Propeller Speed Control System on Autonomous Quadcopter with Variations in Load Fulcrum Point

The need for unmanned vehicles is increasingly needed in certain conditions, such as distribution of disaster supply, distribution of medicines, distribution of vaccines in the affected areas in pandemic situations. The various types of goods to be distributed require a different fulcrum. This research implemented PID control for the quadcopter balance control system to achieve stability during hovering. PID control is used to achieve a certain setpoint to produce the required PWM output for the propeller to reach a speed that can fly the quadcopter tilted until it reaches a steady state. Tests were carried out on the roll and pitch motion of the quadcopter by providing a load. The results show that PID control can be implemented for the quadcopter balance control system during hovering by determining the PID constants for each roll and pitch motion with the constanta of Kp = 0.15, Kd = 0.108, and Ki = 0.05. The quadcopter takes 3 – 6 seconds to return to the 0 degree setpoint when it is loaded.

Measurement, Modeling, and Optimization Speed Control of BLDC Motor Using Fuzzy-PSO Based Algorithm

Measurement, modeling, and optimization are three important components that must be done to get a better system on the BLDC motor speed control system. The problem that arises in the BLDC motor speed control system is the instability indicated by a high overshoot value, a slow rise time value, and a high error steady-state. The purpose of this study is to increase the stability indicator by eliminating the high value of overshoot and error steady-state and increasing the value of the rise time. The method used in this research is to measure the input and output physical parameters, to model the BLDC motor plant mathematically and the last is to perform optimization using several control methods such as Proportional Integral Derivative (PID) control, fuzzy logic intelligent control, and Particle Swarm Optimization algorithm. (PSO). Experimental and simulation results show that the PSO algorithm has a better value in increasing stability indicators when compared to the other two control methods with a rise time of 0.00121 seconds, settling time of 0.00241 seconds, and overshoot of 0%.