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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
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UDACITY40
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
Save this course
Save Additional Content: Control to your list so you can find it easily later:
Save
Save
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Additional Content: Control with these
activities:
Review Linear Algebra Skills
Show steps
Refresh your understanding of essential linear algebra skills before starting the course to enhance your comprehension of vehicle dynamics.
Browse courses on
Linear Algebra
Show steps
-
Go over your notes or review online resources on basic linear algebra concepts
-
Solve practice problems to test your understanding
-
Attend a refresher session or workshop on linear algebra
Form a Study Group for Vehicle Control Systems
Show steps
Collaborate with classmates to form a study group focused on vehicle control systems, fostering a supportive learning environment.
Browse courses on
Control Systems
Show steps
-
Identify classmates interested in forming a study group
-
Establish regular meeting times and locations
-
Create a study schedule and assign roles
-
Work together to review course material, solve problems, and prepare for assessments
Develop a Kinematic Vehicle Model
Show steps
Creating a kinematic vehicle model will solidify your understanding of vehicle dynamics and kinematics.
Browse courses on
Vehicle Modeling
Show steps
-
Research the concepts of kinematics and vehicle dynamics.
-
Gather data on the vehicle's dimensions and parameters.
-
Develop the mathematical equations for the kinematic model.
-
Implement the model in a simulation environment.
-
Validate the model by comparing its predictions to real-world data.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Solve Differential Equations Practice Problems
Show steps
Strengthen your problem-solving skills by practicing differential equations from various sources.
Browse courses on
Differential Equations
Show steps
-
Find practice problems from textbooks, online resources, or previous assignments
-
Work through the problems step-by-step
-
Check your solutions against provided answers or consult with classmates or instructors
Attend a Workshop on Model Predictive Control
Show steps
Enhance your knowledge by attending a workshop specifically focused on Model Predictive Control, a key topic in this course.
Show steps
-
Research and identify relevant workshops on Model Predictive Control
-
Register for the workshop
-
Attend the workshop and actively participate
-
Take notes and engage in discussions
Design and Simulate a Model Predictive Controller
Show steps
Designing and simulating a model predictive controller will provide you with hands-on experience with advanced control techniques.
Show steps
-
Define the control objectives and constraints.
-
Develop the optimization algorithm for the MPC.
-
Implement the MPC in a simulation environment.
-
Tune the MPC parameters to achieve optimal performance.
-
Evaluate the performance of the MPC through simulations.
Develop a Summary of Linear Algebra Concepts
Show steps
Enhance your understanding by creating a concise summary of key linear algebra concepts covered in the course.
Browse courses on
Linear Algebra
Show steps
-
Review your notes and identify the main concepts
-
Organize the concepts into a logical structure
-
Write clear and concise summaries for each concept
-
Include examples and illustrations to support your explanations
Explore Advanced Vehicle Dynamics Tutorials
Show steps
Expand your knowledge by exploring advanced tutorials and resources on vehicle dynamics to complement the course material.
Browse courses on
Vehicle Dynamics
Show steps
-
Identify reputable sources for vehicle dynamics tutorials
-
Select tutorials that align with your learning goals
-
Follow the tutorials step-by-step
-
Work through the exercises and examples provided
Build a Simulation Model of a Vehicle System
Show steps
Apply your knowledge by creating a simulation model of a vehicle system to test and validate your understanding.
Browse courses on
Vehicle Modeling
Show steps
-
Define the scope and objectives of your simulation model
-
Choose appropriate software and tools for the simulation
-
Develop the mathematical model for the vehicle system
-
Implement the model in the simulation software
-
Validate and refine the simulation model
Review Linear Algebra Skills
Show steps
Refresh your understanding of essential linear algebra skills before starting the course to enhance your comprehension of vehicle dynamics.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Go over your notes or review online resources on basic linear algebra concepts
- Solve practice problems to test your understanding
- Attend a refresher session or workshop on linear algebra
Form a Study Group for Vehicle Control Systems
Show steps
Collaborate with classmates to form a study group focused on vehicle control systems, fostering a supportive learning environment.
Browse courses on
Control Systems
Show steps
Show steps
Show steps
- Identify classmates interested in forming a study group
- Establish regular meeting times and locations
- Create a study schedule and assign roles
- Work together to review course material, solve problems, and prepare for assessments
Develop a Kinematic Vehicle Model
Show steps
Creating a kinematic vehicle model will solidify your understanding of vehicle dynamics and kinematics.
Browse courses on
Vehicle Modeling
Show steps
Show steps
Show steps
- Research the concepts of kinematics and vehicle dynamics.
- Gather data on the vehicle's dimensions and parameters.
- Develop the mathematical equations for the kinematic model.
- Implement the model in a simulation environment.
- Validate the model by comparing its predictions to real-world data.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Solve Differential Equations Practice Problems
Show steps
Strengthen your problem-solving skills by practicing differential equations from various sources.
Browse courses on
Differential Equations
Show steps
Show steps
Show steps
- Find practice problems from textbooks, online resources, or previous assignments
- Work through the problems step-by-step
- Check your solutions against provided answers or consult with classmates or instructors
Attend a Workshop on Model Predictive Control
Show steps
Enhance your knowledge by attending a workshop specifically focused on Model Predictive Control, a key topic in this course.
Show steps
Show steps
Show steps
- Research and identify relevant workshops on Model Predictive Control
- Register for the workshop
- Attend the workshop and actively participate
- Take notes and engage in discussions
Design and Simulate a Model Predictive Controller
Show steps
Designing and simulating a model predictive controller will provide you with hands-on experience with advanced control techniques.
Show steps
Show steps
Show steps
- Define the control objectives and constraints.
- Develop the optimization algorithm for the MPC.
- Implement the MPC in a simulation environment.
- Tune the MPC parameters to achieve optimal performance.
- Evaluate the performance of the MPC through simulations.
Develop a Summary of Linear Algebra Concepts
Show steps
Enhance your understanding by creating a concise summary of key linear algebra concepts covered in the course.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Review your notes and identify the main concepts
- Organize the concepts into a logical structure
- Write clear and concise summaries for each concept
- Include examples and illustrations to support your explanations
Explore Advanced Vehicle Dynamics Tutorials
Show steps
Expand your knowledge by exploring advanced tutorials and resources on vehicle dynamics to complement the course material.
Browse courses on
Vehicle Dynamics
Show steps
Show steps
Show steps
- Identify reputable sources for vehicle dynamics tutorials
- Select tutorials that align with your learning goals
- Follow the tutorials step-by-step
- Work through the exercises and examples provided
Build a Simulation Model of a Vehicle System
Show steps
Apply your knowledge by creating a simulation model of a vehicle system to test and validate your understanding.
Browse courses on
Vehicle Modeling
Show steps
Show steps
Show steps
- Define the scope and objectives of your simulation model
- Choose appropriate software and tools for the simulation
- Develop the mathematical model for the vehicle system
- Implement the model in the simulation software
- Validate and refine the simulation model
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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