C = np.eye(n_st)
fy = mtimes(C, xk2) + vk2
h = Function('h', [xk2, vk2], [fy])
Ch = Function('Ch', [vertcat(xk2, thetah2)],
[jacobian(fy, vertcat(xk2, thetah2))])
Chx = Function('Chx', [xk2], [jacobian(fy, xk2)])
uk_opt = np.array([2.5 * 10**(-6) * discritize, 280])
xkp = np.array([V0, Ca0, Cb0, Cc0, T0, Tj0, Tj0_in])
theta_par = theta_nom
xkh0 = xkp
zkh0 = vertcat(xkh0, theta_par)
Sigmak_p = Qz
Jce, qu_ce, lbq, ubq, g, lbg, ubg, qu_init = Dual.ce_mpc(
F_ode, n_pred, n_ctrl, n_st, n_par, n_ip, uk_lb, uk_ub, xk_lb,
xk_ub, xk2, theta_par, uk2, Tsamp, xkh0, uk_opt)
qp_mpc = {'x': vertcat(*qu_ce), 'f': Jce, 'g': vertcat(*g)}
solver_mpc = nlpsol(
'solver_mpc', MySolver, qp_mpc, {
'ipopt': {
'max_iter': 1000,
"check_derivatives_for_naninf": 'yes',
"print_user_options": 'yes'
}
})
res_mpc = solver_mpc(x0=qu_init, lbx=lbq, ubx=ubq, lbg=lbg, ubg=ubg)
res = res_mpc['x'].full().flatten()
xce1 = res[0::n_st + n_ip]
xce2 = res[1::n_st + n_ip]
xt = xt['xf']
x_t[i9,:] = xt.T
#plt.plot(range(len(xce1)),xce1)
#plt.plot(range(len(xce1)),xce2)
plt.plot(range(run_time), x_t[:,0], ':')
plt.plot(range(run_time), x_t[:,1], ':')
#plt.plot(range(len(xce1)),xe[0]*np.ones((len(xce1),1)))
#plt.plot(range(len(xce1)),xe[1]*np.ones((len(xce1),1)))
plt.show()
import pdb; pdb.set_trace()
uk_opt = .55
Jce,qu_ce,lbq,ubq,g,lbg,ubg,qu_init=Dual.ce_mpc(M_ode,run_time,n_ctrl,
n_st,n_par,n_ip,uk_lb,uk_ub,xk_lb,
xk_ub,xk4,uk4,Tsamp,x0,uk_opt)
qp_mpc = {'x':vertcat(*qu_ce), 'f':Jce, 'g':vertcat(*g)}
solver_mpc = nlpsol('solver_mpc', MySolver, qp_mpc,{'ipopt':
{'max_iter':1000,"check_derivatives_for_naninf":
'yes', "print_user_options":'yes' }})
res_mpc = solver_mpc(x0=qu_init, lbx=lbq, ubx=ubq, lbg=lbg, ubg=ubg)
res = res_mpc['x'].full().flatten()
xres = res[0:(run_time+1)*2]
xce1 = xres[0::n_st]
xce2 = xres[1::n_st]
uce1 = res[(run_time+1)*2:]
xt = x0
x_t = np.zeros((run_time,n_st))
