SIMULINK MODELING OF FUZZY CONTROLLER FOR CANE LEVEL CONTROLLING

Transcription

1 International Journal of Industrial Engineering & Technology (IJIET) ISSN Vol. 3, Issue 1, Mar 2013, TJPRC Pvt. Ltd. SIMULINK MODELING OF FUZZY CONTROLLER FOR CANE LEVEL CONTROLLING YOGESH MISRA 1 & H R KAMATH 2 1 Research Scholar, Mewar University, Chittorgarh, Rajasthan, India 2 Director, Malwa Institute of Technology, Indore, Madhya Pradesh, India ABSTRACT Due to the non-linearity in the input parameters a fuzzy based controller proved to be more effective. This paper deals with the integration of the developed fuzzy inference system with the simulink model of dc cane carrier motor for maintaining the cane level during the crushing of cane in a sugar mill for optimum cane juice extraction. The primary purpose of fuzzy controller is to generate appropriate voltage to vary the speed of cane carrier motor in order to maintain the level of cane fiber in Donnelly chute. The overall system is simulated in MATLAB version (R2020b) and it is verified that the proposed fuzzy controller is able to vary the speed of cane carrier motor depending upon the variation in cane fiber. KEYWORDS: Simulink, Fuzzy Logic Controller, Sugar Mill, Cane Carrier, Cane Fiber, Donnelly Chute INTRODUCTION Embedding intelligence in industrial automation by using soft computing is growing. In the manufacturing process it improves the production efficiency with higher consistency and quality. In a sugar mill for optimum juice extraction, the cane level in Donnelly chute is to be maintained. The conventional controllers[1] used for the automation of a process in an industry show poor performance when the process is non-linear. Because of the nonlinearity controllers based on mathematic models are very difficult to design with traditional control theory [2][3][4]. However fuzzy control algorithm is an efficient control method best suited for nonlinear process. So it is suitable to design fuzzy inference system for cane level controlling[5]. This paper deals with the integration of already developed fuzzy inference system[6] with the cane carrier motor for maintaining it s speed so that the level of cane fiber in Donnelly chute remains constant. FUZZY MODEL OF CANE LEVEL CONTROLLER Maintaining the cane level during cane juice extraction is an important task of the operator. A schematic of cane juice extraction from first mill is shown in Figure 1[6][7]. Parameters of cane crushing mechanism are Speed of cane carrier motor, Speed of cane crushing mill and Total height of Donnelly Chute. Cane fiber is carried by cane carrier and finally dumped in Donnelly chute from where it is crushed by the rollers. For the maximum extraction of cane juice the level of cane fiber is maintained in between 1.98m 2.2m. The amount of cane fiber carried by cane carrier varies due to non-uniformity of cane supply. If the level of cane fiber falls below the desired level then the speed of cane carrier motor is to be increased and if the level of cane fiber rises above the desired level the speed of cane carrier motor is to be decreased for maintaining the desired level[8]. Due to the non-linearity in the process a fuzzy based controller will be suitable for maintaining the desired cane

2 44 Yogesh Misra & H R Kamath fiber level in Donnelly chute. The fuzzy control system is designed using fuzzy logic tool box of MATLAB R2010b. Input parameter Height is in the range (0 to 300cm), input parameter Change-Height is in the range (-50cm to +50cm) and output parameter Volt is in the range (120V to 180V). Input parameter Height is in the range (0 to 300cm) and fuzzified into following seven trapezoidal linguistic variables: NL (Negative Large): [ ] NB (Negative Big): [ ] NM (Negative Medium): [ ] NS (Negative Small): [ ] JR (Just Right): [ ] PS (Positive Small): [ ] PM (Positive Medium): [ ] Input parameter Change-Height is in the range (-50cm to +50cm) and fuzzified into following seven trapezoidal linguistic variables: NB (Negative Big): [ ] NM (Negative Medium): [ ] NS (Negative Small): [ ] JR (Just Right): [ ] PS (Positive Small): [ ] PM (Positive Medium): [ ] PB (Positive Big: [ ) Output parameter Volt is in the range (120V to 180V) and fuzzified into following seven trapezoidal linguistic variables: NB (Negative Big): [ ] NM (Negative Medium): [ ] NS (Negative Small): [ ] JR (Just Right): [ ] PS (Positive Small): [ ] PM (Positive Medium): [ ] PB (Positive Big): [ ]

3 Simulink Modeling of Fuzzy Controller for Cane Level Controlling 45 The fuzzy rule matrix shown in Table 1 is developed for the fuzzy controller which produce appropriate voltage to run the cane carrier motor depending upon the height of cane fiber in Donnelly chute and change in cane fiber height in cane carrier. CANE CARRIER MOTOR MODEL A model of DC motor is build in Simulink [9][10] and is shown in figure 2. Various parameters of separately exited DC motor are as follows: E a = armature voltage (V) R a = armature resistance (Ω) L a = armature inductance (H) I a = armature current (Ia) E b = back emf (V) ω = angular speed (rad/sec) T m = motor torque (Nm) Θ = angular position of rotor shaft (rad) J m = motor moment of inertia (kgm 2 ) K f = windage torque coefficient (Nms/rad) K e = torque constant of motor (Nm/A ) K b = back emf constant (Vs/rad) E a (t) = R a i a (t) + L a + e b (t).(i) e b (t) = K b ω(t) T m = K t i a (t).(ii).(iii) T m = J m ω + K f ω(t).(iv) From equation (i) and (ii) we get: E a (t) = R a i a (t) + L a + K b ω(t).(v) From equation (iii) and (iv) we get: K e i a (t) = J m ω + K f ω(t).(vi) Laplace transform of equation (v) and (vi) are given as: E a (s) = R a I a (s) + L a I a (s).s + K b W(s) K e I a (s) = J m W(s).s + K f W(s).(vii).(viii)

