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bench: restructuring files
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5 files changed

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‎benchmark/0_bench_setup.jl‎

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Ts = 400.0
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sys = [ tf(1.90,[1800.0,1]) tf(1.90,[1800.0,1]) tf(1.90,[1800.0,1]);
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tf(-0.74,[800.0,1]) tf(0.74,[800.0,1]) tf(-0.74,[800.0,1]) ]
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function f!(ẋ, x, u, d, p)
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mul!(ẋ, p.A, x)
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mul!(ẋ, p.Bu, u, 1, 1)
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mul!(ẋ, p.Bd, d, 1, 1)
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return nothing
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end
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function h!(y, x, d, p)
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mul!(y, p.C, x)
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mul!(y, p.Dd, d, 1, 1)
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return nothing
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end

‎benchmark/1_bench_sim_model.jl‎

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linmodel = setop!(LinModel(sys, Ts, i_d=[3]), uop=[10, 50], yop=[50, 30], dop=[5])
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nonlinmodel = NonLinModel(f!, h!, Ts, 2, 4, 2, 1, p=linmodel, solver=nothing)
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nonlinmodel = setop!(nonlinmodel, uop=[10, 50], yop=[50, 30], dop=[5])
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u, d, y = [10, 50], [5], [50, 30]
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## ----------------- Runtime benchmarks ---------------------------------------------
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# TODO: Add runtime benchmarks for SimModel
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## ----------------- Allocation benchmarks ------------------------------------------
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samples, evals = 1, 1
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SUITE["allocation"]["SimModel"]["LinModel"]["updatestate!"] = @benchmarkable(
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updatestate!($linmodel, $u, $d); samples=samples, evals=evals
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)
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SUITE["allocation"]["SimModel"]["LinModel"]["evaloutput"] = @benchmarkable(
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evaloutput($linmodel, $d); samples=samples, evals=evals
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)
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SUITE["allocation"]["SimModel"]["NonLinModel"]["updatestate!"] = @benchmarkable(
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updatestate!($nonlinmodel, $u, $d); samples=samples, evals=evals
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)
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SUITE["allocation"]["SimModel"]["NonLinModel"]["evaloutput"] = @benchmarkable(
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evaloutput($nonlinmodel, $d); samples=samples, evals=evals
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)
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SUITE["allocation"]["SimModel"]["NonLinModel"]["linearize!"] = @benchmarkable(
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linearize!($linmodel, $nonlinmodel); samples=samples, evals=evals
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)

‎benchmark/2_bench_state_estim.jl‎

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## ----------------- Runtime benchmarks ---------------------------------------------
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# TODO: Add runtime benchmarks for StateEstimator
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## ----------------- Allocation benchmarks ------------------------------------------
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samples, evals = 1, 1
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skf = SteadyKalmanFilter(linmodel)
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SUITE["allocation"]["StateEstimator"]["SteadyKalmanFilter"]["preparestate!"] =
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@benchmarkable(
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preparestate!($skf, $y, $d),
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samples=samples, evals=evals
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)
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SUITE["allocation"]["StateEstimator"]["SteadyKalmanFilter"]["updatestate!"] =
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@benchmarkable(
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updatestate!($skf, $u, $y, $d),
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setup=preparestate!($skf, $y, $d),
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samples=samples, evals=evals
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)
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SUITE["allocation"]["StateEstimator"]["SteadyKalmanFilter"]["evaloutput"] =
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@benchmarkable(
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evaloutput($skf, $d),
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setup=preparestate!($skf, $y, $d),
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samples=samples, evals=evals
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)
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kf = KalmanFilter(linmodel, nint_u=[1, 1], direct=false)
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SUITE["allocation"]["StateEstimator"]["KalmanFilter"]["preparestate!"] =
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@benchmarkable(
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preparestate!($kf, $y, $d),
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samples=samples, evals=evals
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)
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SUITE["allocation"]["StateEstimator"]["KalmanFilter"]["updatestate!"] =
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@benchmarkable(
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updatestate!($kf, $u, $y, $d),
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setup=preparestate!($kf, $y, $d),
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samples=samples, evals=evals
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)
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lo = Luenberger(linmodel, nint_u=[1, 1])
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SUITE["allocation"]["StateEstimator"]["Luenberger"]["preparestate!"] =
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@benchmarkable(
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preparestate!($lo, $y, $d),
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samples=samples, evals=evals
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)
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SUITE["allocation"]["StateEstimator"]["Luenberger"]["updatestate!"] =
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@benchmarkable(
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updatestate!($lo, $u, $y, $d),
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setup=preparestate!($lo, $y, $d),
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samples=samples, evals=evals
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)
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im = InternalModel(nonlinmodel)
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SUITE["allocation"]["StateEstimator"]["InternalModel"]["preparestate!"] =
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@benchmarkable(
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preparestate!($im, $y, $d),
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samples=samples, evals=evals
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)
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SUITE["allocation"]["StateEstimator"]["InternalModel"]["updatestate!"] =
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@benchmarkable(
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updatestate!($im, $u, $y, $d),
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setup=preparestate!($im, $y, $d),
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samples=samples, evals=evals
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)
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ukf = UnscentedKalmanFilter(nonlinmodel)
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SUITE["allocation"]["StateEstimator"]["UnscentedKalmanFilter"]["preparestate!"] =
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@benchmarkable(
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preparestate!($ukf, $y, $d),
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samples=samples, evals=evals
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)
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SUITE["allocation"]["StateEstimator"]["UnscentedKalmanFilter"]["updatestate!"] =
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@benchmarkable(
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updatestate!($ukf, $u, $y, $d),
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setup=preparestate!($ukf, $y, $d),
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samples=samples, evals=evals
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)
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SUITE["allocation"]["StateEstimator"]["UnscentedKalmanFilter"]["evaloutput"] =
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@benchmarkable(
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evaloutput($ukf, $d),
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setup=preparestate!($ukf, $y, $d),
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samples=samples, evals=evals
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)
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ekf = ExtendedKalmanFilter(linmodel, nint_u=[1, 1], direct=false)
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SUITE["allocation"]["StateEstimator"]["ExtendedKalmanFilter"]["preparestate!"] =
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@benchmarkable(
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preparestate!($ekf, $y, $d),
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samples=samples, evals=evals
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)
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SUITE["allocation"]["StateEstimator"]["ExtendedKalmanFilter"]["updatestate!"] =
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@benchmarkable(
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updatestate!($ekf, $u, $y, $d),
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setup=preparestate!($ekf, $y, $d),
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samples=samples, evals=evals
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)

