bump version

Author: franckgaga

Project.toml

@@ -1,7 +1,7 @@
 name = "ModelPredictiveControl"
 uuid = "61f9bdb8-6ae4-484a-811f-bbf86720c31c"
 authors = ["Francis Gagnon"]
-version = "0.7.1"
+version = "0.8.0"
 
 [deps]
 ControlSystemsBase = "aaaaaaaa-a6ca-5380-bf3e-84a91bcd477e"

README.md

@@ -93,7 +93,7 @@ for more detailed examples.
 - [x] supported feedback strategy:
   - [x] state estimator (see State Estimation features)
   - [x] internal model structure with a custom stochastic model
-- [x] offset-free tracking with a single or multiple integrators on measured outputs
+- [x] automatic model augmentation with integrating states for offset-free tracking
 - [x] support for unmeasured model outputs
 - [x] feedforward action with measured disturbances that supports direct transmission
 - [x] custom predictions for:
@@ -115,7 +115,9 @@ for more detailed examples.
   - [x] extended Kalman filter
   - [x] unscented Kalman filter
   - [ ] moving horizon estimator
-- [x] automatic model augmentation for offset-free tracking
+- [x] easily estimate unmeasured disturbances by adding one or more integrators at the:
+  - [x] manipulated inputs
+  - [x] measured outputs
 - [x] observers in predictor form to ease control applications
 - [ ] moving horizon estimator that supports:
   - [ ] inequality state constraints

docs/src/manual/nonlinmpc.md

@@ -59,7 +59,7 @@ plot(sim!(model, 60, u), plotu=false)
 An [`UnscentedKalmanFilter`](@ref) estimates the plant state :
 
 ```@example 1
-estim = UnscentedKalmanFilter(model, σQ=[0.1, 0.5], σR=[0.5], nint_ym=[1], σQ_int=[5.0])
+estim = UnscentedKalmanFilter(model, σQ=[0.1, 0.5], σR=[0.5], nint_ym=[1], σQint_ym=[5.0])
 ```
 
 The standard deviation of the angular velocity ``ω`` is higher here (`σQ` second value)
@@ -75,7 +75,7 @@ plot(res, plotu=false, plotxwithx̂=true)
 ```
 
 The estimate ``x̂_3`` is the integrator state that compensates for static errors (`nint_ym`
-and `σQ_int` parameters of [`UnscentedKalmanFilter`](@ref)). The Kalman filter performance
+and `σQint_ym` parameters of [`UnscentedKalmanFilter`](@ref)). The Kalman filter performance
 seems sufficient for control. As the motor torque is limited to -1.5 to 1.5 N m, we
 incorporate the input constraints in a [`NonLinMPC`](@ref):
 

src/controller/nonlinmpc.jl

@@ -168,7 +168,7 @@ Use custom state estimator `estim` to construct `NonLinMPC`.
 ```jldoctest
 julia> model = NonLinModel((x,u,_)->0.5x+u, (x,_)->2x, 10.0, 1, 1, 1);
 
-julia> estim = UnscentedKalmanFilter(model, σQ_int=[0.05]);
+julia> estim = UnscentedKalmanFilter(model, σQint_ym=[0.05]);
 
 julia> mpc = NonLinMPC(estim, Hp=20, Hc=1, Cwt=1e6)
 NonLinMPC controller with a sample time Ts = 10.0 s, Ipopt optimizer, UnscentedKalmanFilter estimator and:

test/test_state_estim.jl

@@ -90,7 +90,7 @@ end
     @test kalmanfilter5.Q̂ ≈ Hermitian(diagm(Float64[1, 4, 9 ,16, 25, 36]))
     @test kalmanfilter5.R̂ ≈ Hermitian(diagm(Float64[49, 64]))
 
-    kalmanfilter6 = KalmanFilter(linmodel2, σP0=[1,2,3,4], σP0_int=[5,6])
+    kalmanfilter6 = KalmanFilter(linmodel2, σP0=[1,2,3,4], σP0int_ym=[5,6])
     @test kalmanfilter6.P̂0 ≈ Hermitian(diagm(Float64[1, 4, 9 ,16, 25, 36]))
     @test kalmanfilter6.P̂  ≈ Hermitian(diagm(Float64[1, 4, 9 ,16, 25, 36]))
     @test kalmanfilter6.P̂0 !== kalmanfilter6.P̂