Roadmap 2025-26

[ocots]

Here is the control-toolbox roadmap for 2025-2026.

@jbcaillau @joseph-gergaud @PierreMartinon

[ocots]

Note for next meeting.

• Call for minisymposia at JuliaCon Global 2026
• Modern documentation: DocumenterVitepress, example
• Continuous benchmark (GitHub action)

[ocots]

Development Priorities & Feature List

Bug Fixes

• Fix max objective issue for exa

Workflows

• test Claude GitHub agent (we need an API key):
  • usage.md
  • solutions.md
• add spell check

Core Features

• Allow dimension zero control
• Add parameters to OCP definition? (shall we?)
• Improve outputs (multipliers, costate, etc.)
• Real-time plot of solver iterations
• ⭐ Adaptive time-stepping
• Improve initial guess specification
• ⭐ Add constraints at intermediate times, multi-phase problems (à la Dmitruk)
• ⭐ Non-uniform grid for exa

Solvers & Optimization

• ⭐ Add UNO solver
• Add direct shooting
• ⭐ BVP methods with adaptive time-stepping
• Support MINLP (Mixed-Integer Nonlinear Programming)
• Support NMPC (Nonlinear Model Predictive Control)
• ⭐ Implement homotopy methods

CTFlows Enhancements

• Refactor codebase
• Integrate SimpleDiffEq
• ⭐ Improve performance of AD on flows
• Add GPU support
• Support open and closed loop control
• Add implicit control support

DSL & Language Features

• Add DSL transformations
• ⭐ Penalisation à la Malisani and implicit control
• Natural language to DSL OC converter
• Integrate ModelingToolKit: to use symbolic reduction... Present use cases.
• Add use cases of AI: Latex to OC, OC to MTK, MTK to OC...

Documentation & Tutorials

• Add internal explanations for CTDirect (discretization methods)
• Create tutorial on AD backend options
• Add tutorials: turnpike, AD, free times, state constraints, impulsional cases, abnormal cases, good initial guess, ML and OC

⭐ Benchmarking

• Benchmark NLP solvers
• Benchmark modelers
• Benchmark linear solvers
• Benchmark AD backends
• Write comprehensive benchmarking article
• Compare to CasADi, InfinitOpt, JuMP, Grape

Visualization

• Add Makie support
• Add Plotly support

Publications & Examples

• ⭐ Submit JOSS papers (OC and OCP.jl)
• Add more problems to OCP.jl
• Add application examples: Kepler, MRI, Geometry, Zermelo...

Quality & Testing

• Improve code coverage

[ocots]

DSL & Language Features Discussion

This discussion focuses on the Domain-Specific Language (DSL) used in the OptimalControl.jl / CTParser.jl ecosystem, and on potential extensions and research directions beyond the current long project. We highlight formal transformations, symbolic manipulations, and AI-assisted workflows.

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1. DSL Transformations and Symbolic Extensions

The DSL can be extended with formal transformations to handle more complex optimal control (OC) problems:

• Multi-phase and free final time problems
  Transformations at the DSL level can encode complex structures like multi-phase problems, free final time, or interior point constraints.
  Relevant discussions: CTParser Issue #139

• Symbolic reduction with ModelingToolkit.jl
  Integrate symbolic simplifications or reductions directly in the DSL workflow. See: OptimalControl Issue #658 for concrete use cases.

• Loss control and constraint penalization
  Introduce formal DSL transformations for penalization and loss control. Related discussions: OptimalControl Issue #620, OptimalControl Issue #525

• Regularization techniques (Malisani style)
  Apply symbolic or numerical regularization for OC problems. References:

  • BVP-DAE repository
  • BVP-DAE Issue #1
  • Malisani et al., HAL 2020

• DSL syntax enhancements
  Examples include handling new keywords, state declarations, and multi-phase notation. See:

  • CTParser Issue #26
  • CTParser Issue #32

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2. Formal Verification of DSL Transformations

• For critical transformations, consider formal verification of the generated DSL code.
  Tools like Coq or Lean can check correctness automatically.
• LLMs can assist in verifying the structure of the generated code or proposing candidate transformations.
• Example discussion / inspiration: Claude.ai snippet

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3. AI / LLM Integration

• Transformations can be symbolic or AI-assisted:
  • Natural language → DSL conversion
  • LaTeX / Math → DSL
  • DSL → ModelingToolkit.jl → OC solver
  • Bidirectional DSL ↔ MTK transformations
• LLMs can help generate or verify DSL code from natural language or mathematical descriptions.
• Relevant issues highlighting AI-assisted workflows:
  • CTParser Issue #31
  • CTParser Issue #29
  • CTParser Issue #30

