Open AccessImplementasi Robot Keseimbangan Beroda Dua Berbasis Mikrokontroler
Author(s) -
Grace Bobby,
Erwin Susanto,
Fiky Yosef Suratman
Publication year - 2015
Publication title -
elkomika
Language(s) - English
Resource type - Journals
eISSN - 2459-9638
pISSN - 2338-8323
DOI - 10.26760/elkomika.v3i2.142
Subject(s) - control theory (sociology) , kalman filter , pid controller , fuzzy logic , gyroscope , computer science , controller (irrigation) , accelerometer , control engineering , engineering , artificial intelligence , control (management) , biology , temperature control , agronomy , aerospace engineering , operating system
ABSTRAKPerkembangan dunia robot berkembang pesat dari tahun ke tahun. Salah satu contohnya ialah Segway Personal Transporter. Variasi teknik dalam pergerakan robot pada lingkungan yang dinamik pun semakin banyak, diantaranya Pole-Placement Controller, Fuzzy Logic, Proportional Integrated Derivative Controller (Kontrol PID). Pada penelitian ini Fuzzy Logic akan digunakan sebagai pengontrol robot keseimbangan ini. Pada sistem ini digunakan dua sensor (accelerometer dan gyroscope) untuk mendapatkan pembacaan data yang stabil dan handal. Dari hasil percobaan kalman filter, diperoleh nilai parameter kalman filter yang optimal adalah Qaccelerometer = 0,001 , Qgyroscope = 0,003 dan Rpengukuran = 0,03.Kata kunci: Accelerometer, Gyroscope, Fuzzy Logic, Kalman Filter, Self-balancing Control. ABSTRACTThe development of robots is growing rapidly from year to year. One example is the Segway Personal Transporter. A variety of techniques in the movement of the robot in the dynamic environment became more numerous, including Pole-Placement Controller, Fuzzy Logic, Proportional Integrated Derivative Controller (PID control). In this project, Fuzzy Logic will be used as an balancing robot controller. In this system, used two sensors (accelerometer and gyroscope) to obtain data readout is stable and reliable. From the experimental of Kalman filter, obtained the optimal parameter values of Kalman filter are Qaccelerometer = 0.001, Qgyroscope = 0.003 and Rmeasure = 0.03.Keywords: Accelerometer, Gyroscope, Fuzzy Logic, Kalman Filter, Self-balancing Control.