Optimization & Embedded Systems

Optimization

• Linear optimization
• Non-linear optimization (gradient method, optimal step gradient, Lagrange multipliers)
• Optimization applied to robotics (optimal control based on quadratic programming under linear constraints)

On-board system

• Harvard & Von Neumann architectures
• Knowledge and implementation of the main features of a microcontroller
• Choice and sizing of an embedded programming solution for a given need
• Programming a Raspberry Pi board in C
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• Optimization

  • Linear optimization
  • Non-linear optimization (gradient descent, Lagrange multipliers)
  • Applying optimization in robotics (optimal control based on quadratic programming under linear constraints)

  Embedded Systems

  • Harvard & Von Neumann Architectures
  • Knowledge and implementation of the main functions of a microcontroler
  • Choice and implementation of an embedded programming solution adapted to given design specifications
  • C Programming on a Raspberry Pi

   

Optimization part: at the end of the course, students will be able to properly formulate an optimization problem and propose the most appropriate tools to solve it.

Embedded systems part: at the end of the course, students will be able to choose and implement an embedded programming solution for a given need.

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Optimization: at the end of the course, the students will know how to formulate an optimization problem and propose the most appropriate tools for solving it.

Embedded Systems: at the end of the course, the students will know how to choose and implement an embedded programming solution, given the design specifications.

Contact Hours:

            Taught lectures: 15 hours

            Laboratory Practicals: 27 hours

Programming in C, linear algebra, mathematical analysis.

Recommended prerequisites*:

Programming in Python.

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C Programming, linear Algebra, Calculus.

Reccommended prerequisites: Python Programming. 

CM : 15h

Practical work : 27h

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Taught lectures: 15 hours

Laboratory Practicals: 27 hours