Design of PID-fuzzy for speed control of brushless DC motor in dynamic electric vehicle to improve steady-state performance

Jaya, Arman; Purwanto, Era; Fauziah, Melinda Badriatul; Murdianto, Farid Dwi; Prabowo, Gigih; Rusli, Muhammad Rizani

doi:10.1109/elecsym.2017.8240399

Cited by 47 publications

(18 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…PID control is composed of 3 main parameters, namely proportional (P), integral (I), and derivative (D) [12]. PID control has the characteristics of increasing the rise time, reducing the steady-state error, and reducing oscillations [7]. The block diagram of the PID control can be seen in the following figure: Where Kp is proportional gain, Kd is derivative gain and Ki is integral gain.…”

Section: Pid Controlmentioning

confidence: 99%

See 1 more Smart Citation

Fuzzy Logic Controller and Its Application in Brushless DC Motor (BLDC) in Electric Vehicle - A Review

Apribowo

Ahmad

Maghfiroh

2021

JEEICT

View full text Add to dashboard Cite

<p class="Abstract">Brushless DC motor (BLDC) is one type of electric motor that is widely used, especially in automotive systems. This motor is widely used as a driving force in electric vehicles. BLDC motor is chosen because it has the characteristics of high efficiency, reliability, and a wide speed range. Besides, BLDC motors require less maintenance and can operate quieter than DC motors. Even though it has many advantages, in its application the use of BLDC motors in electric vehicles is often less than optimal. The use of a conventional control system proportional integral derivative (PID) still has many weaknesses, especially in response to changes in load and track conditions. In this study, a control system was designed to regulate the speed of the BLDC motor, using a combination of Fuzzy and PID methods. Based on the results of the tests that have been done, the Fuzzy-PID control can provide better and more stable performance than using the conventional PI control.</p>

show abstract

Section: Pid Controlmentioning

confidence: 99%

“…Even though it has many advantages, in its application the use of BLDC motors in electric vehicles is often less than optimal. Variations in setpoint and dynamic load conditions must be considered in electric vehicles [7]. In conventional control systems, these two aspects cannot be applied in controlling electric vehicles.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Logic Controller and Its Application in Brushless DC Motor (BLDC) in Electric Vehicle - A Review

Apribowo

Ahmad

Maghfiroh

2021

JEEICT

View full text Add to dashboard Cite

show abstract

“…Motor DC Brushless mempunyai tiga fase permanen magnet motor yang membutuhkan tegangan DC sebagai suplai (Jaya & Fauziah, 2017). Motor DC tanpa sikat (BLDC) menggunakan bahan semikonduktor untuk mengubah arah putarannya dalam menggerakkan motor (Fathoni & Utomo, 2019).…”

Section: Pendahuluanunclassified

Pengontrolan Kecepatan Rotor BLDC UAV Berdasarkan Hasil Identifikasi menggunakan Metode Regresi

et al. 2021

View full text Add to dashboard Cite

ABSTRAKPenggunaan dan aplikasi motor Brushless DC cukup banyak di industri, namun masih cukup sulit untuk mengendalikannya. Pada penelitian sebelumnya telah dipelajari karakteristik parameter motor BLDC UAV menggunakan Metode Regresi untuk mengetahui hubungan antar parameter yang ada dalam sistem motor BLDC. Sinyal PWM merupakan salah satu yang menentukan kecepatan rotor dari BLDC. Pada penelitian ini identifikasi model motor BLDC hasil eksperimen digunakan untuk mengendalikan kecepatan rotor secara open loop dan closed loop. Pengendalian secara open loop menggunakan invers model hasil Metode Regresi menghasilkan kesalahan maksimal 3,77% untuk kecepatan rotor lebih dari 3500 rpm. Sedangkan pada pengendalian secara closed loop menggunakan model hasil Metode Regresi dan pengendali PI (Proportional Integral) dengan Kp = 1 dan Ki = 5, secara simulasi menghasilkan kecepatan rotor dengan settling time 1 detik.Kata kunci: motor BLDC, kecepatan rotor, identifikasi model, pengendalian ABSTRACTThe use and application of Brushless DC motors is quite a lot in the industry, but it is still quite difficult to control. In previous research the characteristics of UAV BLDC motor parameters using the regression method to determine the relation of the parameters in the BLDC motor system. The PWM signal is one that determines the rotor speed of the BLDC. In this study the identification of the BLDC motor model experimental results is used to control the rotor speed in open loop and closed loop. Control with open loop using the inverse model of the Regression Method produces a maximum error of 3.77% for rotor speeds of more than 3500 rpm. Whereas control with closed loop using the model of the Regression Method and PI (Proportional Integral) controller with Kp = 1 and Ki = 5, the simulation produces rotor speed with a settling time of 1 second.Keywords: BLDC motor, rotor speed, model identification, control

show abstract

“…In addition, several algorithms have been used for speed control of Brushless Direct Current (BLDC) motors but, amongst them, the PID control features ease implementation and avoids the necessity of modelling the actuator, which tends to be troublesome due to the parameter identification. For instance, in [ 27 , 28 ] a PID algorithm combined with fuzzy control was used for reference tracking of the angular velocity of a BLDC motor. In [ 29 ], an adaptive PID control algorithm was designed and in [ 30 ] an adaptive fuzzy PID control scheme was used, both for the same purpose.…”

Section: Introductionmentioning

confidence: 99%

Velocity Sensor for Real-Time Backstepping Control of a Multirotor Considering Actuator Dynamics

Mayorga-Macías

González-Jiménez

Meza-Aguilar

et al. 2020

Sensors

View full text Add to dashboard Cite

A real-time implementation of a control scheme for a multirotor, based on angular velocity sensors for the actuators, is presented. The control scheme is composed of two loops: an inner loop for the actuators and an outer loop for the unmanned aerial vehicle (UAV). The UAV control algorithm is designed by means of the backstepping technique and a robust sliding mode differentiator, and the actuator control strategy is based on a standard proportional-integral-derivative (PID) controller. A robust exact differentiator, based on high order sliding modes, is used to estimate the complex derivatives present in the proposed control law. As the measurements of the propeller’s angular velocities are required for the control law, velocity sensors are mounted in the axles of the rotors to retrieve them and a signal conditioning stage is implemented. In addition, dynamical models for the actuators of the aircraft were calculated by means of transfer functions obtained via experimental measurements in a test bench developed for this purpose. This test bench permits to characterize the parameters of the transfer functions by comparing the forces computed using the nominal parameter to the measured forces. To this end, it is assumed that the loads in the actuators of the vehicle are insignificant during flight. The effectiveness of the proposed sensor, its signal conditioning, and the overall control scheme are validated by means of simulation results and real-time experiments.

show abstract

Design of PID-fuzzy for speed control of brushless DC motor in dynamic electric vehicle to improve steady-state performance

Cited by 47 publications

References 2 publications

Fuzzy Logic Controller and Its Application in Brushless DC Motor (BLDC) in Electric Vehicle - A Review

Fuzzy Logic Controller and Its Application in Brushless DC Motor (BLDC) in Electric Vehicle - A Review

Pengontrolan Kecepatan Rotor BLDC UAV Berdasarkan Hasil Identifikasi menggunakan Metode Regresi

Velocity Sensor for Real-Time Backstepping Control of a Multirotor Considering Actuator Dynamics

Contact Info

Product

Resources

About