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4. Use Cases and Examples

• Integrating ModelingToolkit.jl for symbolic simplification and reduction: see OptimalControl Issue #658
• Regularization, loss control, multi-phase OC problems: see OptimalControl Issue #525
• AI-assisted DSL generation from LaTeX / natural language: inspired by Claude.ai example

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5. Next Steps / Research Directions

1. Formal DSL transformations for multi-phase, free final time, and interior constraints.
2. Symbolic integration with ModelingToolkit.jl for automatic reduction and simplification.
3. LLM-assisted workflows to convert natural language or LaTeX into DSL code and to verify transformations.
4. Formal verification pipeline using Coq / Lean for critical transformation checks.
5. Documented examples linking symbolic, AI-assisted, and solver-ready workflows.

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References & Issues

• OptimalControl Issue #658 – Integrate ModelingToolkit.jl
• OptimalControl Issue #620 – Loss control transformations
• OptimalControl Issue #525 – Multi-phase / advanced DSL transformations
• CTParser Issue #139 – DSL transformation discussion
• CTParser Issue #31 – LLM-assisted DSL
• CTParser Issue #29 – DSL syntax extensions
• CTParser Issue #30 – DSL code verification
• CTParser Issue #26 – DSL keywords & multi-phase
• CTParser Issue #32 – DSL transformations
• BVP-DAE repository
• BVP-DAE Issue #1
• Malisani et al., HAL 2020
• Claude.ai example

[ocots]

Explore AI and control-toolbox

Explore the possibilities offered by artificial intelligence to interact with the software.
In particular, use natural language and standard LLM tools to provide models or analyze solutions.

• Perform transformations on models:

  • Free final time → fixed final time.
  • Lagrange → Mayer.
  • Multi-phase → single-phase.
  • Non-regular → regular (using various regularizations such as logarithmic barriers, interior-point methods, etc.).
  • Add a homotopy parameter.

• Analyze solutions:

  • Identify the optimal structure and switching times.
  • Provide a report on the quality of the solution based on the logs.

• Build shooting functions:

  • Propose a set of examples and provide guidelines for constructing the shooting function.
  • From there, and using the optimal structure, build the corresponding shooting function.

• Build an initialization for the shooting function with a robustification system.

[ocots]

Issues to write or improve as a priority for @ocots

🧾 Publication and presentation

• Finish the JOSS paper: https://github.com/control-toolbox/joss/issues/1
• Prepare a Wikipedia page, first as an issue draft: [Doc] Create a page on wikipedia OptimalControl.jl#672
• Once it is rich enough, prepare a JOSS paper for OptimalControlProblems.jl (independently of OptimalControl.jl)

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⚙️ Solvers and model handling

• Check that the solver no longer appears in direct_transcription and close the issue: [Dev] Remove the solver from direct_transcription description CTDirect.jl#493
• Review of the handling of the modeller in direct_transcription and related modules:
  • Description CTDirect.jl#521
  • Description and problems OptimalControlProblems.jl#174
  • then handle this consistently within CTBenchmarks.jl
• Review how to specify which solver we actually want (not just by using); use an explicit variable, add tests, and update the documentation.
• Add MadNCL and UNO to the benchmarks: [Dev] Try MadNCL CTDirect.jl#514, Add optimisation solver: Uno CTDirect.jl#304
• Review how a solution is constructed to make it more unified: [General] Build an OCP solution CTDirect.jl#483
• Investigate whether it’s possible to give stronger constraints to solvers to prevent them from leaving the feasible set: [General] Definition of the variables inside an interval OptimalControl.jl#664
• Add tests in OptimalControl.jl to verify that different solvers can be used successfully: [Dev] Add ExaModels unit tests OptimalControl.jl#575

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🧮 Modeling and optimization problems

