DOI: 10.12928/TELKOMNIKA.v14i4.4032 1417
Novel Smart Fuzzy Controller Based Separation
Efficiency Model for Centrifugation
M. S. Salim1, Naseer Sabri*2, Noaman M. Noaman3, S. Fouad1,4 1
School of Laser & Optical Electronics Engineering, AlNahrain University, P.O.Box 64040, Jadria, Iraq 2
School of Computer and Communication Engineering, University Malaysia Perlis, Kangar, 01000, Malaysia
3Informatic Engineering Department, AMA International University, Bahrain
*Corresponding author, e-mail: [email protected]
Abstract
In biomedical laboratories, to get surely separation efficiency of liquids using currently centrifuge devices, centrifugation process must take not less than 10 minutes at 3000 rpm of sample rotation. An intelligent fuzzy controller for laboratory centrifuge device based on separation efficiency model is produced. The separation efficiency model is optimizing the time of centrifugation. The new controller programmed with separation efficiency model have many objectives such as high separation efficiency, decrease blood test period, low cost and device power consumption are achieved. In addition, increases the reliability for centrifuge device to estimate the centrifugation period for wide range of predefined separation efficiency, and then, the power consumption of specific separation efficiency for any centrifuge device wattage can be estimate. The new Fuzzy Logic Controller of centrifuge device has successfully save 18kW.h monthly for 100 daily time device operation.
Keywords: Intelligent FLC, BLDC motor controller, Attenuation Measurement, Separation efficiency
Copyright © 2016 Universitas Ahmad Dahlan. All rights reserved.
1. Introduction
The Fuzzy logic (FL) has rapidly become one of the most successful of today’s technology for developing sophisticated control system. With it aid complex requirement so may be implemented in amazingly simple, easily minted and inexpensive controllers. Several studies show, both in simulations and experimental results, that Fuzzy Logic control yields superior results with respect to those obtained by conventional control algorithms thus, in industrial electronics the FLC control has become an attractive solution in controlling the electrical motor drives with large parameter variations [1-7]. However, the design of conventional control system essential is normally based on the mathematical model of plant .if an accurate mathematical model is available with known parameters it can be analyzed, for example by bode plots or nyquist plot, and controller can be designed for specific performances, such procedure is time consuming. Also fuzzy logic controller has adaptive characteristics; The adaptive characteristics can achieve robust performance to system with uncertainty parameters variation and load disturbances. The block diagram of a basic FLC is shown in Figure 1. Figure 1 gives a breakdown of the basic fuzzy controller architecture used throughout the works in this research.
Based on our previous research of improving the separation efficiency measurements for blood-plasma [7], a comprehensive simulation model with fuzzy logic controller based on predefined separation efficiency model is presented. MATLAB/fuzzy logic toolbox is used to design separation efficiency to centrifugation period converter and FLC, which is integrated into simulations with simulink. The control algorithms, fuzzy logic and PID are compared. Several simulation results are shown to confirm the performance and the validity of the proposed model. Also the model based on optimization spinning time simulation provides greater details of the BLDC motor drive system [8-13].
reliable of device by selecting various separation efficiency and exactly evaluation the period of separation efficiency.
Figure 1. The 2-input 1-output fuzzy controller design. The controller uses fuzzy part (a), inference or rule-lookup (b), and de-fuzzifies by aggregation (c)
2. Method
The laboratory centrifuge device used for separation two or more materials from each other based on their density. The separation time period and accuracy are evaluated based on the theory of sedimentation. The sedimentation time and accuracy of separation is limited by several parameters such parameters related with density, size, radius and shape of particles, therefore, if there is a variation in particle size or shape due to centrifugation process the separation accuracy is different. The mathematical model for time as a function of separation efficiency (separation efficiency model (SEM)) derived in our previous research produce a save spinning time period and keeping high separation accuracy. The fuzzy controller proposed for laboratory centrifuge device is shown in Figure 2. The current laboratory centrifuge controller is manually feed by maximum operating speed and centrifugation period (the centrifugation time take to separate less than 1 ml of blood-plasma is 10 minutes) while proposed controller supplied with converter of separation efficiency percentage to centrifugation time, this will produce high reliable of device by selecting various separation efficiency and exactly evaluation the required period of separation efficiency. The design of proposed fuzzy controller is divided into two parts, separation efficiency percentage to centrifugation period converter (SECPC) and fuzzy logic controller, as illustrated in more details in next sections.
Figure 2. Schematic diagram of Smart Fuzzy Controller
2.1. Converter Model
speed (long settling time). The practically period of acceleration and deceleration are identical and equal to 13.6 sec as rapid mode operation.
The rest time of velocity profile is determined by time optimization model (equation 1, [7]) as a constant speed period (tc). Figure 4 shows velocity profile for 20% separation efficiency, which the total period of 20% separation is about 55 seconds calculated using equation 1 [7].
(0.0324 0.194) c Acc Dec T t t t
T t t
Acc Dec
(1)
2.2. FLC Design
The two-input-one-output FLC is designed for the present application. The inputs to the FLC are error e(k) = (set-point speed – measured speed), and change-in-error ce(k) = (present error – previous error) [14-19]. These two inputs are defined on a universe of discourse with the seven membership functions (NL, NM, NS, ZE, PS, PM, and PL). The output of the FLC is ‘CU’ is given as input to the BLDC motor driver. The inputs and controlled output of the FLC are described by:
E=e(k) = r(k) – y(k) (2)
CE=ce(k)=e(k)– e(k-1) (3)
CU=Cu(k) (4)
Figure 3. Time period of linear velocity profile for 20% separation efficiency.
