Does DiffEqFlux.jl supports a constrained optimal control problem, where in both control and states are constrained

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1

I am new to Julia and I am still learning the language.
I thought I could use Julia to solve an Optimal Control problem, which has some constraints. I looked at two examples I found at Readme · DiffEqFlux.jl and https://diffeqflux.sciml.ai/dev/examples/optimal_control/.

From sciml_train ( https://diffeqflux.sciml.ai/dev/Scimltrain/ ) documentation I found that there is a box constraint available.

Is it possible to solve the constrained optimal control problem?

2

Yes, do the same thing except throw the loss function into NLopt.jl with constrained optimization.

3

Thanks for the reply, but is there any example where can get an insight into solving the problem. I am able to solve a simple unconstrained problem however could not find the syntax for adding the constraints for optimal control problem. Any pointers would be much appreciated.

4

Instead of using sciml_train there, you can do a direct construction of NLopt.jl objective function and use Zygote.gradient to compute the gradient.