ModelPredictiveControl.jl

The package depends on ControlSystemsBase.jl for the linear systems and JuMP.jl for the solvers.

Features

✅ implemented feature ⬜ planned feature

✅ linear and nonlinear plant models exploiting multiple dispatch
✅ supported objective function terms:
- ✅ output setpoint tracking
- ✅ move suppression
- ✅ input setpoint tracking
- ✅ economic costs (economic model predictive control)
- ⬜ terminal cost to ensure nominal stability
✅ soft and hard constraints on:
- ✅ output predictions
- ✅ manipulated inputs
- ✅ manipulated inputs increments
⬜ custom manipulated input constraints that are a function of the predictions
✅ supported feedback strategy:
- ✅ state estimator (see State Estimation features)
- ✅ internal model structure with a custom stochastic model
✅ offset-free tracking with a single or multiple integrators on measured outputs
✅ support for unmeasured model outputs
✅ feedforward action with measured disturbances that supports direct transmission
✅ custom predictions for:
- ✅ output setpoints
- ✅ measured disturbances
✅ easy integration with Plots.jl
✅ optimization based on JuMP.jl:
- ✅ quickly compare multiple optimizers
- ✅ nonlinear solvers relying on automatic differentiation (exact derivative)
✅ additional information about the optimum to ease troubleshooting

⬜ supported state estimators/observers:
- ✅ steady-state Kalman filter
- ✅ Kalman filter
- ✅ Luenberger observer
- ✅ internal model structure
- ⬜ extended Kalman filter
- ✅ unscented Kalman filter
- ⬜ moving horizon estimator
✅ observers in predictor form to ease control applications
⬜ moving horizon estimator that supports:
- ⬜ inequality state constraints
- ⬜ zero process noise equality constraint to reduce the problem size