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Fix type instability in LinMPC and NonLinMPC (introduced in 1.1.0) #130

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Fix type instability in LinMPC and NonLinMPC (introduced in 1.1.0) #130

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7 changes: 3 additions & 4 deletions src/controller/execute.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -371,11 +371,10 @@ The function mutates `Ue`, `Ŷe` and `Ū` in arguments, without assuming any i
function extended_predictions!(Ue, Ŷe, Ū, mpc, model, Ŷ0, ΔŨ)
ny, nu = model.ny, model.nu
# --- extended manipulated inputs Ue = [U; u(k+Hp-1)] ---
U0 = Ū
U0 .= mul!(U0, mpc.S̃, ΔŨ) .+ mpc.T_lastu0
Ue[1:end-nu] .= U0 .+ mpc.Uop
U = Ū
U .= mul!(U, mpc.S̃, ΔŨ) .+ mpc.T_lastu0 .+ mpc.Uop
# u(k + Hp) = u(k + Hp - 1) since Δu(k+Hp) = 0 (because Hc ≤ Hp):
Ue[end-nu+1:end] .= @views Ue[end-2nu+1:end-nu]
Ue[end-nu+1:end] .= @views U[end-nu+1:end]
# --- extended output predictions Ŷe = [ŷ(k); Ŷ] ---
Ŷe[1:ny] .= mpc.ŷ
Ŷe[ny+1:end] .= Ŷ0 .+ mpc.Yop
Expand Down
32 changes: 21 additions & 11 deletions src/controller/nonlinmpc.jl
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Expand Up @@ -2,16 +2,17 @@ const DEFAULT_NONLINMPC_OPTIMIZER = optimizer_with_attributes(Ipopt.Optimizer,"s

struct NonLinMPC{
NT<:Real,
SE<:StateEstimator,
SE<:StateEstimator,
JM<:JuMP.GenericModel,
JEfunc<:Function,
GCfunc<:Function,
P<:Any
} <: PredictiveController{NT}
estim::SE
# note: `NT` and the number type `JNT` in `JuMP.GenericModel{JNT}` can be
# different since solvers that support non-Float64 are scarce.
optim::JM
con::ControllerConstraint{NT}
con::ControllerConstraint{NT, GCfunc}
ΔŨ::Vector{NT}
ŷ ::Vector{NT}
Hp::Int
Expand Down Expand Up @@ -48,9 +49,17 @@ struct NonLinMPC{
Yop::Vector{NT}
Dop::Vector{NT}
buffer::PredictiveControllerBuffer{NT}
function NonLinMPC{NT, SE, JM, JEfunc, P}(
estim::SE, Hp, Hc, M_Hp, N_Hc, L_Hp, Cwt, Ewt, JE::JEfunc, gc, nc, p::P, optim::JM
) where {NT<:Real, SE<:StateEstimator, JM<:JuMP.GenericModel, JEfunc<:Function, P<:Any}
function NonLinMPC{NT, SE, JM, JEfunc, GCfunc, P}(
estim::SE,
Hp, Hc, M_Hp, N_Hc, L_Hp, Cwt, Ewt, JE::JEfunc, gc::GCfunc, nc, p::P, optim::JM
) where {
NT<:Real,
SE<:StateEstimator,
JM<:JuMP.GenericModel,
JEfunc<:Function,
GCfunc<:Function,
P<:Any
}
model = estim.model
nu, ny, nd, nx̂ = model.nu, model.ny, model.nd, estim.nx̂
ŷ = copy(model.yop) # dummy vals (updated just before optimization)
Expand Down Expand Up @@ -82,7 +91,7 @@ struct NonLinMPC{
nΔŨ = size(Ẽ, 2)
ΔŨ = zeros(NT, nΔŨ)
buffer = PredictiveControllerBuffer{NT}(nu, ny, nd, Hp)
mpc = new{NT, SE, JM, JEfunc, P}(
mpc = new{NT, SE, JM, JEfunc, GCfunc, P}(
estim, optim, con,
ΔŨ, ŷ,
Hp, Hc, nϵ,
Expand Down Expand Up @@ -318,9 +327,9 @@ function NonLinMPC(
L_Hp = diagm(repeat(Lwt, Hp)),
Cwt = DEFAULT_CWT,
Ewt = DEFAULT_EWT,
JE ::JEfunc = (_,_,_,_) -> 0.0,
JE ::JEfunc = (_,_,_,_) -> 0.0,
gc!::Function = (_,_,_,_,_,_) -> nothing,
gc ::Function = gc!,
gc ::GCfunc = gc!,
nc = 0,
p::P = estim.model.p,
optim::JM = JuMP.Model(DEFAULT_NONLINMPC_OPTIMIZER, add_bridges=false),
Expand All @@ -329,14 +338,15 @@ function NonLinMPC(
SE<:StateEstimator{NT},
JM<:JuMP.GenericModel,
JEfunc<:Function,
GCfunc<:Function,
P<:Any
}
nk = estimate_delays(estim.model)
if Hp ≤ nk
@warn("prediction horizon Hp ($Hp) ≤ estimated number of delays in model "*
"($nk), the closed-loop system may be unstable or zero-gain (unresponsive)")
end
return NonLinMPC{NT, SE, JM, JEfunc, P}(
return NonLinMPC{NT, SE, JM, JEfunc, GCfunc, P}(
estim, Hp, Hc, M_Hp, N_Hc, L_Hp, Cwt, Ewt, JE, gc, nc, p, optim
)
end
Expand Down Expand Up @@ -520,7 +530,7 @@ function get_optim_functions(mpc::NonLinMPC, ::JuMP.GenericModel{JNT}) where JNT
gc = get_tmp(gc_cache, ΔŨ1)
Ŷ0, x̂0end = predict!(Ȳ, x̂0, x̂0next, u0, û0, mpc, model, ΔŨ)
Ue, Ŷe = extended_predictions!(Ue, Ŷe, Ū, mpc, model, Ŷ0, ΔŨ)
ϵ = (nϵ ≠ 0) ? ΔŨ[end] : zero(T) # ϵ = 0 if nϵ == 0 (meaning no relaxation)
ϵ = (nϵ ≠ 0) ? ΔŨ[end] : 0 # ϵ = 0 if nϵ == 0 (meaning no relaxation)
mpc.con.gc!(gc, Ue, Ŷe, mpc.D̂e, mpc.p, ϵ)
g = con_nonlinprog!(g, mpc, model, x̂0end, Ŷ0, gc, ϵ)
return obj_nonlinprog!(Ȳ, Ū, mpc, model, Ue, Ŷe, ΔŨ)::T
Expand All @@ -539,7 +549,7 @@ function get_optim_functions(mpc::NonLinMPC, ::JuMP.GenericModel{JNT}) where JNT
gc = get_tmp(gc_cache, ΔŨ1)
Ŷ0, x̂0end = predict!(Ȳ, x̂0, x̂0next, u0, û0, mpc, model, ΔŨ)
Ue, Ŷe = extended_predictions!(Ue, Ŷe, Ū, mpc, model, Ŷ0, ΔŨ)
ϵ = (nϵ ≠ 0) ? ΔŨ[end] : zero(T) # ϵ = 0 if nϵ == 0 (meaning no relaxation)
ϵ = (nϵ ≠ 0) ? ΔŨ[end] : 0 # ϵ = 0 if nϵ == 0 (meaning no relaxation)
mpc.con.gc!(gc, Ue, Ŷe, mpc.D̂e, mpc.p, ϵ)
g = con_nonlinprog!(g, mpc, model, x̂0end, Ŷ0, gc, ϵ)
end
Expand Down
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