Add optimization to flat systems trajectory generation #569
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This commit allows a cost function and constraints to be passed to the flatsys point_to_point() function and when present will use sp.optimize to find a trajectory. Preliminary unit tests, benchmarks, docstrings and documentation also in place. Switched the order of arguments in point_to_point() so that Tf (or timepts) now comes before the initial and final states, consistent with ordering elsewhere in the package. Make some small updates to optimal.py docstrings and argument names to make things consistent with flatsys.
This commit changes the way that cost functions and constraints are handled for flat system to carry out optimization only in the null space of the flat system basis coefficients, eliminating the use of an equality constraint for the terminal condition.
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I am not in a position to evaluate this at the moment, but my inclination would be to merge and include with 0.9 so people can play with it/find rough edges sooner rather than later. |
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This PR adds the ability to generate trajectories for differential flat systems taking into account cost functions and constraints, in a manner that is consistent with the
control.optimalmodule. In particular, you can now callflat.point_to_pointwithcostandconstraintskeywords. The code then uses any extra degrees of freedom in the flat trajectory to minimize cost and satisfy constraints at a list of time points. For differentially flat systems, this method is much faster than solving an optimal control problem with terminal constraints.I also found and fixed some docstring glitches and naming inconsistencies in
control.optimalalong the way (these are all minor and don't change behavior. Unit tests, examples, and updated documentation are included.