Comparative Analysis of Room Temperature Controller Using Fuzzy Logic & PID

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1 Advance in Electronic and Electric Engineering. ISSN , Volume 3, Number 7 (2013), pp Research India Publications Comparative Analysis of Room Temperature Controller Using Fuzzy Logic & PID Jay Kumar 1, Richa Kapoor 2, Brijebdra 3, Hemant 4, Jyoti 5 1 Electronics & Communication, RBS Engineering Technical Campus Agra, India 2 Electrical & Electronics HCST, Farah, Mathura India 3, 4, 5 Electronics & Communication, RBS Engineering Technical Campus Agra, India. Abstract In this paper a control scheme for static room is proposed, based on the continuous monitoring of the thermal variables. The aim is to maintain the temperature and the humidity of room close to the targeted values, and reduce the electrical energy intake of the compressor/fan while utilizing all available resources in the most efficient manner. In this paper, the indoor temperature control loop has been implemented using a conventional PID algorithm & fuzzy logic. Since Fuzzy logic technique is an innovative technology used in designing solutions for multi-parameter and non-linear control models for the definition of a control strategy. As a result, it delivers solutions faster than the conventional control design techniques. A practical application of a fuzzy control system & PID for a static room was carried out and the simulation results are presented using Simulink / MATLAB trying to deal mainly with the issue of maintaining the desired indoor temperature in spite of the change in outdoor temperature simultaneously reduceing the temperature oscillations and energy consumption. Keywords: Temperature control, Fuzzy logic, PID..

2 854 Jay Kumar et al 1. Introduction A Control scheme for Indoor Room temperature is proposed based on the continuous monitoring of the Thermal and climate variables. The dynamic behavior of the relevant variables is determined and expressed in terms of a system transfer function. In this study, the indoor temperature control loop has been implemented using a fuzzy logic & compare with PID algorithm. Since control applications have multiple inputs and require mathematical modeling and tuning of a large number of parameters, which makes implementation very tedious and time consuming. In case of fuzzy logic it provides an alternative control because it is closer to real world. Fuzzy logic is handled by rules, membership functions and inference process, which results in improved performance, simpler implementation and reduced design costs [1, 2]. Most Fuzzy rules can simplify the implementation by combining multiple inputs into single if-then statements while still handling non-linearity & does not require mathematical modeling. Fuzzy logic controller (FLC) uses the qualitative knowledge of a system to design a controller. FLC deals with the uncertainties in the process of control by collecting both human knowledge and expertise [4]. The purpose of this paper is to reduce the temperature oscillations and heat consumption by using the fuzzy logic tool box/ Simulink in the Matlab. This proposed design work of Room temperature System is the application of fuzzy logic Control system consisting of three input variables: Temperature difference between outdoor & indoor, no. of person in room and Rate of change of temperature and output variable is Heat Consumption used in a processing plant to maintain the temperature inside room. 2. PID Controller The PID controller algorithm is the almost universally used control strategy. The PID Control equation is defined by- Where Kp, Ki and Kd are the proportional, integral, and derivative constant gains, respectively. The signal e(t) defined as e(t) = r(t) c(t) is the error signal between the reference r(t) and the process output c(t). (1) Figure 2.1: Simulink model using PID controller.

3 Comparative Analysis of Room Temperature Controller Using Fuzzy Logic & PID 855 In the created model, the thermostat is set to 20 C. Changes in outdoor temperature are Simulated by a sine wave with amplitude of 3 degrees to a base temperature of 12 degrees. The temperature outdoor varies in a sinusoidal manner, whereas the indoors temperature is maintained within 3 C of "Set Point" 20 C. Figure 2.2: Indoor temperature using PID Controller. 3. Fuzzy controller Fuzzy logic was born in 1965 by Zadeh. Nowadays, it is widely used in industrial applications. Fuzzy logic can model the nonlinear relationship between inputs and outputs. It can simulate the operator s behavior without use of mathematical model. It is a method that transfers human knowledge into mathematics, with the aid of if then rules [3]. 3.1 fuzzification Fuzzification aims to convert crisp input values into corresponding fuzzy set values. FLC involves receiving input signal and converting the signal into fuzzy variable (fuzzifier). The fuzzy control rules relate the input fuzzy variables to an output fuzzy variable which is called fuzzy associative memory (FAM), and defuzzifying to obtain crisp values to operate the system (defuzzifier). In this FLC, there are three fuzzy input variables and one output fuzzy variable. The fuzzy input variables are the error, e which is the error between the reference and the measured temperature and the error difference, second is Rate of Change of Temperature & third is no. of person inside room. The membership function for fuzzy sets can have many different shapes, depending on a definition. The popularly used fuzzy membership functions in many applications are triangular, trapezoidal, bell-shaped and sigmoid membership function. The membership function used is the triangular type as shown in Figure 3.1.

