orbitofrontal cortex for the emotional (BELBIC) controller of the induction motor are given in Table V. R EFERENCES [1] R. Marino, S. Peresada, and P. Valigi, “Adaptive input–output linearizing control of induction motors,” IEEE Trans. Autom. Control, vol. 38, no. 2, pp. 208–220, Feb. 1993. [2] M. P. Kazmierkowski and D. L. Sobczuk, “Sliding mode feedback linearized control of PWM inverter-fed induction motor,” in Proc. IEEE IECON, Taipei, Taiwan, 1996, pp. 244–249. [3] R. De Doncker, F. Profumo, and A. Tenconi, “The universal field oriented (UFO) controller in the air gap reference frame,” J. Inst. Elect. Eng., vol. 113-D, no. 4, pp. 477–486, 1993. [4] I. Takahashi and N. Noguchi, “A new quick response and high efficiency control strategy of an induction motor,” IEEE Trans. Ind. Appl., vol. IA22, no. 5, pp. 820–827, Sep. 1986. [5] D. Casadei, F. Profumo, G. Serra, and A. Tani, “FOC and DTC: Two viable schemes for induction motors torque control,” IEEE Trans. Power Electron., vol. 17, no. 5, pp. 779–787, Sep. 2002. [6] G. S. Buja and M. P. Kazmierkowski, “Direct torque control of PWM inverter-fed AC motors—A survey,” IEEE Trans. Ind. Electron., vol. 51, no. 4, pp. 744–757, Aug. 2004. [7] B. K. Bose, “Neural network applications in power electronics and motor drives—An introduction and perspective,” IEEE Trans. Ind. Electron., vol. 54, no. 1, pp. 14–33, Feb. 2007. [8] B. K. Bose, “Expert system, fuzzy logic, and neural network applications in power electronics and motion control,” Proc. IEEE, vol. 82, no. 8, pp. 1303–1323, Aug. 1994. [9] J. Zhao and B. K. Bose, “Neural network based waveform processing and delayless filtering in power electronics and ac drives,” IEEE Trans. Ind. Electron., vol. 51, no. 5, pp. 981–991, Oct. 2004. [10] L. Dong and E. Izquierdo, “A biologically inspired system for classification of natural images,” IEEE Trans. Circuits Syst. Video Technol., vol. 17, no. 5, pp. 590–603, May 2007. [11] M. Mohamadian, E. Nowicki, F. Ashrafzadeh, A. Chu, R. Sachdeva, and E. Evanik, “A novel neural network controller and its efficient DSP implementation for vector controlled induction motor drives,” IEEE Trans. Ind. Appl., vol. 39, no. 6, pp. 1622–1629, Nov./Dec. 2003. [12] S. H. Kim, T. S. Park, J. Y. Yoo, and G. T. Park, “Speed sensorless vector control of an induction motor using neural network speed estimation,” IEEE Trans. Ind. Electron., vol. 48, no. 3, pp. 609–614, Jun. 2001. [13] B. Karanayi, M. F. Rahman, and C. Grantham, “Online stator and rotor resistance estimation scheme using artificial neural networks for vector controlled speed sensorless induction motor drive,” IEEE Trans. Ind. Electron., vol. 54, no. 1, pp. 167–176, Feb. 2007. [14] C. M. Kwan and F. L. Lewis, “Robust backstepping control of induction motors using neural networks,” IEEE Trans. Neural Netw., vol. 11, no. 5, pp. 1178–1187, Sep. 2000. [15] W. W. Tan and H. Huo, “A generic neuro fuzzy model-based approach for detecting faults in induction motors,” IEEE Trans. Ind. Electron., vol. 52, no. 5, pp. 1420–1427, Oct. 2005. [16] K. L. Shi, T. F. Chan, Y. K. Wong, and S. L. Ho, “Direct self control of induction motor based on neural network,” IEEE Trans. Ind. Appl., vol. 37, no. 5, pp. 1290–1298, Sep.–Oct. 2001.

