[ANN] ModelPredictiveControl.jl

franckgaga · June 10, 2023, 6:10pm

ModelPredictiveControl.jl

I’m currently writing a model predictive control package for Julia. Here’s a quick list of the main features:

Both linear and nonlinear plant models are supported. The implementation exploits multiple dispatch.
The optimization is based on JuMP.jl, allowing to quickly compare the performances of many optimizers.
The nonlinear predictive controller, and also the extended Kalman Filter and the moving horizon estimator (both will be added soon), uses automatic differentiation for the Jacobian.
Both internal model structure (classical approach) and closed-loop state estimator (modern approach) are supported for the feedback.
The nonlinear predictive controller based on a linear model (e.g. economic optimization of a linear plant model) computes the prediction with matrices instead of a for loop.
The nonlinear predictive controller automatically designs an adequate state estimator for the feedback, contrarily to nlmpc in MATLAB.
etc.

Feedback is certainly welcome!

Francis Gagnon

ufechner7 · June 10, 2023, 7:24pm

Did you look at GitHub - infiniteopt/InfiniteOpt.jl: An intuitive modeling interface for infinite-dimensional optimization problems. and https://web.casadi.org/ ?

Both can be used to implement NMPC control systems…

And the link to your package is not working. This one should work: GitHub - franckgaga/ModelPredictiveControl.jl: A model predictive control package for Julia.

franckgaga · June 10, 2023, 9:33pm

Yes, thanks! The link is modified.

I know about these two packages. They seems really powerful. My goal is to create a package that offer simpler implementations of both MPC and NMPC. You feed a plant model and you should be ready to go for quick simulations or control of a real plant (using additional DAC/ADC packages).

Francis

francis-gagnon · June 15, 2023, 5:57pm

@odow I think I read somewhere that vector decision variables is planned in JuMP.jl. Is it true and is there any timeline for that ? I’m using the splatting syntax right now and, intuitively, there is probably some overhead that hurdles the performance of the NMPC.

Thanks!

odow · June 15, 2023, 6:18pm

Link to code example? The answer is a somewhat, maybe, it depends.

francis-gagnon · June 15, 2023, 6:28pm

github.com

franckgaga/ModelPredictiveControl.jl/blob/73770d0abf25d5edc95b6fde29cbfd4f5e1d73a5/src/controller/nonlinmpc.jl#L287


      
                      Ŷ[:] = predict(mpc, model, ΔŨ)
                      C[:] = con_nonlinprog(mpc, model, Ŷ, ΔŨ)
                      last_ΔŨtup_dual = ΔŨtup
                  end
                  return C[i]
              end
              Cfunc = [(ΔŨ...) -> con_nonlinprog_i(i, ΔŨ) for i in 1:nC]
              (Jfunc, Cfunc)
          end
          register(optim, :Jfunc, nvar, Jfunc, autodiff=true)
          @NLobjective(optim, Min, Jfunc(ΔŨvar...))
          if nC ≠ 0
              n = 0
              for i in eachindex(con.Ŷmin)
                  sym = Symbol("C_Ŷmin_$i")
                  register(optim, sym, nvar, Cfunc[n + i], autodiff=true)
              end
              n = lastindex(con.Ŷmin)
              for i in eachindex(con.Ŷmax)
                  sym = Symbol("C_Ŷmax_$i")
                  register(optim, sym, nvar, Cfunc[n + i], autodiff=true)

Here’s what I did. I memoized my objective and constrain functions using PreallocationTools.jl. I’m also using closure for the parameters so I applied the tip at Performance of captured variables.

franckgaga · September 7, 2023, 8:37pm

ModelPredictiveControl v8.0.0

The release will be registered soon. Many features were added since the OP. Here is a short list:

Added: ExtendedKalmanFilter with automatic Jacobian computations using ForwardDiff.jl
Added: ExplicitMPC for linear predictive controllers without constraint and a small sample time
Added: automatic model augmentation based on observability for offset-free tracking
Added: support for both unmeasured disturbances at model input and output
Debug: only update the linear coefficients in LinMPC quadratic programming objective
Multiple improvements in documentation
Multiple other bug corrections
Improved coverage with many new tests

Feedback is welcome!

Francis Gagnon

afishy · September 7, 2023, 8:52pm

Is there an MPC package that can integrate with Flux/Zygote for optimization?

franckgaga · September 7, 2023, 9:00pm

To my knowledge, I don’t think so. Mine will support the packages accordingly if the developers of JuMP.jl decide to support them. But I think it needs a lot of work on their part. (I assume you are referring to the optimization and AD tools, not the modeling part.)

baggepinnen · September 8, 2023, 5:21am

This package has shaped up to become really impressive, congratulations on the nice work!

franckgaga · September 8, 2023, 6:54pm

Thanks Fredrik, the compliment is truly appreciated !

I found a major bug in the estimation of the unmeasured disturbances at the model input (nint_u option). The implementation is a bit more complex than the estimation of output disturbances.

I will release a fix soon. It should work as expected now. I also added a usage example of nint_u option in the manual. I still need to add new tests that use the nint_u option.