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29 | 29 | mpc9 = LinMPC(model, nint_u=[1, 1], nint_ym=[0, 0])
|
30 | 30 | @test mpc9.estim.nint_u == [1, 1]
|
31 | 31 | @test mpc9.estim.nint_ym == [0, 0]
|
32 |
| - mpc10 = LinMPC(model, M_Hp=diagm(collect(1.01:0.01:1.2))) |
33 |
| - @test mpc10.weights.M_Hp ≈ diagm(collect(1.01:0.01:1.2)) |
| 32 | + mpc10 = LinMPC(model, M_Hp=Hermitian(diagm(1.01:0.01:1.2), :L)) |
| 33 | + @test mpc10.weights.M_Hp ≈ diagm(1.01:0.01:1.2) |
34 | 34 | @test mpc10.weights.M_Hp isa Hermitian{Float64, Matrix{Float64}}
|
35 |
| - mpc11 = LinMPC(model, N_Hc=diagm([0.1,0.11,0.12,0.13]), Cwt=Inf) |
| 35 | + mpc11 = LinMPC(model, N_Hc=Hermitian(diagm([0.1,0.11,0.12,0.13]), :L), Cwt=Inf) |
36 | 36 | @test mpc11.weights.Ñ_Hc ≈ diagm([0.1,0.11,0.12,0.13])
|
37 | 37 | @test mpc11.weights.Ñ_Hc isa Hermitian{Float64, Matrix{Float64}}
|
38 |
| - mcp12 = LinMPC(model, L_Hp=diagm(collect(0.001:0.001:0.02))) |
39 |
| - @test mcp12.weights.L_Hp ≈ diagm(collect(0.001:0.001:0.02)) |
| 38 | + mcp12 = LinMPC(model, L_Hp=Hermitian(diagm(0.001:0.001:0.02), :L)) |
| 39 | + @test mcp12.weights.L_Hp ≈ diagm(0.001:0.001:0.02) |
40 | 40 | @test mcp12.weights.L_Hp isa Hermitian{Float64, Matrix{Float64}}
|
41 | 41 | model2 = LinModel{Float32}(0.5*ones(1,1), ones(1,1), ones(1,1), zeros(1,0), zeros(1,0), 1.0)
|
42 | 42 | mpc13 = LinMPC(model2)
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@@ -463,14 +463,14 @@ end
|
463 | 463 | mpc9 = ExplicitMPC(model, nint_u=[1, 1], nint_ym=[0, 0])
|
464 | 464 | @test mpc9.estim.nint_u == [1, 1]
|
465 | 465 | @test mpc9.estim.nint_ym == [0, 0]
|
466 |
| - mpc10 = ExplicitMPC(model, M_Hp=diagm(collect(1.01:0.01:1.2))) |
467 |
| - @test mpc10.weights.M_Hp ≈ diagm(collect(1.01:0.01:1.2)) |
| 466 | + mpc10 = ExplicitMPC(model, M_Hp=Hermitian(diagm(1.01:0.01:1.2), :L)) |
| 467 | + @test mpc10.weights.M_Hp ≈ diagm(1.01:0.01:1.2) |
468 | 468 | @test mpc10.weights.M_Hp isa Hermitian{Float64, Matrix{Float64}}
|
469 |
| - mpc11 = ExplicitMPC(model, N_Hc=diagm([0.1,0.11,0.12,0.13])) |
| 469 | + mpc11 = ExplicitMPC(model, N_Hc=Hermitian(diagm([0.1,0.11,0.12,0.13]), :L)) |
470 | 470 | @test mpc11.weights.Ñ_Hc ≈ diagm([0.1,0.11,0.12,0.13])
|
471 | 471 | @test mpc11.weights.Ñ_Hc isa Hermitian{Float64, Matrix{Float64}}
|
472 |
| - mcp12 = ExplicitMPC(model, L_Hp=diagm(collect(0.001:0.001:0.02))) |
473 |
| - @test mcp12.weights.L_Hp ≈ diagm(collect(0.001:0.001:0.02)) |
| 472 | + mcp12 = ExplicitMPC(model, L_Hp=Hermitian(diagm(0.001:0.001:0.02), :L)) |
| 473 | + @test mcp12.weights.L_Hp ≈ diagm(0.001:0.001:0.02) |
474 | 474 | @test mcp12.weights.L_Hp isa Hermitian{Float64, Matrix{Float64}}
|
475 | 475 | model2 = LinModel{Float32}(0.5*ones(1,1), ones(1,1), ones(1,1), zeros(1,0), zeros(1,0), 1.0)
|
476 | 476 | mpc13 = ExplicitMPC(model2)
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@@ -671,14 +671,14 @@ end
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671 | 671 | nmpc11 = NonLinMPC(nonlinmodel, Hp=15, nint_u=[1, 1], nint_ym=[0, 0])
|
672 | 672 | @test nmpc11.estim.nint_u == [1, 1]
|
673 | 673 | @test nmpc11.estim.nint_ym == [0, 0]
|
674 |
| - nmpc12 = NonLinMPC(nonlinmodel, Hp=10, M_Hp=diagm(collect(1.01:0.01:1.2))) |
675 |
| - @test nmpc12.weights.M_Hp ≈ diagm(collect(1.01:0.01:1.2)) |
| 674 | + nmpc12 = NonLinMPC(nonlinmodel, Hp=10, M_Hp=Hermitian(diagm(1.01:0.01:1.2), :L)) |
| 675 | + @test nmpc12.weights.M_Hp ≈ diagm(1.01:0.01:1.2) |
676 | 676 | @test nmpc12.weights.M_Hp isa Hermitian{Float64, Matrix{Float64}}
|
677 |
| - nmpc13 = NonLinMPC(nonlinmodel, Hp=10, N_Hc=diagm([0.1,0.11,0.12,0.13]), Cwt=Inf) |
| 677 | + nmpc13 = NonLinMPC(nonlinmodel, Hp=10, N_Hc=Hermitian(diagm([0.1,0.11,0.12,0.13]), :L), Cwt=Inf) |
678 | 678 | @test nmpc13.weights.Ñ_Hc ≈ diagm([0.1,0.11,0.12,0.13])
|
679 | 679 | @test nmpc13.weights.Ñ_Hc isa Hermitian{Float64, Matrix{Float64}}
|
680 |
| - nmcp14 = NonLinMPC(nonlinmodel, Hp=10, L_Hp=diagm(collect(0.001:0.001:0.02))) |
681 |
| - @test nmcp14.weights.L_Hp ≈ diagm(collect(0.001:0.001:0.02)) |
| 680 | + nmcp14 = NonLinMPC(nonlinmodel, Hp=10, L_Hp=Hermitian(diagm(0.001:0.001:0.02), :L)) |
| 681 | + @test nmcp14.weights.L_Hp ≈ diagm(0.001:0.001:0.02) |
682 | 682 | @test nmcp14.weights.L_Hp isa Hermitian{Float64, Matrix{Float64}}
|
683 | 683 | nmpc15 = NonLinMPC(nonlinmodel, Hp=10, gc=(Ue,Ŷe,D̂e,p,ϵ)-> [p*dot(Ue,Ŷe)+sum(D̂e)+ϵ], nc=1, p=10)
|
684 | 684 | LHS = zeros(1)
|
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