ModelPredictiveControl.jl

A model predictive control package for Julia.

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

Contents

• ModelPredictiveControl.jl
• • Contents
  • Features
• Manual
• • Installation
  • Predictive Controller Design
• Plant Models
• • SimModel
  • LinModel
  • NonLinModel
  • Set Operating Points
• State Estimators
• • StateEstimator
  • SteadyKalmanFilter
  • KalmanFilter
  • UnscentedKalmanFilter
  • InternalModel
• Predictive Controllers
• • PredictiveController
  • LinMPC
  • NonLinMPC
  • Set Constraint
  • Move Manipulated Input
• Generic Functions
• • Evaluate Output y
  • Update State x
  • Init State x
  • Set State x
• SimModel Internals
• StateEstimator Internals
• • KalmanFilter
  • UnscentedKalmanFilter
  • InternalModel
• PredictiveController Internals
• Index

Features

Legend

✅ implemented feature ⬜ planned feature

Model Predictive Control Features

• ✅ linear and nonlinear plant models exploiting multiple dispatch
• ⬜ model predictive controllers based on:
  • ✅ linear plant models
  • ⬜ 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
• ⬜ 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:
  • ✅ 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

State Estimation Features

• ⬜ 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