Introduction To Ai/Ml Motion Control
Published 7/2025
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 776.25 MB | Duration: 1h 33m
Published 7/2025
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 776.25 MB | Duration: 1h 33m
Focusing on RL Autonomous Systems
What you'll learn
List the benefits of AI/ML automation products, in comparison with classical PID automation products
Identify the differences between new AI automation product and common PID motion-controlled product
Understand the required team and the development process of AI/ML automation products
Plan objectives, action items, resources and cost of a simple startup RL motion-controlled product
Requirements
Interest in next generation development process of autonomous AI/ML mechatronics products
Experience in automation, such as, education, engineering, marketing, design, manufacturing and application
Description
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.
Overview
Section 1: Introduction
Lecture 1 Motivation
Lecture 2 Who is using RL motion control
Section 2: Motion Control
Lecture 3 The AI pyramid of Values
Lecture 4 Types of AI motion Control
Lecture 5 PID motion Control
Lecture 6 RL System Blocks
Lecture 7 The difference between PID and RL
Section 3: Development Tools
Lecture 8 System Analysis
Lecture 9 Reward Function
Lecture 10 RL Agents, Critics, Actors
Lecture 11 NN Optimization, Belman Equation
Section 4: Conclusion
Lecture 12 Deployment of NN
Lecture 13 RL Startup Options
Mechatronics engineers ME, EE, CS, System Analysts, who are excited to start designing AI/ML motion control,Business, Marketing and Investment managers in an automation business, who value AI/ML in their market,Industrial, healthcare, agriculture, semiconductor, electronic equip' manufacturers who consider AI in their tools,Professors, teachers and students in high schools, colleges and universities who plan AI/ML courses and labs