4 856 Jay Kumar et al Figure 3.1: Member ship function for input Temperature Error. 3.2 fuzzy rules A fuzzy logic rule is called a fuzzy association. A fuzzy associative memory (FAM) is formed by partitioning the universe of discourse of each condition variable according to the level of fuzzy resolution chosen for these antecedents, thereby a grid of FAM elements [5]. The entry at each grid element in the FAM corresponds to fuzzy action. The FAM has three inputs and one output. Table 3.1 Rules For Membership Function. s.n. Inputs output Temp. Error Rate of Change of Temp. No. of Person in Room Heat Consumption 1. Too Very Large 8-11 Very High 2. Too Large 6-9 High 3. Too Normal 4-7 High 4. Too Small 2-5 Medium 5. Too Very Small 0-3 Medium 6. Very Large 8-11 Very High 7. Large 6-9 High 8. Normal 4-7 Medium 9. Small 2-5 Medium 10 Very Small 0-3 Very Low 3.3 defuzzification It is the opposite of fuzzification. It aims to convert a value (actually several values) of the fuzzy output variable to a single crisp value. There are many ways to perform defuzzification, just as there are many ways to perform fuzzification. The most common method is called Min-Max method, which selects only the maximum or minimum values from each fuzzy rule for further processing. Procedurally, the most

5 Comparative Analysis of Room Temperature Controller Using Fuzzy Logic & PID 857 convenient way to implement defuzzification using this method is to combine it with fuzzy rules processing [6]. 4. Simulation Result & Conclusion Throughout the paper many simulations have been carried out to study the implementation of fuzzy logic control in room temperature control system. The simulations also include the PID controller. The performance of both types of control techniques is carefully compared. Result for the PID controller is under. This model gives a very high oscillated output in indoor temperature and very high energy consumption, as shown in fig 2.2. A fuzzy based room temperature control system have been developed and successfully demonstrated as shown in fig 4.2. The system has been designed for maintaining the temperature at any desired value. The membership function can be modified to achieve any desired temperature. The use of fuzzy logic provides very fast response and reliable operation. Heat consumption analysis shows that FLC gives more energy savings compared with PID controller. The study has shown that FLC is better than PID controller. Figure 4.1: Rule Viewer Window. Figure 4.3: Simulink model using fuzzy logic with three inputs

6 858 Jay Kumar et al References [1] Ahmad Parvaresh, Seyed Mohammad Ali Mohammadi, Ali Parvaresh, A new mathematical dynamic model for HVAC system components based on Matlab/Simulink, International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: , Volume-1, Issue-2, July [2] Alok Singh, Sandeep Kumar, Controlling Thermal Comfort Of Passenger Vehicle Using Fuzzy Controller, International Journal of Engineering Research and Applications (IJERA) ISSN: Vol. 2, Issue 4, June-July [3] Henry Nasution, Hishamuddin Jamaluddin, Jamaluddin Mohd. Syeriff, Energy Analysis for Air Conditioning System Using Fuzzy Logic Controller, TELKOMNIKA, Vol.9, No.1, April 2011, pp. 139~150 ISSN: [4] M. Abbas, M. Saleem Khan, Fareeha Zafar, Autonomous Room Air Cooler Using Fuzzy Logic Control System, International Journal of Scientific & Engineering Research Volume 2, Issue 5, May-2011,ISSN [5] Mohd Fauzi Othman and Siti Marhainis Othman Department of Control and Instrumentations, Faculty of Electrical Engineering, Universiti Teknologi Malaysia, Fuzzy Logic Control for Non Linear Car AirConditioning vol 8 no 2, 2006 [6] W. Leonard, Introduction to control Engineering and Linear Control System. Springer-Verlag Heidelberg, 1976, pp [7] YAO, Jian and Jin XU. Indoor Thermal Environment Simulation by using MATLAB and Simulink.Applied Mechanics and Material 2010, vol , pp

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