[17] K. M. Passino, “Biomimicry of bacterial foraging for distributed optimization and control,” IEEE Control Syst. Mag., vol. 22, no. 3, pp. 52–67, Jun. 2002. [18] T. Orlowska-Kowalska and K. Szabat, “Control of the drive system with stiff and elastic couplings using adaptive neuro-fuzzy approach,” IEEE Trans. Ind. Electron., vol. 54, no. 1, pp. 228–240, Feb. 2007. [19] F. J. Lin, R. F. Fung, and R. J. Wai, “Comparison of slidingmode and fuzzy neural network control for motor-toggle servomechanism,” IEEE/ASME Trans. Mechatronics, vol. 3, no. 4, pp. 302–318, Dec. 1998. [20] S. Al-Olimat, A. A. Ghandakly, and S. K. Kamalasadan, “Induction motor speed control via fuzzy logic modification of reference model,” in Proc. IEEE PES, 2007, pp. 1–7. [21] T. C. Chen and T. T. Sheu, “Model referencing neural network controller for induction motor speed control,” IEEE Trans. Energy Convers., vol. 17, no. 2, pp. 157–163, Jun. 2002. [22] M. Wlas, Z. Krzeminski, J. Guzinski, H. Abu-Rub, and H. A. Toliyat, “Artificial-neural-network-based sensorless nonlinear control of induction motors,” IEEE Trans. Energy Convers., vol. 20, no. 3, pp. 520–528, Sep. 2005. [23] J. C. Löpez, L. Romeral, A. Arias, and E. Aldabas, “Novel fuzzy adaptive sensorless induction motor drive,” IEEE Trans. Ind. Electron., vol. 53, no. 4, pp. 1170–1178, Jun. 2006. [24] Y. V. Siva Reddy, M. V. Kumar, T. B. Reddy, and J. Amarnath, “Direct torque control of induction motor based on state feedback and variable structure fuzzy controllers,” in Proc. IEEE Power India Conf., New Delhi, India, 2006, pp. 1–5. [25] M. R. Jamaly, A. Armani, M. Dehyadegari, C. Lucas, and Z. Navabi, “Emotion on FPGA: Model driven approach,” Expert Syst. Appl., vol. 36, no. 4, pp. 7369–7378, May 2009. [26] 1101-8453J. Moren, “Emotion and learning: A computational model of the Amygdala,” Ph.D. dissertation, Lund Univ., Lund, Sweden, 2002. [27] J. Moren and C. Balkenius, “A computational model of emotional learning in the amygdala,” in Proc. 6th Int. Conf. Simul. Adapt. Behav., Cambridge, MA, 2000, pp. 411–436. [28] C. Lucas, D. Shahmirzadi, and N. Sheikholeslami, “Introducing BELBIC: Brain emotional learning based intelligent control,” Int. J. Intell. Automat. Soft Comput., vol. 10, no. 1, pp. 11–22, 2004. [29] N. Sheikholeslami, D. Shahmirzadi, E. Semsar, C. Lucas, and M. J. Yazdanpanah, “Applying brain emotional learning algorithm for multivariable control of HVAC systems,” J. Intell. Fuzzy Syst., vol. 17, no. 1, pp. 35–46, 2006. [30] H. Rouhani, M. Jalili, B. Arabi, W. Eppler, and C. Lucas, “Brain emotional learning based intelligent controller applied to neurofuzzy model of micro-heat exchanger,” Expert Syst. Appl., vol. 32, no. 3, pp. 911–918, 2007. [31] R. M. Milasi, C. Lucas, and B. N. Araabi, “Intelligent modeling and control of washing machine using locally linear neuro-fuzzy (LLNF) modeling and modified brain emotional learning based intelligent controller (BELBIC),” Asian J. Control, vol. 8, no. 4, pp. 393–400, Dec. 2006. [32] R. M. Milasi, M. R. Jamali, and C. Lucas, “Intelligent washing machine: A bioinspired and multi-objective approach,” Int. J. Control Automat. Syst., vol. 5, no. 4, pp. 436–443, Aug. 2007. [33] M. R. Jamali, M. Valadbeigi, M. Dehyadegari, Z. Navabi, and C. Lucas, “Toward embedded emotionally intelligent system,” in Proc. IEEE EWDTS, Sep. 2007, pp. 51–56. [34] M. R. Jamali, A. Arami, B. Hosseini, B. Moshiri, and C. Lucas, “Real time emotional control for anti-swing and positioning control of SIMO overhead traveling crane,” Int. J. Innovative Comput. Inf. Control, vol. 4, no. 9, pp. 2333–2344, Sep. 2008. [35] M. R. Jamali, M. Dehyadegari, A. Arami, C. Lucas, and Z. Navabi, “Realtime embedded emotional controller,” J. Neural Comput. Appl., vol. 19, no. 1, pp. 13–19, 2010. [36] M. A. Rahman, R. M. Milasi, C. Lucas, B. N. Arrabi, and T. S. Radwan, “Implementation of emotional controller for interior permanent magnet synchronous motor drive,” IEEE Trans. Ind. Appl., vol. 44, no. 5, pp. 1466–1476, Sep./Oct. 2008. [37] P. C. Krause, Analysis of Electric Machinery. New York: McGraw-Hill, 1997. [38] E. T. Rolls, The Brain and Emotion. London, U.K.: Oxford Univ. Press, 1999. [39] M.-W. Naouar, E. Monmasson, A. A. Naassani, I. Slama-Belkhodja, and N. Patin, “FPGA-based current controllers for AC machine drives—A review,” IEEE Trans. Ind. Electron., vol. 54, no. 4, pp. 1907–1925, Aug. 2007. [40] Matlab Simulink User Guide, The MathWorks Inc., Natick, MA, 2003.

