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Additional Content
Control
David Silver,
Thomas Hossler,
Antje Muntzinger,
Andreas Haja,
Aaron Brown,
Munir Jojo Verge,
and
Mathilde Badoual
David Silver,
Thomas Hossler,
Antje Muntzinger,
Andreas Haja,
Aaron Brown,
Munir Jojo Verge,
and
Mathilde Badoual
Find additional content here on Vehicles Models and Model Predictive Control, a more advanced form of control.
Register for this course and see more details by visiting:
OpenCourser.com/course/qtlmql/additional
What's inside
Syllabus
In this lesson, you'll learn about kinematic and dynamic vehicle models. We'll use these later with Model Predictive Control.
In this lesson, you'll learn how to frame the control problem as an optimization problem over time horizons. This is Model Predictive Control!
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Focuses on vehicles models and Model Predictive Control (MPC), which are highly relevant to engineers working in the automotive industry
Led by a team of renowned experts in the field, including David Silver, a pioneer in reinforcement learning
Provides hands-on experience through interactive labs and simulations
May not be suitable for beginners, as it requires some prior knowledge of control theory and programming
The course duration is not specified, which may be a concern for learners with limited time
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Activities
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Additional Content: Control.
These are activities you can do either before, during, or after a course.
Career center
Learners who complete Additional Content: Control will develop knowledge and skills
that may be useful to these careers:
Autonomy Engineer
Autonomy Engineer
Autonomy Engineers design, develop, and test autonomous vehicles and systems. This course may be useful for Autonomy Engineers who want to learn more about vehicle models and Model Predictive Control, which can be used to optimize the performance of autonomous vehicles and systems.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and maintain machine learning models and systems. This course may be useful for Machine Learning Engineers who want to learn more about vehicle models and Model Predictive Control, which can be used to optimize the performance of machine learning models and systems.
Vehicle Dynamics Engineer
Vehicle Dynamics Engineer
Vehicle Dynamics Engineers design, develop, and test vehicles to ensure that they meet performance and safety requirements. This course may be useful for Vehicle Dynamics Engineers who want to learn more about vehicle models and Model Predictive Control, which can be used to optimize the performance of vehicles.
Data Scientist
Data Scientist
Data Scientists use data to solve business problems. This course may be useful for Data Scientists who want to learn more about vehicle models and Model Predictive Control, which can be used to optimize the performance of data-driven models.
Systems Engineer
Systems Engineer
Systems Engineers design, develop, and maintain systems that integrate multiple components into a single, functioning system. This course may be useful for Systems Engineers who want to learn more about vehicle models and Model Predictive Control, which can be used to optimize the performance of systems.
Simulation Engineer
Simulation Engineer
Simulation Engineers design, develop, and maintain simulations that are used to predict the behavior of systems and processes. This course may be useful for Simulation Engineers who want to learn more about vehicle models and Model Predictive Control, which can be used to optimize the performance of simulations.
Robotics Engineer
Robotics Engineer
Robotics Engineers design, develop, and maintain robots and robotic systems. This course may be useful for Robotics Engineers who want to learn more about vehicle models and Model Predictive Control, which can be used to optimize the performance of robots and robotic systems.
Validation Engineer
Validation Engineer
Validation Engineers ensure that products and systems meet customer requirements. This course may be useful for Validation Engineers who want to learn more about vehicle models and Model Predictive Control, which can be used to optimize the performance of products and systems.
Control Systems Engineer
Control Systems Engineer
Control Systems Engineers design, develop, and maintain control systems for a variety of applications, including industrial automation, robotics, and aerospace. This course may be useful for Control Systems Engineers who want to learn more about vehicle models and Model Predictive Control, which can be used to optimize the performance of control systems.
Mechatronics Engineer
Mechatronics Engineer
Mechatronics Engineers design, develop, and maintain systems that combine mechanical, electrical, and computer engineering principles. This course may be useful for Mechatronics Engineers who want to learn more about vehicle models and Model Predictive Control, which can be used to optimize the performance of mechatronic systems.
Test Engineer
Test Engineer
Test Engineers design, develop, and conduct tests to ensure that products and systems meet specifications. This course may be useful for Test Engineers who want to learn more about vehicle models and Model Predictive Control, which can be used to optimize the performance of tests.
Mechanical Engineer
Mechanical Engineer
Mechanical Engineers design, develop, and maintain mechanical systems, including engines, machines, and structures. This course may be useful for Mechanical Engineers who want to learn more about vehicle models and Model Predictive Control, which can be used to optimize the performance of mechanical systems.
Electrical Engineer
Electrical Engineer
Electrical Engineers design, develop, and maintain electrical systems, including power generation, transmission, and distribution systems. This course may be useful for Electrical Engineers who want to learn more about vehicle models and Model Predictive Control, which can be used to optimize the performance of electrical systems.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. This course may be useful for Software Engineers who want to learn more about vehicle models and Model Predictive Control, which can be used to optimize the performance of software systems.
Automotive Engineer
Automotive Engineer
Automotive Engineers design, develop, and test vehicles, and they may specialize in a particular area, such as powertrain, chassis, or electronics. This course may be useful for Automotive Engineers who want to learn more about vehicle models and Model Predictive Control, which can be used to optimize vehicle performance and safety.
For more career information including salaries, visit:
OpenCourser.com/course/qtlmql/additional
Reading list
We've selected 12 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Additional Content: Control.
Save
Good resource for understanding the basics of vehicle dynamics. It covers topics such as kinematics, dynamics, and control, which are important for understanding this course.
Classic reference on optimal control theory. It covers the fundamentals of optimal control, which are used in this course.
Save
Comprehensive reference on nonlinear control systems. It covers topics such as Lyapunov stability, feedback linearization, and nonlinear observers, which are used in this course.
Widely-used textbook on feedback control. It covers the fundamentals of feedback control, which are used in this course.
Commonly used textbook in control engineering. It covers the fundamentals of control theory, which are used in this course.
Provides an introduction to vehicle system dynamics. It covers topics such as vehicle modeling, simulation, and control, which are relevant to this course.
Widely-used textbook on control systems engineering. It covers the fundamentals of control theory, which are used in this course.
Provides an overview of automotive control systems. It covers topics such as engine control, transmission control, and brake control, which are relevant to this course.
Provides an introduction to electric vehicle technology. It covers topics such as electric motors, batteries, and charging systems, which are relevant to this course.
Comprehensive reference on automotive electronics. It covers topics such as sensors, actuators, and controllers, which are used in this course.
Provides an introduction to car hacking. It covers topics such as reverse engineering, software exploitation, and security vulnerabilities, which are relevant to this course.
Provides an introduction to computer vision for autonomous vehicles. It covers topics such as image processing, object detection, and scene understanding, which are relevant to this course.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/qtlmql/additional
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Time
120 hours
Level
Advanced
Via
Udacity
Instructors
David Silver
Thomas Hossler
Antje Muntzinger
Andreas Haja
Aaron Brown
Munir Jojo Verge
Mathilde Badoual
Language
English
Good to know
Focuses on vehicles models and Model Predictive Control (MPC), which are highly relevant to engineers working in the automotive industry
Led by a team of renowned experts in the field, including David Silver, a pioneer in reinforcement learning
Provides hands-on experience through interactive labs and simulations
May not be suitable for beginners, as it requires some prior knowledge of control theory and programming
The course duration is not specified, which may be a concern for learners with limited time
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