Simulation of Temperature Control System

digilib.uinsby.ac.id digilib.uinsby.ac.id digilib.uinsby.ac.id digilib.uinsby.ac.id digilib.uinsby.ac.id digilib.uinsby.ac.id digilib.uinsby.ac.id 87 I S M O S A T, Proceeding International Symposium For Modern School Development, .... Figure 5. Rise time and Settle time of the simulation results PID-PSO with LDIW. The simulation results show that the control system response curves correspond- ing to the set point 40 o . Rise time is reached at the 7:18 time simulation and settle time at 15.76 time simulation, as in Figure 5, where the X axis is time simulation and the Y axis is in units of temperature o C.When the setpoint is reached and the control system running, Error generated 0:18. Overshoot does not occur when raise the target because the PSO with LDIW better methods for changing the value of the gain Error, ΔError and output so PWM value given is not too high. The follow- ing comparison table PSO simulation results without and with LDIW. Table 1. Comparison of the simulation results PSO Setpoint PSO ixed W = 0.4 PSO with LDIW 0.4W0.9 Rise time Settle time Error Rise ime Settle time Error 40 7.63 27.74 0.26 7.18 15.76 0.18

6. Conclusion

PID controller based on PSO can be applied to a prototype plant temperature control with LM35 temperature sensor as a feedback control system through a port con- nected arduino serial ADC integrated with Matlab-Simulink. Optimization results ex- pelled through PWM port arduino to control the heater and blower, so that the deined set point temperature can be achieved steady state. On set point temperature of 40 o C, the average value obtained error 0.18, com- pared with manual PID tuning is 0.78 and the PID-PSO ixed inertia 12.26. The use of PID controller based on PSO with LDIW method shows faster perfor- mance, with rise time 7.18 ms, settle time 15.76 ms compared with the results of the PID control - PSO ixed Inertia, rise time is reached at 7.63 ms, settle time 27.74 ms. References [1] A. Hasyim, “Alat Pengatur Suhu Air dan Keluaran Udara Sebagai Penggerak Air Pada Ember Penetas Telur Ikan Gurameh Berbasis Mikrokontroler,” Universitas Negeri Yogyakarta, 2013. [2] A. Binsar Parmonangan, “Pengatur Suhu Ruangan Otomatis Dengan Sensor Suhu Berbasis Mikrokontroler Atmega 8535,” Fakultas Ilmu Komputer Teknologi Informasi, Universitas Gunadarma, Jakarta, 2014. [3] P. Singhala, D.. Shah, and B. Patel, “Temperature Control using Fuzzy Logic,” Int. J. Instrum. Control Syst., vol. 4; 1, pp. 1–10, Jan. 2014. [4] K. H. Ang, G. Chong, and Y. Li, “PID control system analysis, design, and technology,” IEEE Trans. Control Syst. Technol. , vol. 13, no. 4, pp. 559–576, Jul. 2005. [5] H. Fachrudin, I. Robandi, and N. Sutantra, “Model and Simulation of Vehicle Lateral Stability Control,” presented at the 2nd APTECS, 2010, International Seminar on Applied Technology, Science, and Arts, Surabaya, ITS, 2010, p. 26. [6] R. Eberhart and J. Kennedy, “A new optimizer using particle swarm theory,” in, Proceedings of the Sixth International Symposium on Micro Machine and Human Science, 1995. MHS ’95 , 1995, pp. 39–43. [7] H. Fachrudin, I. Robandi, and N. Sutantra, “Modeling and Simulation digilib.uinsby.ac.id digilib.uinsby.ac.id digilib.uinsby.ac.id digilib.uinsby.ac.id digilib.uinsby.ac.id digilib.uinsby.ac.id digilib.uinsby.ac.id 88 Of Vehicle Stability Control On Steer By Wire System Using Fuzzy Logic Control And PID Control Tuned By PSO,” in 3rd International Conference on Engineering and ICT ICEI2012 , Melaka, Malaysia, 2012, p. 85. [8] H. Fachrudin, I. Robandi, and N. Sutantra, “Optimization of automatic steering control on a vehicle with a steer-by-wire system using particle swarm optimization,” Turk. J. Electr. Eng. Comput. Sci. , vol. 24; 2, pp. 541– 557, Feb. 2016. [9] Alrijadjiz, “Implementasi Metode PSO- LDW untuk Optimasi Kontroler PID Pada Plant Orde Tinggi,” Politeknik Elektronika Negeri Surabaya-Institut Teknologi Sepuluh Nopember ITS, Surabaya, 2010. [10] A. E. A. Awouda and R. Bin Mamat, “Reine PID tuning rule using ITAE criteria,” in 2010 The 2nd International Conference on Computer and Automation Engineering ICCAE , 2010, vol. 5, pp. 171–176. [11] J. Kennedy and R. Eberhart, “Tutorial on Particle Swarm Optimization,” presented at the IEEE Swarm Intelligence Symposium 2005, Pasadena, California USA, 2005. [12] J. Kennedy and R. Eberhart, “Particle swarm optimization,” presented at the, IEEE International Conference on Neural Networks, 1995., University of Western Australia, Perth, Western Australia, 1995, vol. 4, pp. 1942 –1948. Fachrudin Hunaini re- ceived the B.Sc. degree in Electrical Engineering from the University of Widyagama, Malang, Indonesia, in 1991 and M, Eng., degree in Electri- cal Engineering from Sepuluh Nopember Institute of Technolgy, Surabaya, Indonesia in 1999. At this time as a candidate Dr.Eng. in Electrical Engineering at Sepuluh Nopember Institute of Technolgy, Surabaya, Indonesia. The current research focused on optimal control system-based on behavior on the steering of vehicles using Steer-by-wire system. Resa Dian Pradikta re- ceived the B.Sc. degree in Elec- trical Engineering from the Uni- versity of Widyagama, Malang, Indonesia, in 2016 and The cur- rent research focused on opti- mal control system-based on be- havior on Temperature optimal control system. Imam Robandi , He recived B.Sc. degree in power engineer- ing from Sepuluh Nopember In- stitute of Technolgy, Surabaya, Indonesia in 1989, and M, Eng., degree in Electrical Enginering from the Bandung Institute of Technology, Indonesia in 1994 and Dr.Eng. de- gree in the Department of Electrical Engineering from Tottori University, Japan, 2002. He is cur- rently Professor and as Chairman of the Labora- tory of Power System Operation and Control in the Department of Electrical Engineering, Sepu- luh Nopember Institute of Technology, Surabaya, Indonesia. His current reasearch interest includes Stability of power systems, Electric Car, Solar cell and Artiicial Iintelegent Control.