ModelPredictiveControl.jl

Features

✅ implemented feature ⬜ planned feature

✅ linear and nonlinear plant models exploiting multiple dispatch
⬜ model predictive controllers based on :
- ✅ linear plant models
- ⬜ linear plant models in a nonlinear controller using fast matrix algebra for the predictions (e.g. economic optimization of a linear model)
- ⬜ nonlinear plant models
⬜ supported objective function terms :
- ✅ output setpoint tracking
- ✅ move suppression
- ✅ input setpoint tracking
- ⬜ additional custom penalty (e.g. economic costs)
- ⬜ 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
⬜ additional information about the optimum to ease troubleshooting :
- ✅ optimal input increments over control horizon
- ✅ slack variable optimum
- ✅ objective function optimum
- ✅ output predictions at optimum
- ✅ current stochastic output predictions
- ⬜ custom penalty value at optimum

⬜ supported state estimators/observers :
- ✅ steady-state Kalman filter
- ✅ Kalman filter
- ⬜ Luenberger observer
- ✅ internal model structure
- ⬜ 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