The triangular membership function is used to fuzzify the error and change-in-error. The error and change-in-errors are mapped between -1.0 and +1.0 on the universe of discourse. The fuzzy inference engine is the heart of the FLC comprises both the knowledge base and decision-making logic. The knowledge base consists of a data base with necessary linguistic variables (rule set) and decision- making logic used to decide what control action to be taken. The inference process of the FLC relates the fuzzy state variables e(k) and ce(k) to the fuzzy controlled action cu(k) with the help of linguistic rules. The decision-making logic uses IF and THEN rules to pick up appropriate control action for the process. As an example, the following is a possible control rule for a FLC [16]:
IF e(k) is ‘PM’ and ce(k) is ‘NS’, THEN cu(k) is ‘PS’ IF e(k) is ‘PM’ and ce(k) is ‘Z’, THEN cu(k) is ‘PM’ IF e(k) is ‘PM’ and ce(k) is ‘Ps’, THEN cu(k) is ‘PB’ IF e(k) is ‘PM’ and ce(k) is ‘PM’, THEN cu(k) is ‘PB’
A control rule can be regarded as an implication, Ei, CEi, →Ui . The implementation of the inference mechanism in the present study is using Mamdani’s minimum operation Rc, which is given as:
: 1, R U
c i ni i
Where αi, the weighing factor, is the measure of the contribution of the ith rule to the fuzzy control action and is expressed as:
i
C i
C E i
(6)
The output of the fuzzy inference engine is a fuzzy set on the output universe of discourse, so this needs to convert into non-fuzzy (crisp value). The centre of gravity (COG) method is used for defuzzification. The defuzzified output for the process is calculated from the equation
Where, n is the number of elements in control output fuzzy set. wi is the support member value for the ith element μU (wi) is the value of grade of membership function for ith element. Defuzzification is a mapping from a space of fuzzy control actions defined over an output universe of discourse into a space of non-fuzzy (crisp) control actions.
3. Results & Discussion
3.1. Optimization of Process Period
The designed fuzzy logic controller is based on the rule based designed, Figure 4, shows the simulink diagram of proposed centrifuge fuzzy controller. The detailed operational characteristics of phase current, back EMF and speed is shown in Figure 5. These parameters characteristics based on inverter pulse width modulation (PWM) of switching function concept. The frequency of PWM is increase during the acceleration period, therefore the amplitude of current and electromagnetic phase increase then speed increase as shown in Figure 5 (left column).
During a constant speed period, the PWM frequency is 200Hz, therefore, the BLDC running at 3000 rpm (maximum speed) (middle column Figure 5). At the end of maximum speed(constant speed period) the BLDC speed go into deceleration period in which the speed start gradually reduced because of PWM frequency decreased which produce amplitude reduction for phase current, EMF phase and speed as shown in Figure 5 (right column).
The dynamic responses of the speed are shown in Figure 6. The reference value of maximum current is computed from the generated constant torque reference; consequently it is used in the hysteresis control block. Furthermore, the control algorithms, FLC and PID can be compared by using developed model. As shown in Figure 6, if the PID controller is used, the real speed reaches the desired value in 14.1 seconds and 0.1% over shoot. On the other hand, if FLC is used, the real speed and torque reach the desired value in 13.6 seconds settling time and 0.0% over shoot, as mentioned before this period is acceleration time needed to prevent damage may be happened to blood cells during centrifugation process. In conclusion it can be said that unlike the classical controller used in currently centrifuge controller, FLC based on separation efficiency is more effective and reliable in BLDC motor drives of centrifuge device. Unlike classical model the proposed centrifuge controller has benefits such as adjustable seperastion efficiency, shorter separation time, lower power consumption belong to high accuracy.
3.2 Power Consumption of Intelligent Centrifuge Controller
Figure 4. Simulink diagram of centrifugation fuzzy logic
Figure 5. Waveforms of phase current, EMF and linear speed profile during acceleration speed period (left column), constant speed period (middle column), and deceleration speed period
(right column)
The experimental results, 0.35ml with greater than 95% plasma concentration, the proper time is 3 minute, therefore, the total time of centrifugation be 3.45 minutes. The electrical power consumption can be easily calculated using the following formula, which it mentioned in our previous research [7, 14]:
(
. )
(
)
( )
PC PC t
T
kW h
D
kW
O h
(8)
Where TPC is the total power consumption, DPC is device power consumption, and Ot is operation time period. The power of Laboratory centrifuge device (Kubota corporation model 2420) used is 180watt. Evaluation of the separation efficiency, and power consumption for discrete five minute using 180 watt centrifuge device are shown in Table 1 [14].
The classical spinning time required to separate 0.35 ml plasma of 1 ml blood with separation efficiency greater than 90%, is five minutes according to manufacture recommended, Figure 7. Therefore, the power consumption for one time daily operation of currently centrifuge controller (five minutes spinning) is 16.36x10-3 kW.h, while 10.36x10-3 kW.h using optimization time model (3.453 minute spinning). The device power consumption is directly proportional to centrifugation period and separation efficiency. [14]
Table 1. Evaluation of the attenuation, separation efficiency, and power consumption for discrete five minute using 180 watt centrifuge device [14]
Number of
Figure 7. Power consumption versus 1ml blood sample centrifugation time for 180Watt centrifuge device
4. Conclusion
device power consumption and centrifugation time, plasma separation efficiency is linear. Therefore, the power consumption of specific separation efficiency for any centrifuge device wattage can be estimate. The mathematical model of centrifugation time derived based on attenuation measurements has successfully save 18kW.h monthly for 100 daily time device operation.
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