• Use a single model for problems with OptimalControl.jl and always express them in minimization form:
  [Doc] _s problems OptimalControlProblems.jl#157,
  [General] Objective value CTDirect.jl#474
  Also improve how problems are retrieved: [Dev] Exa and problems OptimalControlProblems.jl#136
• Optimization with a control of dimension 0: [Dev] Allow dim zero control OptimalControl.jl#656
• Add the notion of parameters: [Dev] Add parameters OptimalControl.jl#655
• Clarify how multipliers, costates, etc. are handled:
  [General] Costate OptimalControlProblems.jl#170,
  Dual variable of the path constraints CTDirect.jl#405
• Constraints and dual variables management: [Dev] Dual variables CTModels.jl#105
• Improve dimension handling in visualizations and the way piecewise-constant controls are displayed: [General] Control logic CTModels.jl#207
• Explain how the initial guess is handled in OptimalControlProblems.jl: [Doc] Intial guess OptimalControlProblems.jl#151
• In the manual section on the initial guess, discuss projection onto constraints: [Doc] Projection of the initial iterate OptimalControl.jl#600
• Add model comparisons and check for ducted_fan: [General] Test comparaison NLP model OptimalControlProblems.jl#166
• Create efficient tests and update import/export functions: [General] Improve import and export CTModels.jl#217

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📚 Documentation, tutorials, and internal consistency

• Display the documentation of problems in OptimalControlProblems.jl: [Doc] Examples are not displayed correctly in the REPL OptimalControlProblems.jl#173
• Make the documentation of problems clearer: [Doc] Typo in the docstrings OptimalControlProblems.jl#172
  Explain that the correct using statement and backend must be specified. Consider replacing backend types with symbols.
• Export additional functions and update documentation: [General] Export some other functions OptimalControl.jl#618
  Maintain naming consistency. See also [Dev] Naming and consistency CTModels.jl#169
• Review the tutorial on NLP manipulation: [General] Build solution from DOCP Tutorials.jl#38
  Clearly explain how to call different solvers, on CPU/GPU, with Mumps, etc.
• Add an ID for the LQR tutorial: [General] Tutorial LQR Tutorials.jl#24
• Provide a solution for this problem and update the LQR tutorial: [Bug] Error matrix formulation OptimalControl.jl#546
• Finalize the tutorial on logarithmic barrier regularization: [Dev] Tutorial on regularization Tutorials.jl#22
• Finalize the tutorial on intermediate-time constraints: [Dev] Tutorial for constraints at intermediate times Tutorials.jl#18
• Finalize the Kepler application: [Dev] Kepler add examples Kepler.jl#28
• Remove unnecessary stars or improve the table of contents: https://control-toolbox.org/CTBenchmarks.jl/stable/dev.html
• Check that all documentation is up to date: [Doc] Relecture de la documentation OptimalControl.jl#562

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🧠 Benchmarks and workflows

• Merge in CTBenchmarks: branch 82-doc-add-new-instances-benchmark into 71-doc-plot-benchmarks-results.
• Replace moonshot by mothra or occidata: Add moonshot CPU CTBenchmarks.jl#87
• Typo for MadNLP: [Doc] Typo for MadNLP CTBenchmarks.jl#88
• Remove moonshot from simple CPU runs and replace it with mothra and occidata: [General] Add moonshot CPU CTBenchmarks.jl#86
  Also improve label management for triggering benchmarks.
• Add a comment here to update progress: [Doc] Add new instances benchmark CTBenchmarks.jl#82
  Then merge, explain this to students, and update Generate plots from benchmarks results CTBenchmarks.jl#77 so they can continue their plots.
• Create workflow issues for adding new problems in OptimalControlProblems.jl, then one issue per problem to be added
  (transform [Dev] Add examples OptimalControlProblems.jl#92 into a discussion).
  Include interactive figures for benchmarks and formalize the definition of a setup for a performance profile.
• Check if mothra now works: [General] Add bench on KKT CTBenchmarks.jl#63
• For benchmarks, determine whether to compare with manually discretized and modeled problems: Test with ADNLPModels.jl CTBenchmarks.jl#31
• Check the Space Shuttle problem: New instance of Space Shuttle ( Task #4) CTBenchmarks.jl#22

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🔬 Miscellaneous and code quality

• Introduce JET tests in all packages if possible: [Dev] Reduce type instabilities CTModels.jl#155
• Real-time plot: [Feature] Real time plot of solver iterations OptimalControl.jl#568
• Review this issue: [Dev] Dual variables CTModels.jl#105

[jbcaillau]

@ocots @PierreMartinon refactoring with CTSolvers.jl is now a hot topic

[ocots]

Example of problems for benchmarking: NonlinearOptimizationTestFunctions.jl

[ocots]

Discussion about benchmarking is here: https://github.com/orgs/control-toolbox/discussions/75.