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bench: added CSTR LinMPC benchmarks
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‎benchmark/benchmarks.jl‎

Lines changed: 174 additions & 89 deletions
Original file line numberDiff line numberDiff line change
@@ -1,11 +1,12 @@
11
using BenchmarkTools
22
using ModelPredictiveControl, ControlSystemsBase, LinearAlgebra
3+
using JuMP, OSQP, DAQP, Ipopt, MadNLP
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45
Ts = 400.0
56
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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8-
const SUITE = BenchmarkGroup()
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const SUITE = BenchmarkGroup(["ModelPredictiveControl"])
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1011
## ==================================================================================
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## ================== SimModel benchmarks ===========================================
@@ -26,116 +27,200 @@ 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]
2829

29-
SUITE["allocation"]["SimModel"] = BenchmarkGroup(["allocation"])
30-
SUITE["allocation"]["SimModel"]["LinModel_updatestate!"] = @benchmarkable(
31-
updatestate!($linmodel, $u, $d),
32-
samples=1
30+
## ----------------- Runtime benchmarks ---------------------------------------------
31+
# TODO: Add runtime benchmarks for SimModel
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33+
34+
## ----------------- Allocation benchmarks ------------------------------------------
35+
samples, evals = 1, 1
36+
SUITE["allocation"]["SimModel"]["LinModel"]["updatestate!"] = @benchmarkable(
37+
updatestate!($linmodel, $u, $d); samples=samples, evals=evals
3338
)
34-
SUITE["allocation"]["SimModel"]["LinModel_evaloutput"] = @benchmarkable(
35-
evaloutput($linmodel, $d),
36-
samples=1
39+
SUITE["allocation"]["SimModel"]["LinModel"]["evaloutput"] = @benchmarkable(
40+
evaloutput($linmodel, $d); samples=samples, evals=evals
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)
38-
SUITE["allocation"]["SimModel"]["NonLinModel_updatestate!"] = @benchmarkable(
39-
updatestate!($nonlinmodel, $u, $d),
40-
samples=1
42+
SUITE["allocation"]["SimModel"]["NonLinModel"]["updatestate!"] = @benchmarkable(
43+
updatestate!($nonlinmodel, $u, $d); samples=samples, evals=evals
4144
)
42-
SUITE["allocation"]["SimModel"]["NonLinModel_evaloutput"] = @benchmarkable(
43-
evaloutput($nonlinmodel, $d),
44-
samples=1
45+
SUITE["allocation"]["SimModel"]["NonLinModel"]["evaloutput"] = @benchmarkable(
46+
evaloutput($nonlinmodel, $d); samples=samples, evals=evals
4547
)
46-
47-
SUITE["allocation"]["SimModel"]["NonLinModel_linearize!"] = @benchmarkable(
48-
linearize!($linmodel, $nonlinmodel),
49-
samples=1
48+
