ModelPredictiveControl.jl

A model predictive control package for Julia.

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

The objective is to provide a simple, clear and modular framework to quickly design model predictive controllers (MPCs) in Julia, while preserving the flexibility for advanced real-time optimization. Modern MPCs based on closed-loop state estimators are the main focus of the package, but classical approaches that rely on internal models are also possible. The JuMP.jl interface allows the user to test different solvers easily if the performance of the default settings is not satisfactory.

The documentation is divided in two parts:

• Manual — This section includes step-by-step guides to design predictive controllers on multiple case studies.
• Functions — Documentation of methods and types exported by the package. The "Internals" section provides implementation details of functions that are not exported.

Manual

• Manual: Installation
• Manual: Linear Design
• • Linear Model
  • Linear Model Predictive Controller
  • Adding Feedforward Compensation
• Manual: Nonlinear Design
• • Nonlinear Model
  • Nonlinear Model Predictive Controller
  • Economic Model Predictive Controller

Functions: Public

• Functions: Plant Models
• • SimModel
  • LinModel
  • NonLinModel
  • Set Operating Points
• Functions: State Estimators
• • StateEstimator
  • SteadyKalmanFilter
  • KalmanFilter
  • Luenberger
  • UnscentedKalmanFilter
  • ExtendedKalmanFilter
  • InternalModel
  • Default Model Augmentation
• Functions: Predictive Controllers
• • PredictiveController
  • LinMPC
  • ExplicitMPC
  • NonLinMPC
  • Set Constraint
  • Move Manipulated Input u
  • Get Additional Information
• Functions: Generic Functions
• • Evaluate Output y
  • Update State x
  • Init State x
  • Set State x
  • Quick Simulation

Functions: Internals

• Functions: SimModel Internals
• Functions: StateEstimator Internals
• Functions: PredictiveController Internals