Difference between revisions of "EECI 2020: Computer Session: TuLiP"

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** 6 cell robot, with dynamics: [http://www.cds.caltech.edu/~murray/courses/eeci-sp2020/robot_simple_continuous.ipynb robot_simple_continuous.ipynb]
 
** 6 cell robot, with dynamics: [http://www.cds.caltech.edu/~murray/courses/eeci-sp2020/robot_simple_continuous.ipynb robot_simple_continuous.ipynb]
 
** 3x3 exercise: [http://www.cds.caltech.edu/~murray/courses/eeci-sp2020/exercise_3x3.ipynb exercise_3x3.ipynb]
 
** 3x3 exercise: [http://www.cds.caltech.edu/~murray/courses/eeci-sp2020/exercise_3x3.ipynb exercise_3x3.ipynb]
** Left turn exercise: [http://www.cds.caltech.edu/~murray/courses/eeci-sp2020/exercise_leftturn.py exercise_leftturn.ipynb]
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** Left turn exercise: [http://www.cds.caltech.edu/~murray/courses/eeci-sp2020/exercise_leftturn.ipynb exercise_leftturn.ipynb]
  
 
== Further Reading ==
 
== Further Reading ==

Revision as of 18:47, 18 March 2020

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This lecture provides an overview of TuLiP, a Python-based software toolbox for the synthesis of embedded control software that is provably correct with respect to a GR[1] specifications. TuLiP combines routines for (1) finite state abstraction of control systems, (2) digital design synthesis from GR[1] specifications, and (3) receding horizon planning. The underlying digital design synthesis routine treats the environment as adversary; hence, the resulting controller is guaranteed to be correct for any admissible environment profile. TuLiP applies the receding horizon framework, allowing the synthesis problem to be broken into a set of smaller problems, and consequently alleviating the computational complexity of the synthesis procedure, while preserving the correctness guarantee.

A brief overview of TuLiP will be followed by hands-on exercises using the toolbox.

Lecture Materials

Further Reading

Additional Information