SUITE["allocation"]["SimModel"]["NonLinModel"]["linearize!"] = @benchmarkable(
49+
linearize!($linmodel, $nonlinmodel); samples=samples, evals=evals
5050
)
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5252
## ==================================================================================
5353
## ================== StateEstimator benchmarks =====================================
5454
## ==================================================================================
55+
56+
57+
## ----------------- Runtime benchmarks ---------------------------------------------
58+
# TODO: Add runtime benchmarks for StateEstimator
59+
60+
61+
## ----------------- Allocation benchmarks ------------------------------------------
62+
samples, evals = 1, 1
63+
5564
skf = SteadyKalmanFilter(linmodel)
56-
SUITE["allocation"]["StateEstimator"] = BenchmarkGroup(["allocation"])
57-
SUITE["allocation"]["StateEstimator"]["SteadyKalmanFilter_preparestate!"] = @benchmarkable(
58-
preparestate!($skf, $y, $d),
59-
samples=1
60-
)
61-
SUITE["allocation"]["StateEstimator"]["SteadyKalmanFilter_updatestate!"] = @benchmarkable(
62-
updatestate!($skf, $u, $y, $d),
63-
setup=preparestate!($skf, $y, $d),
64-
samples=1
65-
)
66-
SUITE["allocation"]["StateEstimator"]["SteadyKalmanFilter_evaloutput"] = @benchmarkable(
67-
evaloutput($skf, $d),
68-
setup=preparestate!($skf, $y, $d),
69-
samples=1
70-
)
65+
SUITE["allocation"]["StateEstimator"]["SteadyKalmanFilter"]["preparestate!"] =
66+
@benchmarkable(
67+
preparestate!($skf, $y, $d),
68+
samples=samples, evals=evals
69+
)
70+
SUITE["allocation"]["StateEstimator"]["SteadyKalmanFilter"]["updatestate!"] =
71+
@benchmarkable(
72+
updatestate!($skf, $u, $y, $d),
73+
setup=preparestate!($skf, $y, $d),
74+
samples=samples, evals=evals
75+
)
76+
SUITE["allocation"]["StateEstimator"]["SteadyKalmanFilter"]["evaloutput"] =
77+
@benchmarkable(
78+
evaloutput($skf, $d),
79+
setup=preparestate!($skf, $y, $d),
80+
samples=samples, evals=evals
81+
)
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7283
kf = KalmanFilter(linmodel, nint_u=[1, 1], direct=false)
73-
SUITE["allocation"]["StateEstimator"]["KalmanFilter_preparestate!"] = @benchmarkable(
74-
preparestate!($kf, $y, $d),
75-
samples=1
76-
)
77-
SUITE["allocation"]["StateEstimator"]["KalmanFilter_updatestate!"] = @benchmarkable(
78-
updatestate!($kf, $u, $y, $d),
79-
setup=preparestate!($kf, $y, $d),
80-
samples=1
81-
)
84+
SUITE["allocation"]["StateEstimator"]["KalmanFilter"]["preparestate!"] =
85+
@benchmarkable(
86+
preparestate!($kf, $y, $d),
87+
samples=samples, evals=evals
88+
)
89+
SUITE["allocation"]["StateEstimator"]["KalmanFilter"]["updatestate!"] =
90+
@benchmarkable(
91+
updatestate!($kf, $u, $y, $d),
92+
setup=preparestate!($kf, $y, $d),
93+
samples=samples, evals=evals
94+
)
8295