ARAB MARKADEH et al.: SPEED AND FLUX CONTROL OF IMs USING EMOTIONAL INTELLIGENT CONTROLLER

G. R. Arab Markadeh was born in Shahrekord, Iran, in 1974. He received the B.Sc., M.S., and Ph.D. degrees in electrical engineering from Isfahan University of Technology, Isfahan, Iran, in 1996, 1998, and 2005, respectively. He is currently an Assistant Professor in the Faculty of Engineering, Shahrekord University, Shahrekord. His fields of research are nonlinear control, power electronics, and variable-speed drives. Dr. Markadeh was the recipient of the IEEE Industrial Electronics Society IECON’04 Best Paper Presentation Award in 2004.

Ehsan Daryabeigi (S’10) received the B.Sc. degree in electrical engineering from Islamic Azad University, Yazd Campus, Iran, in 2005. He has been working toward the M.Sc. degree in electrical engineering at Islamic Azad University, Najafabad Campus, Iran, since 2009. He is currently with Shahrekord University, Shahrekord, Iran. His research areas are intelligent control of ac motors, power electronics, and power systems. He has contributed to several national and international journals and conferences. Mr. Daryabeigi is a member of the Young Researchers Club.

Caro Lucas received the M.Sc. degree from the University of Tehran, Tehran, Iran, in 1973, and the Ph.D. degree from the University of California, Berkeley, in 1976. He was a Professor and the Director of the Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, University of Tehran, as well as a Researcher with the School of Intelligent Systems (SIS), Institute for Studies in Theoretical Physics and Mathematics, Tehran. He served as the Director of SIS, from 1993 to 1997, and as Chairman of the School of Electrical and Computer Engineering, University of Tehran, from 1986 to 1988. He was also the Managing Editor of Memories of the Engineering Faculty, University of Tehran, from 1979 to 1991, and Associate Editor of the Journal of Intelligent and Fuzzy Systems, from 1992 to 1999. He had been a Reviewer of Mathematical Reviewers since 1987. He was also a Visiting Associate Professor at the University of Toronto, Toronto, ON, Canada, from the summer of 1989 to 1990, and the University of California, Berkeley, from 1988 to 1989, an Assistant Professor at the University of Garyounis, Benghazi, Libya, from 1984 to 1985, and the University of California, Los Angeles, from 1975 to 1976, a Senior Researcher with the International Center for Theoretical Physics and the International Center for Genetic Engineering and Biotechnology, both in Trieste, Italy, and the Institute of Applied Mathematics Chinese Academy of Sciences, Harbin Institute of Electrical Technology, Harbin, China, a Research Associate with the Manufacturing Research Corporation of Ontario, and a Research Assistant with the Electronics Research Laboratory, University of California, Berkeley. He is the holder of a patent on Speaker Independent Farsi Isolated Word Neurorecognizer. His research interests include biological computing, computational intelligence, uncertain systems, intelligent control, neural networks, multiagent systems, data mining, business intelligence, financial modeling, and knowledge management. He was the founder of SIS and has assisted in founding several new research organizations and engineering disciplines in Iran. Also, he had been a researcher in several industrial projects on intelligent control of switched reluctance and interior permanent-magnet synchronous motors, inspired by biological systems and their applications. He died in 2010. Dr. Lucas was the Chairman of the IEEE Iran Section from 1990 to 1992, and of several international conferences. He was also the recipient of several research grants from the University of Tehran and SIS.

