jrPhD / jrPhD/OpenLoopBalanceControl
How to Ensure That the Optimization Converges
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- Dominant language
- Python
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- 0
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Description
I recap here the different ideas to make opty converging :
- use gains so the cost function is evenly distributed over terms
- try different integration methods
- scale progressively the amplitude of the experimental data to iteratively solve a more complex problem
- use a initial condition that satisfies the constraints, like the solution of a more simple problem/model
- use regulation to get a smoother torques
- minimize the torques
Contributor guide
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First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by locating the opty direct-collocation trajectory optimization setup; the issue names no files or tests. Review the checked and unchecked convergence ideas, then define a reproducible convergence criterion and verify any selected approach against the experimental data and torque objectives.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- robotics
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Quiet
- Clarity
- Needs clarification
- Newbie friendliness
- 30/100