8396
lo = Luenberger(linmodel, nint_u=[1, 1])
84-
SUITE["allocation"]["StateEstimator"]["Luenberger_preparestate!"] = @benchmarkable(
85-
preparestate!($lo, $y, $d),
86-
samples=1
87-
)
88-
SUITE["allocation"]["StateEstimator"]["Luenberger_updatestate!"] = @benchmarkable(
89-
updatestate!($lo, $u, $y, $d),
90-
setup=preparestate!($lo, $y, $d),
91-
samples=1
92-
)
97+
SUITE["allocation"]["StateEstimator"]["Luenberger"]["preparestate!"] =
98+
@benchmarkable(
99+
preparestate!($lo, $y, $d),
100+
samples=samples, evals=evals
101+
)
102+
SUITE["allocation"]["StateEstimator"]["Luenberger"]["updatestate!"] =
103+
@benchmarkable(
104+
updatestate!($lo, $u, $y, $d),
105+
setup=preparestate!($lo, $y, $d),
106+
samples=samples, evals=evals
107+
)
93108

94109
im = InternalModel(nonlinmodel)
95-
SUITE["allocation"]["StateEstimator"]["InternalModel_preparestate!"] = @benchmarkable(
96-
preparestate!($im, $y, $d),
97-
samples=1
98-
)
99-
SUITE["allocation"]["StateEstimator"]["InternalModel_updatestate!"] = @benchmarkable(
100-
updatestate!($im, $u, $y, $d),
101-
setup=preparestate!($im, $y, $d),
102-
samples=1
103-
)
110+
SUITE["allocation"]["StateEstimator"]["InternalModel"]["preparestate!"] =
111+
@benchmarkable(
112+
preparestate!($im, $y, $d),
113+
samples=samples, evals=evals
114+
)
115+
SUITE["allocation"]["StateEstimator"]["InternalModel"]["updatestate!"] =
116+
@benchmarkable(
117+
updatestate!($im, $u, $y, $d),
118+
setup=preparestate!($im, $y, $d),
119+
samples=samples, evals=evals
120+
)
104121

105122
ukf = UnscentedKalmanFilter(nonlinmodel)
106-
SUITE["allocation"]["StateEstimator"]["UnscentedKalmanFilter_preparestate!"] = @benchmarkable(
107-
preparestate!($ukf, $y, $d),
108-
samples=1
109-
)
110-
SUITE["allocation"]["StateEstimator"]["UnscentedKalmanFilter_updatestate!"] = @benchmarkable(
111-
updatestate!($ukf, $u, $y, $d),
112-
setup=preparestate!($ukf, $y, $d),
113-
samples=1
114-
)
115-
SUITE["allocation"]["StateEstimator"]["UnscentedKalmanFilter_evaloutput"] = @benchmarkable(
116-
evaloutput($ukf, $d),
117-
setup=preparestate!($ukf, $y, $d),
118-
samples=1
119-
)
123+
SUITE["allocation"]["StateEstimator"]["UnscentedKalmanFilter"]["preparestate!"] =
124+
@benchmarkable(
125+
preparestate!($ukf, $y, $d),
126+
samples=samples, evals=evals
127+
)
128+
SUITE["allocation"]["StateEstimator"]["UnscentedKalmanFilter"]["updatestate!"] =
129+
@benchmarkable(
130+
updatestate!($ukf, $u, $y, $d),
131+
setup=preparestate!($ukf, $y, $d),
132+
samples=samples, evals=evals
133+
)
134+
SUITE["allocation"]["StateEstimator"]["UnscentedKalmanFilter"]["evaloutput"] =
135+
@benchmarkable(
136+
evaloutput($ukf, $d),
137+
setup=preparestate!($ukf, $y, $d),
138+
samples=samples, evals=evals
139+
)
120140

121141
ekf = ExtendedKalmanFilter(linmodel, nint_u=[1, 1], direct=false)
122-
SUITE["allocation"]["StateEstimator"]["ExtendedKalmanFilter_preparestate!"] = @benchmarkable(
123-
preparestate!($ekf, $y, $d),
124-
samples=1
125-
)
126-
SUITE["allocation"]["StateEstimator"]["ExtendedKalmanFilter_updatestate!"] = @benchmarkable(
127-
updatestate!($ekf, $u, $y, $d),
128-
setup=preparestate!($ekf, $y, $d),
129-
samples=1
130-
)
142+
SUITE["allocation"]["StateEstimator"]["ExtendedKalmanFilter"]["preparestate!"] =
143+
@benchmarkable(
144+
preparestate!($ekf, $y, $d),
145+
samples=samples, evals=evals
146+
)
147+
SUITE["allocation"]["StateEstimator"]["ExtendedKalmanFilter"]["updatestate!"] =
148+
@benchmarkable(
149+
updatestate!($ekf, $u, $y, $d),
150+
setup=preparestate!($ekf, $y, $d),
151+
samples=samples, evals=evals
152+
)
131153

