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multizone_building_MPC_layer.py
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import cvxpy as cp
from cvxpy import *
import numpy as np
import scipy as sp
from scipy import sparse
from pylab import *
import time
import neuromancer.psl as psl
from cvxpylayers.torch.cvxpylayer import CvxpyLayer
import torch
from neuromancer.plot import pltCL
import building_utils
import pickle
import os
def get_building_MPC_layer(N,nu,nx,ny,nd, umin,umax, A,C,E,F,G,y_ss, Q_weight,R_weight):
Q = Q_weight * sparse.eye(ny)
QN = Q
R = R_weight * sparse.eye(nu)
# Define problem
u = Variable((N, nu))
x = Variable((N+1,nx))
y = Variable((N+1,ny))
B = Parameter((nx,nu))
d = Parameter((N+1,nd))
slack_lower = Variable((N+1,ny))
slack_upper = Variable((N+1,ny))
x_init = Parameter(nx)
ymin = Parameter((N+1,ny))
ymax = Parameter((N+1,ny))
objective = 0
constraints = [x[0,:] == x_init]
for k in range(N):
constraints += [x[k+1,:] == A @ x[k,:] + B @ u[k,:] + E @ d[k,:] + G]
constraints += [u[k,:] <= umax]
constraints += [u[k,:] >= umin]
objective += quad_form(u[k,:],R)
for k in range(N+1):
constraints += [y[k,:] == C @ x[k,:] + F - y_ss]
#constraints += [y[k,:] >= ymin[k,:] - slack_lower[k,:]]
#constraints += [y[k,:] <= ymax[k,:] + slack_upper[k,:]]
#constraints += [slack_lower[k,:] >= 0]
#constraints += [slack_upper[k,:] >= 0]
objective += quad_form(slack_upper[k,:], QN) + quad_form(slack_lower[k,:], QN)
constraints += [y >= ymin - slack_lower]
constraints += [y <= ymax + slack_upper]
constraints += [slack_lower >= 0]
constraints += [slack_upper >= 0]
prob = Problem(Minimize(objective), constraints)
#cvxlayer_pre = CvxpyLayer(prob, parameters=[x_init,y_init,d,ymin,ymax], variables=[u,x,y,slack_lower,slack_upper])
#def cvxlayer(x_init,y_init,d,ymin,ymax):
# out = cvxlayer_pre(x_init,y_init,d,ymin,ymax)
# return out
return CvxpyLayer(prob, parameters=[B,x_init,d,ymin,ymax], variables=[u,x,y,slack_lower,slack_upper])
def get_building_MPC_layer_hard(N,nu,nx,ny,nd, umin,umax, A,C,E,F,G,y_ss, R_weight):
Q = Q_weight * sparse.eye(ny)
QN = Q
R = R_weight * sparse.eye(nu)
# Define problem
u = Variable((N, nu))
x = Variable((N+1,nx))
y = Variable((N+1,ny))
B = Parameter((nx,nu))
d = Parameter((N+1,nd))
slack_lower = Variable((N+1,ny))
slack_upper = Variable((N+1,ny))
x_init = Parameter(nx)
ymin = Parameter((N+1,ny))
ymax = Parameter((N+1,ny))
objective = 0
constraints = [x[0,:] == x_init]
for k in range(N):
constraints += [x[k+1,:] == A @ x[k,:] + B @ u[k,:] + E @ d[k,:] + G]
constraints += [u[k,:] <= umax]
constraints += [u[k,:] >= umin]
objective += quad_form(u[k,:],R)
for k in range(N+1):
constraints += [y[k,:] == C @ x[k,:] + F - y_ss]
#constraints += [y[k,:] >= ymin[k,:] - slack_lower[k,:]]
#constraints += [y[k,:] <= ymax[k,:] + slack_upper[k,:]]
#constraints += [slack_lower[k,:] >= 0]
#constraints += [slack_upper[k,:] >= 0]
constraints += [y >= ymin - slack_lower]
constraints += [y <= ymax + slack_upper]
constraints += [slack_lower == 0]
constraints += [slack_upper == 0]
prob = Problem(Minimize(objective), constraints)
#cvxlayer_pre = CvxpyLayer(prob, parameters=[x_init,y_init,d,ymin,ymax], variables=[u,x,y,slack_lower,slack_upper])
#def cvxlayer(x_init,y_init,d,ymin,ymax):
# out = cvxlayer_pre(x_init,y_init,d,ymin,ymax)
# return out
return CvxpyLayer(prob, parameters=[B,x_init,d,ymin,ymax], variables=[u,x,y,slack_lower,slack_upper])
def plot_solution(ymin_traj,ymax_traj, x_traj,y_traj,d_traj,u_traj):
# constraints bounds
Umin = umin * np.ones([nsteps, nu])
Umax = umax * np.ones([nsteps, nu])
Ymin = ymin_traj #trajectories['ymin'].detach().reshape(nsteps_test+1, nref)
Ymax = ymax_traj #trajectories['ymax'].detach().reshape(nsteps_test+1, nref)
# plot closed loop trajectories
fig, ax = pltCL(Y= y_traj, #trajectories['y'].detach().reshape(nsteps_test+1, ny),
R=Ymax,
X= x_traj, #trajectories['x'].detach().reshape(nsteps_test+1, nx),
D= d_traj, #trajectories['d'].detach().reshape(nsteps_test+1, nd),
U= u_traj, #trajectories['u'].detach().reshape(nsteps_test, nu),
Umin=Umin, Umax=Umax, Ymin=Ymin, Ymax=Ymax)
