Introduction to AI/ML Motion Control

Introduction to AI/ML Motion Control udemy course free download

Focusing on RL Autonomous Systems

This course extends a Udemy course with over 10k students, "Introduction to Mechatronics, including AI/ML features", which summarizes a Graduate course taught by the author at Stony Brook University.  This course introduces an exciting topic of AI/ML motion control, based on SAAR Inc. few years of activity in AI/ML mechatronics. This course is intended for engineers, scientists, marketing, business and investors with interest in autonomous product development for virtually any field. It explains the basic principles of commonly used Supervised Learning (SL) Labeled Datasets. It highlights the more difficult topic of Reinforcement Learning (RL) as used in autonomous motion control systems.  It explains how RL maximizes a Reward function based on Static, Kinematic, Dynamic, and Motion Control simulation, with sensed State of force, position, velocity, sound and image sensors, all as an input to the Neural Network (NN) Controller. The course illustrates how the NN is mathematically being trained, and after being trained, how the output Actions of the motion control drive the system actuators in an optimal Rewarded way. The course highlights the similarity to an old PID motion control, which dominates existing automation products. The course describes the technology pyramid of Mechanical, Electrical, Software, System Analysis, AI/ML, Data Storage and IoT communication, jointly with market, business and investment needs, which move new autonomous systems towards Technological Singularity.  Finally, the course highlights key companies which sell RL products, and those who provide AI/ML training tools and data storage clouds, for global IoT deployment of trained NN controllers into automation products all over the world.