‎benchmark/3_bench_predictive_control.jl‎

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G = [ tf(1.90, [18, 1]) tf(1.90, [18, 1]);
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tf(-0.74,[8, 1]) tf(0.74, [8, 1]) ]
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uop, yop = [20, 20], [50, 30]
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model = setop!(LinModel(G, 2.0); uop, yop)
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function test_mpc(mpc, plant)
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plant.x0 .= 0; y = plant()
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initstate!(mpc, plant.uop, y)
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N = 75; ry = [50, 30]; ul = 0
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U, Y, Ry = zeros(2, N), zeros(2, N), zeros(2, N)
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for i = 1:N
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i == 26 && (ry = [48, 35])
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i == 51 && (ul = -10)
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y = plant()
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preparestate!(mpc, y)
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u = mpc(ry)
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U[:,i], Y[:,i], Ry[:,i] = u, y, ry
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updatestate!(mpc, u, y)
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updatestate!(plant, u+[0,ul])
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end
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return U, Y, Ry
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end
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## ----------------- Runtime benchmarks ---------------------------------------------
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optim = JuMP.Model(OSQP.Optimizer, add_bridges=false)
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transcription = SingleShooting()
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mpc_osqp_ss = setconstraint!(LinMPC(model; optim, transcription), ymin=[45, -Inf])
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JuMP.unset_time_limit_sec(mpc_osqp_ss.optim)
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optim = JuMP.Model(OSQP.Optimizer, add_bridges=false)
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transcription = MultipleShooting()
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mpc_osqp_ms = setconstraint!(LinMPC(model; optim, transcription), ymin=[45, -Inf])
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JuMP.unset_time_limit_sec(mpc_osqp_ms.optim)
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optim = JuMP.Model(DAQP.Optimizer, add_bridges=false)
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transcription = SingleShooting()
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mpc_daqp_ss = setconstraint!(LinMPC(model; optim, transcription), ymin=[45, -Inf])
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optim = JuMP.Model(DAQP.Optimizer, add_bridges=false)
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transcription = MultipleShooting()
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mpc_daqp_ms = setconstraint!(LinMPC(model; optim, transcription), ymin=[45, -Inf])
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# needed to solve Hessians with eigenvalues at zero, like in MultipleShooting transcription:
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JuMP.set_attribute(mpc_daqp_ms.optim, "eps_prox", 1e-6)
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samples, evals = 500, 1
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SUITE["runtime"]["PredictiveController"]["CSTR"]["LinMPC"]["OSQP"]["SingleShooting"] =
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@benchmarkable(test_mpc($mpc_osqp_ss, $model);
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samples=samples, evals=evals
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)
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SUITE["runtime"]["PredictiveController"]["CSTR"]["LinMPC"]["OSQP"]["MultipleShooting"] =
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@benchmarkable(test_mpc($mpc_osqp_ms, $model);
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samples=samples, evals=evals
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)
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SUITE["runtime"]["PredictiveController"]["CSTR"]["LinMPC"]["DAQP"]["SingleShooting"] =
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@benchmarkable(test_mpc($mpc_daqp_ss, $model);
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samples=samples, evals=evals
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)
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SUITE["runtime"]["PredictiveController"]["CSTR"]["LinMPC"]["DAQP"]["MultipleShooting"] =
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@benchmarkable(test_mpc($mpc_daqp_ms, $model);
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samples=samples, evals=evals
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)
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# ---------------------- Allocation benchmarks ------------------------------------------
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empc = ExplicitMPC(linmodel, Mwt=[1, 1], Nwt=[0.1, 0.1], Lwt=[0.1, 0.1])
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samples, evals = 1, 1
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SUITE["allocation"]["PredictiveController"]["ExplicitMPC"]["moveinput!"] =
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@benchmarkable(
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moveinput!($empc, $y, $d),
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setup=preparestate!($empc, $y, $d),
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samples=samples, evals=evals
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)

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