Automatic Control, Project Course

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Welcome to the Project Course in Automatic Control

HT2019 - Learning-based MPC and High fidelity simulations together with KTH Formula Student

(See also the smaller 7.5 hp course EL2425

The overall purpose of the course is to give the student practical knowledge and experience of project work in design and implementation of complex control systems. The students get to consolidate their previously aquired knowledge in control theory by solving a real problem on a laboratory system. There is a large focus on independent and self-organized project work. The student also gets experience in project management and technical communication.

After the course the student should have the ability to:

  • work effectively in a larger project group to solve a complex design task
  • analyse and specify requirements of a complex control problem from a systems point-of-view
  • structure and plan the project work and formulate and follow a project plan
  • systematically design, implement, test and demonstrate a prototype control system that meets given specifications
  • use adequate engineering tools and methods and aquire new knowledge and skill when needed
  • pursue own ideas and realize them in practice
  • document and communicate results in writing, orally and graphically

Main content

The majority of the work is project work in a group of 6-10 students, dealing with:

  • project planning
  • requirements specification
  • control design
  • modeling and simulation
  • implementation of real-time control systems
  • validation and testing
  • communication, documentation and demonstration

There will also be a number of compulsory workshops/tutorials, for example on:

  • project management (SCRUM)
  • development tools (ROS, Git)

Project HT 19: Learning-based MPC and High fidelity simulations together with KTH Formula Student

KTH Formula Student (KTHFS) is a nonprofit project where students design, manufacture, test and compete with a car in Formula Student, with the best technical universities all over the world. In the driverless team, they aim to achieve a complete pipeline for an autonomous racing car: Perception (Stereo Camera and 3D Lidar based object detection), SLAM (localization of the vehicle and mapping) and Navigation (path generation and vehicle control).

The aim of the Driverless team, for the 2019/2020 season, will be to improve the current pipeline to optimize the overall performance and the computational requirements. For that to happen, the Navigation subgroup will be running alongside EL2421 Automatic Control, Project Course. The project will have the following focus:

Learning-based Model Predictive Control (MPC) for optimizing path following in a closed circuit

The main goal for the project is to achieve robustness against parameter deviations. The first step in the project would be to extend the kinematic model based MPC to a dynamical bicycle model. In addition learning methods can be applied to achieve better performance. The performance criteria can be freely chosen and could be based on tracking accuracy, lap time,

Simulation based on non-linear dynamic models

First part of this project is understanding the current fully ROS implemented dynamic simulation based on the nonlinear dynamic bicycle with linear tyre forces. This model can then be extended with the nonlinear tyre model based on Pacejka's Magic Tyre Formula. Furthermore, the possibility of developing a ROS node in MATLAB/Simulink for an interface to our IPG Carmaker simulation exists. This approach would provide a high fidelity and state-of-the-art simulation environment for best simulation verification results.

The projects will be finished in the form of a closed implementation which runs in harmony with
the official KTHFS_DV 2018/2019 complete pipeline (along with Perception and SLAM), and the
improvements being measured against the 2018/2019 results.


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