from casadi import *
import numpy as np
from utilities import MPC
import matplotlib.pyplot as plt
from Dynamic_system_small import Bio_reactor as System #specifications, DAE_system, integrator_model
# System = Bio_reactor
# System_unc = Bio_reactor_unc
N = 6 # This is the total steps
#Initialize system
Sys = System()
F = Sys.integrator_model()
# Construct MPC
MPC_ = MPC(System, horizon=N, penalize_u=False)
# Define system
dt, x0, _, _, _ = Sys.specifications()
# Initialize Matrices
N_mc = 1
his_x = np.zeros([N_mc, 1000, len(x0)])
his_u = np.zeros([N_mc, 1000, 2])
u_apply = np.array([0.])
u1 = np.array([
265.15893977, 269.47831514, 271.16360361, 220.6355221, 301.72600227,
120.00073468
]).reshape((1, -1))
u2 = np.array([
25.23242466, 20.31618808, 39.68463853, 39.999973, 39.99999722, 39.99999775
]).reshape((1, -1)) * 0.8
uu = np.vstack((u1, u2))
for kk in range(N_mc):
T = 1. # Time horizon
N = 12 # number of control intervals
# nk, tf, x0, Lsolver, c_code = specifications()
#
# xd, xa, u, uncertainty, ODEeq, Aeq, u_min, u_max, states, \
# algebraics, inputs, nd, na, nu, nmp, modparval, ng, gfcn, Obj_M, Obj_L= DAE_system()
#
# Objective term
# Formulate discrete time dynamics
# Fixed step Runge-Kutta 4 integrator
MPC_ = MPC(System, 100, penalize_u=False, collocation_degree=8)
Sys = System()
dt, x0, _, _, _ = Sys.specifications()
F = Sys.integrator_model()
his_x = np.zeros([1000, len(x0)])
his_u = np.zeros([1000, 1])
t = 0.
u_apply = np.array([0.])
for i in range(1000):
his_x[i] = x0
u_opt, x_opt, w_opt = MPC_.solve_MPC(x=x0, u=u_apply, ref=0.4, t=t)
u_apply = np.array(u_opt)[:, 0]
his_u[i] = u_apply
x1 = F(x0=x0, p=u_apply)
from casadi import *
import numpy as np
from Dynamic_system import polymer_CSTR as System #specifications, DAE_system, integrator_model
from utilities import MPC
import matplotlib.pyplot as plt
import time
MPC_ = MPC(System, 1, penalize_u=False, collocation_degree=8)
Sys = System()
dt, x0, _, _, _ = Sys.specifications()
F = Sys.integrator_model()
his_x = np.zeros([1000, len(x0)])
his_u = np.zeros([1000, 1])
t = 0.
u_apply = np.array([0.1675])
his_t = np.zeros([1000])
for i in range(1000):
his_x[i] = x0
start = time.time()
u_opt, x_opt, w_opt = MPC_.solve_MPC(x=x0,
u=u_apply,
ref=[
3.0780178085943333,
0.14874773146466175,
0.019293038350734066 * 100,
292.54885691405192 / 100
],
t=t) #
his_t[i] = time.time() - start
u_apply = np.array(u_opt)[:, 0]
his_u[i] = u_apply
x1 = F(x0=x0, p=u_apply)
from casadi import *
import numpy as np
from Dynamic_system import Semi_Batch as System #specifications, DAE_system, integrator_model
from utilities import MPC
import matplotlib.pyplot as plt
MPC_ = MPC(System, 20, penalize_u=True, collocation_degree=8)
# solver = nlpsol('solver', 'ipopt', prob)#'bonmin', prob, {"discrete": discrete})#'ipopt', prob, {'ipopt.output_file': 'error_on_fail'+str(ind)+'.txt'})#
#
# # Solve the NLP
# sol = solver(x0=w0, lbx=lbw, ubx=ubw, lbg=lbg, ubg=ubg, p = np.array([1.,100.,0.]))
Sys = System()
dt, x0, _, _, _ = Sys.specifications()
F = Sys.integrator_model()
his_x = np.zeros([1000, len(x0)])
his_u = np.zeros([1000, 2])
u_apply = np.array([0., 0.])
t = 0.
for i in range(20):
his_x[i] = x0
u_opt, x_opt, w_opt = MPC_.solve_MPC(x=x0, u=u_apply, t=t)
u_apply = np.array(u_opt)[:, 0]
his_u[i] = u_apply
x1 = F(x0=x0, p=u_apply)
x0 = np.array(x1['xf']).reshape((-1, ))
t += dt
plt.plot(his_x[:20, 0])
N = 6 # number of control intervals
# nk, tf, x0, Lsolver, c_code = specifications()
#
# xd, xa, u, uncertainty, ODEeq, Aeq, u_min, u_max, states, \
# algebraics, inputs, nd, na, nu, nmp, modparval, ng, gfcn, Obj_M, Obj_L= DAE_system()
#
# Objective term
# Formulate discrete time dynamics
# Fixed step Runge-Kutta 4 integrator
M = 4 # RK4 steps per interval
MPC_ = MPC(System, 12, penalize_u=False)
u_opt, x_opt, w_opt = MPC_.solve_MPC(np.array([1, 150, 0]), t=0.)
# solver = nlpsol('solver', 'ipopt', prob)#'bonmin', prob, {"discrete": discrete})#'ipopt', prob, {'ipopt.output_file': 'error_on_fail'+str(ind)+'.txt'})#
#
# # Solve the NLP
# sol = solver(x0=w0, lbx=lbw, ubx=ubw, lbg=lbg, ubg=ubg, p = np.array([1.,100.,0.]))
Sys = System()
dt, x0, _, _, _ = Sys.specifications()
F = Sys.integrator_model()
his_x = np.zeros([1000, len(x0)])
his_u = np.zeros([1000, 2])
t = 0.
for i in range(6):
his_x[i] = x0
u_opt, x_opt, w_opt = MPC_.solve_MPC(x=x0, t=t)