132154
## ==================================================================================
133155
## ================== PredictiveController benchmarks ===============================
134156
## ==================================================================================
157+
G = [ tf(1.90, [18, 1]) tf(1.90, [18, 1]);
158+
tf(-0.74,[8, 1]) tf(0.74, [8, 1]) ]
159+
uop, yop = [20, 20], [50, 30]
160+
model = setop!(LinModel(G, 2.0); uop, yop)
161+
function test_mpc(mpc, plant)
162+
plant.x0 .= 0; y = plant()
163+
initstate!(mpc, plant.uop, y)
164+
N = 75; ry = [50, 30]; ul = 0
165+
U, Y, Ry = zeros(2, N), zeros(2, N), zeros(2, N)
166+
for i = 1:N
167+
i == 26 && (ry = [48, 35])
168+
i == 51 && (ul = -10)
169+
y = plant()
170+
preparestate!(mpc, y)
171+
u = mpc(ry)
172+
U[:,i], Y[:,i], Ry[:,i] = u, y, ry
173+
updatestate!(mpc, u, y)
174+
updatestate!(plant, u+[0,ul])
175+
end
176+
return U, Y, Ry
177+
end
178+
179+
## ----------------- Runtime benchmarks ---------------------------------------------
180+
optim = JuMP.Model(OSQP.Optimizer, add_bridges=false)
181+
182+
transcription = SingleShooting()
183+
mpc_osqp_ss = setconstraint!(LinMPC(model; optim, transcription), ymin=[45, -Inf])
184+
JuMP.unset_time_limit_sec(mpc_osqp_ss.optim)
185+
186+
transcription = MultipleShooting()
187+
mpc_osqp_ms = setconstraint!(LinMPC(model; optim, transcription), ymin=[45, -Inf])
188+
JuMP.unset_time_limit_sec(mpc_osqp_ms.optim)
189+
190+
optim = JuMP.Model(DAQP.Optimizer, add_bridges=false)
191+
192+
transcription = SingleShooting()
193+
mpc_daqp_ss = setconstraint!(LinMPC(model; optim, transcription), ymin=[45, -Inf])
194+
195+
transcription = MultipleShooting()
196+
mpc_daqp_ms = setconstraint!(LinMPC(model; optim, transcription), ymin=[45, -Inf])
197+
198+
samples, evals = 500, 1
199+
SUITE["runtime"]["PredictiveController"]["CSTR"]["LinMPC"]["OSQP"]["SingleShooting"] =
200+
@benchmarkable(test_mpc($mpc_osqp_ss, $model);
201+
samples=samples, evals=evals
202+
)
203+
SUITE["runtime"]["PredictiveController"]["CSTR"]["LinMPC"]["OSQP"]["MultipleShooting"] =
204+
@benchmarkable(test_mpc($mpc_osqp_ms, $model);
205+
samples=samples, evals=evals
206+
)
207+
SUITE["runtime"]["PredictiveController"]["CSTR"]["LinMPC"]["DAQP"]["SingleShooting"] =
208+
@benchmarkable(test_mpc($mpc_daqp_ss, $model);
209+
samples=samples, evals=evals
210+
)
211+
SUITE["runtime"]["PredictiveController"]["CSTR"]["LinMPC"]["DAQP"]["MultipleShooting"] =
212+
@benchmarkable(test_mpc($mpc_daqp_ms, $model);
213+
samples=samples, evals=evals
214+
)
215+
216+
217+
# ---------------------- Allocation benchmarks ------------------------------------------
135218
empc = ExplicitMPC(linmodel, Mwt=[1, 1], Nwt=[0.1, 0.1], Lwt=[0.1, 0.1])
136-
SUITE["allocation"]["PredictiveController"] = BenchmarkGroup(["allocation"])
137-
SUITE["allocation"]["PredictiveController"]["ExplicitMPC_moveinput!"] = @benchmarkable(
138-
moveinput!($empc, $y, $d),
139-
setup=preparestate!($empc, $y, $d),
140-
samples=1
141-
)
219+
220+
samples, evals = 1, 1
221+
SUITE["allocation"]["PredictiveController"]["ExplicitMPC"]["moveinput!"] =
222+
@benchmarkable(
223+
moveinput!($empc, $y, $d),
224+
setup=preparestate!($empc, $y, $d),
225+
samples=samples, evals=evals
226+
)

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