def test_mpc(self):
# Inputs
t = np.arange(0, 3600 * 10, 3600)
inp = pd.DataFrame(index=pd.Index(t, name='time'),
columns=['q', 'Tout'])
inp['q'] = np.full(t.size, 0)
inp['Tout'] = np.full(t.size, 273.15)
# Bounds
ubounds = [(0., 4000.)]
xbounds = [(293.15, 296.15)]
# Initial state
x0 = [293.65]
# Optimization
mpc = mshoot.MPCEmulation(emumod=self.model, cfun=cfun)
u, xctr, xemu, yemu, uhist = mpc.optimize(model=self.model,
inp_ctr=inp.copy(),
inp_emu=inp.copy(),
free=['q'],
ubounds=ubounds,
xbounds=xbounds,
x0=x0,
ynominal=[4000., 293.15],
step=1,
horizon=3)
# ax = u.plot(title='u')
# ax.set_ylim(0, 4000)
# ax = xemu.plot(title='xemu')
# ax.set_ylim(292.15, 296.15)
# plt.show()
# Assert the solution is correct
self.assertLess(abs(xemu['heatCapacitor.T'].iloc[-1] - 293.15),
0.3) # Ideally, should be even closer
# Validate emulation with optimized control
inp['q'] = u['q']
yvld, xvld = self.model.simulate(inp, x0)
# self.assertTrue(((yvld - yemu).abs() < 1e-3).all().all()) # Might not be true for FMUs *
self.assertTrue(((xvld - xemu).abs() <
1e-3).all().all()) # Might not be true for FMUs *
u2 = np.sin(t / 86400. * 2. * np.pi) * 1000. + np.random.rand(
48) * 1000. - 500. # Noisy sinusoid
inp = pd.DataFrame(index=pd.Index(t, name='time'))
inp['u1'] = u1
inp['u2'] = u2
inp.plot()
plt.show()
# 5) Define bounds
Tlo = np.where((t > 86400 / 2) & (t < 86400 * 1.5), 273.15 + 23, 273.15 + 17)
Thi = 273.15 + 25
# 6) Instantiate MPCEmulation
mpc = mshoot.MPCEmulation(model_emu, cfun)
# 7) Optimize
u, xctr, xemu, yemu, u_hist = mpc.optimize(model=model_ctr,
inp_ctr=inp,
inp_emu=inp,
free=['u1'],
ubounds=[(-1000, 1000)],
xbounds=[(273.15, 333.15),
(273.15, 333.15),
(Tlo, Thi)],
x0=[293.15, 293.15, 293.15],
maxiter=20,
ynominal=[293.15, 1000.],
step=1,
horizon=3)
