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Lecture 6.1 - Principles of PMSM Control
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Introduction to Robotics
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- 1 Introduction - Introduction to Robotics
- 2 Lecture 1.1 - Introduction
- 3 Lecture - 1.2 - Evolution of Robotics
- 4 Lecture - 2 .1 - Kinematics- Coordinate transformations
- 5 Lecture - 2.2 - Homogeneus Transformation Matrix
- 6 Lecture - 2.3 - Industrial Robot- Kinematic Structures
- 7 Lecture - 2.4 - Robot Architectures
- 8 Lecture - 2.5 - Kinematic Parameters
- 9 Lecture - 2.6 - DH Algorithm
- 10 Lecture - 2.7 - DH Algorithm
- 11 Lecture - 2.8 - Forward Kinematics
- 12 Lecture - 2.9 - Forward Kinematics- Examples
- 13 Lecture - 2.10 -Inverse Kinematics
- 14 Lecture - 2.11 - Inverse Kinematics- Examples
- 15 Lecture - 2.12 - Differential Relations
- 16 Lecture - 2.13 - Manipulator Jacobian and Statics
- 17 Lecture - 3.1 Overview of Electric Actuators and Operational Needs
- 18 Lecture 3.2 - Principles of DC Motor Operation
- 19 Lecture 3.3 - DC Motor Equations and Principles of Control
- 20 Lecture 4.1 - DC Motor Control Regions and Principles of Power Electronics
- 21 Lecture 4.2 - Power Electronic Switching and Current Ripple
- 22 Lecture 4.3 - The H-Bridge and DC Motor Control Structure
- 23 Lecture 5.1 - The Brushless DC Machine
- 24 Lecture 5.2 - Control of the Brushless DC Motor
- 25 Lecture 5.3 - The PM Synchronous Motor (PMSM) and SPWM
- 26 Lecture 6.1 - Principles of PMSM Control
- 27 Lecture 6.2 - Encoders for Speed and Position Estimation
- 28 Lecture 6.3 - Stepper Motors
- 29 Lecture 7.1 - Introduction to Probabilistic Robotics.
- 30 Lecture 7.2 - Recursive State Estimation: Bayes Filter
- 31 Lecture 7.3 - Recursive State Estimation: Bayes Filter Illustration.
- 32 Tutorial - 1 Probability Basics
- 33 Tutorial - 2 Probability Basics
- 34 Lecture 8.1 - Kalman Filter
- 35 Lecture 8.2 - Extended Kalman Filter
- 36 Lecture 8.3 - Particle Filter
- 37 Lecture 8.4 - Binary Bayes
- 38 Lecture - 9.1 Velocity Motion Model
- 39 Lecture - 9.2 Odometry Motion Model
- 40 Lecture - 9.3 Occupa Grid Mapping
- 41 Lecture 9.4 - Range Finder Measurement Model
- 42 Lecture 10.1 - Localization Taxonomy
- 43 Lecture 10.2 - Markov Localization
- 44 Lecture 10.3 - Path Planning
