added: ShootingMethod and CollocationMethod abstract types

Author: franckgaga

src/controller/construct.jl

@@ -600,11 +600,11 @@ end
         ex̂, fx̂, gx̂, jx̂, kx̂, vx̂, bx̂, 
         Eŝ, Fŝ, Gŝ, Jŝ, Kŝ, Vŝ, Bŝ,
         gc!=nothing, nc=0
-    ) -> con, nϵ, P̃Δu, P̃u, Ẽ, Ẽŝ
+    ) -> con, nϵ, P̃Δu, P̃u, Ẽ
 
 Init `ControllerConstraint` struct with default parameters based on estimator `estim`.
 
-Also return `P̃Δu`, `P̃u`, `Ẽ` and `Ẽŝ` matrices for the the augmented decision vector `Z̃`.
+Also return `P̃Δu`, `P̃u` and `Ẽ` matrices for the the augmented decision vector `Z̃`.
 """
 function init_defaultcon_mpc(
     estim::StateEstimator{NT}, 
@@ -660,7 +660,7 @@ function init_defaultcon_mpc(
         C_ymin  , C_ymax , c_x̂min , c_x̂max , i_g,
         gc!     , nc
     )
-    return con, nϵ, P̃Δu, P̃u, Ẽ, Ẽŝ
+    return con, nϵ, P̃Δu, P̃u, Ẽ
 end
 
 "Repeat predictive controller constraints over prediction `Hp` and control `Hc` horizons."

src/controller/linmpc.jl

@@ -5,7 +5,7 @@ struct LinMPC{
     NT<:Real, 
     SE<:StateEstimator, 
     CW<:ControllerWeights,
-    TM<:TranscriptionMethod,
+    TM<:ShootingMethod,
     JM<:JuMP.GenericModel
 } <: PredictiveController{NT}
     estim::SE
@@ -54,7 +54,7 @@ struct LinMPC{
             NT<:Real, 
             SE<:StateEstimator, 
             CW<:ControllerWeights,
-            TM<:TranscriptionMethod, 
+            TM<:ShootingMethod, 
             JM<:JuMP.GenericModel
         }
         model = estim.model
@@ -71,7 +71,7 @@ struct LinMPC{
         Eŝ, Gŝ, Jŝ, Kŝ, Vŝ, Bŝ = init_defectmat(model, estim, transcription, Hp, Hc)
         # dummy vals (updated just before optimization):
         F, fx̂, Fŝ  = zeros(NT, ny*Hp), zeros(NT, nx̂), zeros(NT, nx̂*Hp)
-        con, nϵ, P̃Δu, P̃u, Ẽ, Ẽŝ = init_defaultcon_mpc(
+        con, nϵ, P̃Δu, P̃u, Ẽ = init_defaultcon_mpc(
             estim, weights, transcription,
             Hp, Hc, 
             PΔu, Pu, E, 
@@ -156,7 +156,7 @@ arguments. This controller allocates memory at each time step for the optimizati
 - `N_Hc=Diagonal(repeat(Nwt,Hc))` : positive semidefinite symmetric matrix ``\mathbf{N}_{H_c}``.
 - `L_Hp=Diagonal(repeat(Lwt,Hp))` : positive semidefinite symmetric matrix ``\mathbf{L}_{H_p}``.
 - `Cwt=1e5` : slack variable weight ``C`` (scalar), use `Cwt=Inf` for hard constraints only.
-- `transcription=SingleShooting()` : a [`TranscriptionMethod`](@ref) for the optimization.
+- `transcription=SingleShooting()` : [`SingleShooting`](@ref) or [`MultipleShooting`](@ref).
 - `optim=JuMP.Model(OSQP.MathOptInterfaceOSQP.Optimizer)` : quadratic optimizer used in
   the predictive controller, provided as a [`JuMP.Model`](@extref) object (default to 
   [`OSQP`](https://osqp.org/docs/parsers/jump.html) optimizer).
@@ -215,7 +215,7 @@ function LinMPC(
     N_Hc = Diagonal(repeat(Nwt, get_Hc(move_blocking(Hp, Hc)))),
     L_Hp = Diagonal(repeat(Lwt, Hp)),
     Cwt = DEFAULT_CWT,
-    transcription::TranscriptionMethod = DEFAULT_LINMPC_TRANSCRIPTION,
+    transcription::ShootingMethod = DEFAULT_LINMPC_TRANSCRIPTION,
     optim::JuMP.GenericModel = JuMP.Model(DEFAULT_LINMPC_OPTIMIZER, add_bridges=false),
     kwargs...
 )
@@ -259,7 +259,7 @@ function LinMPC(
     N_Hc = Diagonal(repeat(Nwt, get_Hc(move_blocking(Hp, Hc)))),
     L_Hp = Diagonal(repeat(Lwt, Hp)),
     Cwt  = DEFAULT_CWT,
-    transcription::TranscriptionMethod = DEFAULT_LINMPC_TRANSCRIPTION,
+    transcription::ShootingMethod = DEFAULT_LINMPC_TRANSCRIPTION,
     optim::JM = JuMP.Model(DEFAULT_LINMPC_OPTIMIZER, add_bridges=false),
 ) where {NT<:Real, SE<:StateEstimator{NT}, JM<:JuMP.GenericModel}
     isa(estim.model, LinModel) || error(MSG_LINMODEL_ERR) 

src/controller/nonlinmpc.jl

@@ -94,7 +94,7 @@ struct NonLinMPC{
         Eŝ, Gŝ, Jŝ, Kŝ, Vŝ, Bŝ = init_defectmat(model, estim, transcription, Hp, Hc)
         # dummy vals (updated just before optimization):
         F, fx̂, Fŝ  = zeros(NT, ny*Hp), zeros(NT, nx̂), zeros(NT, nx̂*Hp)
-        con, nϵ, P̃Δu, P̃u, Ẽ, Ẽŝ = init_defaultcon_mpc(
+        con, nϵ, P̃Δu, P̃u, Ẽ = init_defaultcon_mpc(
             estim, weights, transcription,
             Hp, Hc, 
             PΔu, Pu, E,