![]() ![]() This link give an illustration of impact of Kp, Ki and Kd on system Therefore, we usually use an acceptable gain. Finding the best PID gains is difficult task. Each system has its own optimal PID gain. Performance of PID is decided by Kp, Ki and Kd gain (coefficients for the proportional, integral, and derivative terms). What decides performance of a PID controller? In this article, I focus on using PID to control position and speed of Brushed DC motor. ![]() It is a controller that is widely used in industrial to control various process variables such as temperature, pressure, force, feed rate, flow rate, chemical composition (component concentrations), weight, position, speed, and practically every other variable for which a measurement exists. ![]() Have you ever heard about PID controller? This article provides libraries and examples code of controlling position and speed of DC motor using PID controller and auto-tuning. ![]()
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![]() ![]() To further tune the training hyperparameters, genetic algorithms (GA) and particle swarm optimization (PSO) were employed. Furthermore, by adjusting the number of episodes, steps per episode, learning rate, and discount factor, a performance study of several RL algorithms was carried out. The homogeneous transformation technique was used to further convert the pixel values into robot coordinates. ![]() By positioning the camera in an eye-to-hand position, this work used color-based segmentation to identify the locations of obstacles, start, and goal points. For vision-based path planning and obstacle avoidance in assembly line operations, this study introduces various Reinforcement Learning (RL) algorithms based on discrete state-action space, such as Q-Learning, Deep Q Network (DQN), State-Action-Reward- State-Action (SARSA), and Double Deep Q Network (DDQN). Reinforcement learning techniques can be used in cases where there is a no environmental map. Despite providing precise waypoints, the traditional path planning algorithm requires a predefined map and is ineffective in complex, unknown environments. Path planning for robotic manipulators has proven to be a challenging issue in industrial applications. ![]() |
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