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@@ -70,80 +70,3 @@ plot(res, plotry=true, plotymax=true)
 
 See the [manual](https://JuliaControl.github.io/ModelPredictiveControl.jl/stable/manual/linmpc/)
 for more detailed examples.
-
-## Features
-
-### Model Predictive Control Features
-
-- linear and nonlinear plant models exploiting multiple dispatch
-- model linearization based on automatic differentiation (exact Jacobians)
-- supported objective function terms:
-  - output setpoint tracking
-  - move suppression
-  - input setpoint tracking
-  - terminal costs
-  - custom economic costs (economic model predictive control)
-- control horizon distinct from prediction horizon and custom move blocking
-- adaptive linear model predictive controller
-  - manual model modification
-  - automatic successive linearization of a nonlinear model
-  - objective function weights and covariance matrices modification
-- explicit predictive controller for problems without constraint
-- online-tunable soft and hard constraints on:
-  - output predictions
-  - manipulated inputs
-  - manipulated inputs increments
-  - terminal states to ensure nominal stability
-- custom nonlinear inequality constraints (soft or hard)
-- supported feedback strategy:
-  - state estimator (see State Estimation features)
-  - internal model structure with a custom stochastic model
-- automatic model augmentation with integrating states for offset-free tracking
-- support for unmeasured model outputs
-- feedforward action with measured disturbances that supports direct transmission
-- custom predictions for (or preview):
-  - output setpoints
-  - measured disturbances
-  - input setpoints
-- easy integration with `Plots.jl`
-- optimization based on `JuMP.jl` to quickly compare multiple optimizers:
-  - many quadratic solvers for linear control
-  - many nonlinear solvers for nonlinear control (local or global)
-- derivatives based on `DifferentiationInterface.jl` to compare different approaches:
-  - automatic differentiation (exact solution)
-  - symbolic differentiation (exact solution)
-  - finite difference (approximate solution)
-- supported transcription methods of the optimization problem:
-  - direct single shooting
-  - direct multiple shooting
-  - trapezoidal collocation
-- additional information about the optimum to ease troubleshooting
-- real-time control loop features:
-  - implementations that carefully limits the allocations
-  - simple soft real-time utilities
-
-### State Estimation Features
-
-- supported state estimators/observers:
-  - steady-state Kalman filter
-  - Kalman filter
-  - Luenberger observer
-  - internal model structure
-  - extended Kalman filter
-  - unscented Kalman filter
-  - moving horizon estimator
-- disable built-in observer to manually provide your own state estimate
-- easily estimate unmeasured disturbances by adding one or more integrators at the:
-  - manipulated inputs
-  - measured outputs
-- bumpless manual to automatic transfer for control with a proper initial estimate
-- estimators in two possible forms:
-  - filter (or current) form to improve accuracy and robustness
-  - predictor (or delayed) form to reduce computational load
-- moving horizon estimator in two formulations:
-  - linear plant models (quadratic optimization)
-  - nonlinear plant models (nonlinear optimization)
-- moving horizon estimator online-tunable soft and hard constraints on:
-  - state estimates
-  - process noise estimates
-  - sensor noise estimates