# add custom legends to plots
custom_lines = [Line2D([0], [0], color='k', lw=2, linestyle='--'),
Line2D([0], [0], color='tab:blue', lw=2, linestyle='-')]
custom_lines_x = [Line2D([0], [0], color='tab:blue', lw=2, linestyle='-'),
Line2D([0], [0], color='tab:orange', lw=2, linestyle='-'),
Line2D([0], [0], color='tab:green', lw=2, linestyle='-'),
Line2D([0], [0], color='tab:red', lw=2, linestyle='-')]
custom_lines_d = [Line2D([0], [0], color='tab:blue', lw=2, linestyle='-'),
Line2D([0], [0], color='tab:orange', lw=2, linestyle='-'),
Line2D([0], [0], color='tab:green', lw=2, linestyle='-')]
ax[0, 0].legend(custom_lines, ['Bounds', 'Controlled zone temperature'], fontsize=15, loc="best")
ax[1, 0].legend(custom_lines_x, ['Floor temperature', 'Interior facade temperature', 'Exterior facade temperature', 'Controlled zone temperature'], fontsize=15, loc="best")
ax[2, 0].legend(custom_lines, ['Bounds', 'Zone HVAC heat flow'], fontsize=15, loc="best")
ax[3, 0].legend(custom_lines_x, ['Outdoor air temperature', 'Occupant heat load', 'Solar irradiance'], fontsize=15, loc="best")
#custom_lines_d
fig.show()
input("Plotting solution")
if __name__ == "__main__":
torch.manual_seed(0)
np.random.seed(0)
# ground truth system model
sys = psl.systems['LinearSimpleSingleZone'](seed=0)
nzones = 1
# problem dimensions
nx = nzones*sys.nx # number of states
nu = nzones*sys.nu # number of control inputs
nd = nzones*sys.nD # number of disturbances
ny = nzones*sys.ny # number of controlled outputs
nref = ny # number of references
partial_observe = False
if partial_observe: # Toggle partial observability of the disturbance
d_idx = sys.d_idx
else:
d_idx = range(nd)
nd_obs = len(d_idx) # number of observable disturbances
nB = nu*nx # This is the size of B as passed in to the policy network, should be the number of learnable elements
# extract exact state space model matrices:
A = torch.block_diag( *tuple([torch.tensor(sys.A) for _ in range(nzones)]) )
B = torch.block_diag( *tuple([torch.tensor(sys.Beta) for _ in range(nzones)]) )
C = torch.block_diag( *tuple([torch.tensor(sys.C) for _ in range(nzones)]) )
E = torch.block_diag( *tuple([torch.tensor(sys.E) for _ in range(nzones)]) )
F = torch.zeros(ny)
G = torch.zeros(nx)
y_ss = torch.zeros(ny)
# control action bounds
#umin = torch.Tensor([sys.umin.item() for _ in range(nzones)])
umax = torch.Tensor([sys.umax.item() for _ in range(nzones)])
B_scale_factor = umax.max()
umax = umax / B_scale_factor # JK rescaling reduces computation time dramatically
umin = -umax
B = B * B_scale_factor # however, I haven't been able to verify equivalence, due to either solver instability when R!=0 or nonunique solutions when R=0
B_mask = (B != 0.0).float()
nsteps = 50
n_samples = 3
# generate data for a single-zone building
filename = "./data/dist_zone_data_{}steps_{}samples.p".format(nsteps, n_samples)
if not os.path.isfile(filename):
zone_data = building_utils.gen_building_data_single(n_samples, nsteps, nodist = False)
pickle.dump(zone_data,open(filename,'wb'))
else:
zone_data = pickle.load(open(filename,'rb'))
batched_ymin, batched_ymax, batched_dist, batched_x0 = zone_data
# duplicate data across multiple zones
x0 = batched_x0.repeat(1,1,nzones)
y0 = batched_x0[:,:,[-1]].repeat(1,1,nzones)
ymin = batched_ymin.repeat(1,1,nzones)
ymax = batched_ymax.repeat(1,1,nzones)
d = batched_dist.repeat(1,1,nzones)
B = B.repeat(n_samples, 1, 1)
print("sys.E" )
print( sys.E )
print("d" )
print( d )
print("d.shape" )
print( d.shape )
input("waiting")
Q_weight = 5000.0
R_weight = 1.0
diff_solver = get_building_MPC_layer( nsteps,nu,nx,ny,nd, umin,umax, A,C,E,F,G,y_ss, Q_weight, R_weight)
diff_solver_hard = get_building_MPC_layer_hard(nsteps,nu,nx,ny,nd, umin,umax, A,C,E,F,G,y_ss, R_weight)
solver_out = diff_solver(B, x0.squeeze(1), d, ymin, ymax)
u = solver_out[0]
x = solver_out[1]
y = solver_out[2]
slack_upper = solver_out[3]
slack_lower = solver_out[4]
print("x.shape" )
print( x.shape )
print("u.shape" )
print( u.shape )
print("y.shape" )
print( y.shape )
print("x" )
print( x )
print("u" )
print( u )
print("y" )
print( y )
for i in range(n_samples):
plot_solution(ymin[i], ymax[i], x[i], y[i], d[i], u[i])