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M. Azizur Rahman (S’66–M’68–SM’73–F’88– LF’07) was born in Santahar, Bangladesh, on January 9, 1941. He received the B.Sc. degree in electrical engineering from Bangladesh University of Engineering and Technology (BUET), Dhaka, Bangladesh, in 1962, the M.A.Sc. degree in electrical engineering from the University of Toronto, Toronto, ON, Canada, in 1965, and the Ph.D. degree in electrical engineering from Carleton University, Ottawa, ON, in 1968. In 1962, he joined BUET as a Lecturer and was promoted to Full Professor in 1974. Since 1976, he has been with Memorial University of Newfoundland, St. John’s, NL, Canada, where he is currently a Professor and University Research Professor. He has 49 years of teaching experience including about ten years of full-time and concurrent industrial, utility, and consulting experiences with GE, Schenectady, NY; GE Canada, Peterborough, ON; Newfoundland Hydro, St. John’s; Dhaka Electric Supply, Dhaka; Teshmont Consultants, Winnipeg, MB, Canada; Iron Ore Company of Canada, etc. He has been a Visiting Professor and Research Fellow at Imperial College, London, U.K.; Technical University of Eindhoven, Eindhoven, The Netherlands; University of Manitoba, Winnipeg; University of Toronto; Nanyang Technological University, Singapore; Tokyo Institute of Technology, Tokyo, Japan; University of Hong Kong, Hong Kong, China; Tokyo University of Science, Tokyo; and the University of Malaya, Kuala Lumpur, Malaysia. He has published more than 660 papers including 11 patents and two books as well as five book chapters. His current research interests are in machines, intelligent controls, power systems, digital protection, power electronics, and wireless communication. Dr. Rahman is the recipient of numerous awards, including the GE Centennial Invention Disclosure Award in 1978, the IEEE Outstanding Students Counselor’s Award in 1980, the IEEE Notable Service Award for contributions to IEEE and Engineering Professions in 1987, the IEEE Industry Application Society’s Outstanding Achievement Award in 1992, the Association of Professional Engineers and Geoscientists of Newfoundland Merit Award in 1994, the IEEE Canada Outstanding Engineering Educator’s Medal in 1996, the IEEE Third Millennium Medal 2000, the IEEE Cyril Veinott Electromechanical Energy Conversion Award in 2003, the IEEE William E. Newell Power Electronics Award in 2004, the Khwarizmi International Award in 2005, the IEEE Dr. Ing. Eugene Mittelmann Achievement Award 2007, the IEEE Richard Harold Kaufmann Technical Field Award 2007, the 2008 A. D. Dunton Award of Carleton University, the IEEE Power and Energy Society Distinguished Service Award in 2008 and the IEB Gold medal in 2011. He is a registered Professional Engineer in the Province of Newfoundland and Labrador, Canada; a member of the Institution of Electrical Engineers, Japan; a Fellow of the Institution of Engineering and Technology, U.K.; a Fellow of the Engineering Institute of Canada; a Fellow of the Canadian Academy of Engineering; and a Life Fellow of the Institution of Engineers, Bangladesh.