Adaptive position control of DC motor for brush-based Adaptive position control of DC motor for brush-based

Abstract

In this paper, we have developed an automatic brush-based PV cleaning system to control and synchronize the 3 motors together with a smooth periodic of cleaning while moving it horizontally over the PV surface. The mechanical design involved installing linear guides at the top and bottom of the rail to support the aluminium plate that holds the carrier motors and rotating brush. Two different movements of translational and rotational motion of the motors are managed by an algorithm programmed in Arduino Mega. In investigating the performance of motor parameters and dust removal rate, we conducted an experiment by spreading dry sand over the PV surface. Results showed that the torque of the cleaning brush motor increases with the increase in load. The obtained torque of the carrier motor was found to be 9.167 Nm ( stall torque, 9.8 Nm) with a full load of 18 brushes. The torque is inversely proportional to the speed but directly proportional to power. The required power to move the 2.93 kg of cleaning system was 19.20 W with 3.015 Nm of torque. The system achieved 86.8% of the dust removal rate from the four cycles of cleaning operations.

Firefly algorithm tuning of PID position control of DC motor using parameter estimator toolbox

“…Figure 7 shows the block diagram of the PID controller design for this control system. The control signal is determined the by PID equation in (1) to drive the DC motor to achieve a minimum error between a desired angle and an actual angle. In Figure 8, the PWM signal for the DC motor is denoted as u(t), while the actual angle is represented as y(t).…”

“…Nowadays, direct current (DC) motors are widely used in industries, and robotics due to there's excellent speed control characteristics, dependability, and affordability [1]. The speed of a DC motor can be adjusted by changing the voltage at its terminals.…”

Autonomous Drone Electronics Amplified With Pontryagin–Based Optimization

“…Sorfina, et. al, [23] illustrated the impacts of deleterious performances of DC motors resulted in 14% failure rate when utilized for photovoltaic cleaning system automation. Mohanraj, et.…”

Autonomous Drone Electronics Amplified with Pontryagin-Based Optimization

“…Baidya et al [22] utilized a sensor feedback-supported controller based on a novel dandelion optimization PID controller, achieving integral square errors, integral absolute errors, integral errors, integral time square errors, and integral time absolute errors on the order of 10 −2 . Sorfina et al [23] illustrated that the deleterious impact on the performance of DC motors when utilized for photovoltaic cleaning system automation resulted in 14% failure rate. Mohanraj et al [24] illustrated many advantages, including independent variable speed and variable torque operation along with regeneration capability when using DC motor-based electric drive systems by considering road friction, aerodynamic forces and transmission in the calculation of the motor shaft torque using model predictive control and proportional integral control, while only being able to assert the scheme makes significant energy capture possible.…”