4 46 Yogesh Misra & H R Kamath Change Height Height Table 1: Fuzzy Rule Matrix [8] NB NM NS JR PS PM PB NL PB PB PB PB PM PS JR NB PB PB PM PM JR JR JR NM PM PM PS PS PS JR JR NS PM PM PS PS JR JR NS JR PB PM PM JR NS NM NB PS PS PS JR NS NS NM NB PM NS JR NS Nm NM NM NB Figure 1: Cane Juice Extraction Mechanism [8] MATLAB Simulation Results A simulation run is performed on the MATLAB version (R2020b) platform. The result of the simulation is shown in Table 2. Figure 5 shows the variation in cane fiber in Donnelly chute and cane carrier ((in meter). Figure 6 shows the voltage generated by fuzzy controller (in volt) to run cane carrier motor depending upon the height of cane fiber in Donnelly chute and cane carrier. Figure 7 shows the speed of cane carrier motor (in rpm). The time taken by cane carrier motor to reach steady-state speed is also given in Table 2. CONCLUSIONS AND FUTURE WORK This paper presents the integration of fuzzy controller [11] with cane carrier motor using simulink block. The performance criteria are defined in time domain where transient response of the system to a step input is considered. The results of the experiment show that the proposed fuzzy controller is able to vary the speed of cane carrier motor depending upon the variation in cane fiber.

5 Simulink Modeling of Fuzzy Controller for Cane Level Controlling 47 The developed fuzzy controller can be implemented in FPGA and integrated with microcontroller to generate the analog voltage for controlling the opening of a solenoid valve. The solenoid valve will control the in-flow of steam to control the speed of a turbine controlled cane carrier motor. Figure 2: Simulink Model of DC Motor Figure 3: Parameters of DC Motor Figure 4: Simulink Model of Fuzzy Controller to Maintain the Cane Fiber Level in Donnelly Chute

6 48 Yogesh Misra & H R Kamath Figure 5: Height of Cane Fiber in Donnelly Chute and Change in Height of Cane Fiber in Cane Carrier Figure 6: Voltage Generated by Fuzzy Controller to Run DC Motor Figure 7: Speed of Cane Carrier Motor

7 Simulink Modeling of Fuzzy Controller for Cane Level Controlling 49 S. No Cane Fiber Height in Donnelly Chute (m) Cane Fiber Change-Height in Cane Carrier (m) Table 2: Simulation Result (Col. 2 Col. 3) Voltage Generated by Fuzzy Controller (v) Simulatio n Time (Sec) Cane carrier Motor Speed (rpm) Time Taken by Motor to Reach Steady-State (Sec) No Change No Change No Change REFERENCES 1. J.G. Ziegler; N.B. Nichols, "Optimum settings for automatic controllers", Trans.ASME, vol. 64, pp , M. Araki; Control Systems, Robotics and Automation Vol. II - PID Control, Kyoto University, Japan 3. K.J. Astrom and T.H Hagglund, "New tuning methods for PID controllers", 3rd European Control Conference, Process Automation control, 2011, Controller system for industrial automation (online) available at: 5. Y. Misra and H.R Kamath, A review on the application of fuzzy logic in increasing the efficiency of industrial process, International journal of latest trends in engineering and technology, (online) available at: (ISSN X) vol. 1, issue 3, pp , T. Ozcokak, M. Fu and G.C Goodwin, Maceration control of a sugar cane crushing mill, Proceedings of the American Control Conference, issue 6, pp , 2000, Chicago, Illinois. 7. Sugar Knowledge International Ltd., How sugar is made, (online) available at: Y. Misra and H.R Kamath, Design methodology of fuzzy inference system for cane level controlling, International journal of emerging technology and advanced engineering, (online) available at: (ISSN ) vol. 2, issue 6, pp , S. Saneifard, N.R Prasad, H.A Smolleck and J.J Wakileh, "Fuzzy-Logic-Based speed control of shunt DC motor", IEEE Trans. Of Education, vol. 41, No. 2, pp , P. Guillemin, "Fuzzy Logic applied to motor control", IEEE Trans. On Industry applications, vol. 32, No. 1, pp , R. Dodd, A. Chiou, X. Yu and R. Broadfoot, "A smart supervisory control system frame work for a sugar mill crystallization stage", IEEE International Conference on industrial informatics, vol. 32, No. 1, pp , 2008, Daejeon, Korea.

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