- feature list in readme.md

Author: franckgaga

README.md

@@ -4,6 +4,59 @@
 [![](https://img.shields.io/badge/docs-stable-blue.svg)](https://franckgaga.github.io/ModelPredictiveControl.jl/stable)
 [![](https://img.shields.io/badge/docs-dev-blue.svg)](https://franckgaga.github.io/ModelPredictiveControl.jl/dev)
 
+A model predictive control package for Julia.
 
+## Features
 
-A model predictive control package for Julia 
+✅ implemented feature  
+⬜ planned feature
+
+### Model Predictive Control
+
+- ✅ linear and nonlinear plant models using a unified structure
+- ⬜ model predictive controllers based on a :
+  - ✅ linear plant model
+  - ⬜ nonlinear plant model
+- ⬜ support for linear model predictions using fast matrix algebra in a nonlinear
+  controller (e.g. economic cost minimization of a linear plant model)
+- ⬜ 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 :
+  - ✅ internal model structure with custom stochastic model
+  - ✅ state estimator (see State Estimation features)
+- ✅ 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
+- ⬜ get 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
+
+- ⬜ supported state estimators/observers :
+  - ✅ steady-state Kalman filter
+  - ✅ Kalman filter
+  - ⬜ Luenberger observer
+  - ✅ internal model structure
+  - ⬜ unscented Kalman filter
+  - ⬜ moving horizon estimator
+- ✅ observers in the predictor form to facilitate predictive control applications.
+- ⬜ moving horizon estimator that supports :
+  - ⬜ inequality state constraints
+  - ⬜ equality constraints at zero on process noise (to reduce the problem size)

docs/src/index.md

@@ -19,35 +19,55 @@ Pages = [
 
 ## Features
 
+✅ implemented feature  
+⬜ planned feature
+
 ### Model Predictive Control
 
-- linear and nonlinear plant models using a unified structure and multiple dispatch
-- support for linear model predictions based on matrix algebra in a nonlinear controller
-  (e.g. economic optimization of a linear process model)
-- supported criterion terms : output setpoint tracking, move suppression, input setpoint
-  tracking and additional custom penalty (e.g. economic costs)
-- constraints on output predictions, manipulated inputs and manipulated inputs increments
-  that all support softening
-- custom manipulated input constraints that are a function of the predictions
-- supported feedback strategy : internal model structure with custom stochastic model or
-  state estimator (see State Estimation features)
-- offset-free tracking with a single or multiple integrators on each measured output
-- support for unmeasured model outputs
-- feedforward action with measured disturbances that supports direct transmission
-- custom predictions for output setpoints and measured disturbances
-- get additional information about the optimal input to ease troubleshooting : optimal
-  output predictions, slack variable optimum, optimal input increments over control horizon,
-  objective function minimum, custom penalty optimum, etc.
+- ✅ linear and nonlinear plant models using a unified structure
+- ⬜ model predictive controllers based on a :
+  - ✅ linear plant model
+  - ⬜ nonlinear plant model
+- ⬜ support for linear model predictions using fast matrix algebra in a nonlinear
+  controller (e.g. economic cost minimization of a linear plant model)
+- ⬜ 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 :
+  - ✅ internal model structure with custom stochastic model
+  - ✅ state estimator (see State Estimation features)
+- ✅ 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
+- ⬜ get 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
 
-- supported state estimators/observers :
-  - steady-state Kalman filter
-  - Kalman filter
-  - Luenberger observer
-  - internal model structure
-  - unscented Kalman filter
-  - moving horizon estimator
-- observers in the predictor form to facilitate predictive control applications.
-- moving horizon estimator with inequality state constraints, and equality constraints at 
-  zero on process noise (to reduce the problem size)
+- ⬜ supported state estimators/observers :
+  - ✅ steady-state Kalman filter
+  - ✅ Kalman filter
+  - ⬜ Luenberger observer
+  - ✅ internal model structure
+  - ⬜ unscented Kalman filter
+  - ⬜ moving horizon estimator
+- ✅ observers in the predictor form to facilitate predictive control applications.
+- ⬜ moving horizon estimator that supports :
+  - ⬜ inequality state constraints
+  - ⬜ equality constraints at zero on process noise (to reduce the problem size)