415 lines
13 KiB
Python
415 lines
13 KiB
Python
import pickle
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from pathlib import Path
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import casadi as cs
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import numpy as np
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import pandas as pd
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import gpflow
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import tensorflow as tf
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from sklearn.preprocessing import MinMaxScaler
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import callbacks
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from helpers import *
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class PIDcontroller:
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def __init__(self, P, I = 0, D = 0):
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self.P = P
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self.I = I
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self.D = D
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self.ref = 22
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self.err_acc = 0
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self.err_old = 0
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def get_control(self, y):
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"""
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y: Measured output
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"""
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err= self.ref - y
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self.err_acc += err
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sig_P = self.P * err
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sig_I = self.I * self.err_acc
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sig_D = self.D * (err - self.err_old)
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self.err_old = err
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#print(f"P: {sig_P}, I: {sig_I}, D: {sig_D}")
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return sig_P + sig_I + sig_D
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class GP_MPCcontroller:
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def __init__(self, dict_cols, model = None, scaler = None, N_horizon = 10, recover_from_crash = False):
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self.recover_from_crash = recover_from_crash
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if self.recover_from_crash:
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self.model = pickle.load(open("controller_model.pkl", 'rb'))
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self.scaler = pickle.load(open("controller_scaler.pkl", 'rb'))
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self.X_log = pickle.load(open("controller_X_log.pkl", 'rb'))
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df = pd.read_pickle("controller_df.pkl")
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self.recovery_signal = iter(df['SimulatedHeat'])
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if model is not None:
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# Model is already trained. Using as is.
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if scaler is None: raise ValueError("Not allowed to pass a model without a scaler")
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self.model = model
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self.cs_model = callbacks.GPR("gpr", self.model)
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self.scaler = scaler
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self.scaler_helper = ScalerHelper(self.scaler)
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else:
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# No model has been passed. Setting up model initialization
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self.model = None
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self.nb_data = 500 + 1
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self.ident_signal = get_random_signal(self.nb_data, signal_type = 'analog')
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self.Pel = 2 * 6300
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self.COP = 5.0
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self.ident_signal = iter(self.Pel * self.COP * self.ident_signal)
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self.dict_cols = dict_cols
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self.max_lag = max([lag for lag,_ in self.dict_cols.values()])
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self.N_horizon = N_horizon
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self.X_log = []
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# Complete measurement history
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# Columns are: [SolRad, OutsideTemp] (Disturbance), Heat(Input), Temperature (Output)
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self.data_cols = []
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for lags, cols in self.dict_cols.values():
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self.data_cols += cols
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self.data = np.empty((0, len(self.data_cols)))
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# The current weather forcast
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self.weather_forecast = None
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# Current measurements
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self.w, self.u, self.y = None, None, None
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###
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# GPflow model training and update
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###
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def _train_model(self):
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""" Identify model from gathered data """
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nb_train_pts = self.nb_data - 1
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###
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# Dataset
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###
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df = pd.DataFrame(self.data[:nb_train_pts], columns = self.data_cols)
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self.scaler = MinMaxScaler(feature_range = (-1, 1))
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self.scaler_helper = ScalerHelper(self.scaler)
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df_sc = get_scaled_df(df, self.dict_cols, self.scaler)
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df_gpr_train = data_to_gpr(df_sc, self.dict_cols)
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df_input_train = df_gpr_train.drop(columns = self.dict_cols['w'][1] + self.dict_cols['u'][1] + self.dict_cols['y'][1])
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df_output_train = df_gpr_train[self.dict_cols['y'][1]]
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np_input_train = df_input_train.to_numpy()
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np_output_train = df_output_train.to_numpy().reshape(-1, 1)
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data_train = (np_input_train, np_output_train)
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df_test = pd.DataFrame(self.data[nb_train_pts:], columns = self.data_cols)
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df_test_sc = get_scaled_df(df_test, self.dict_cols, self.scaler)
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df_gpr_test = data_to_gpr(df_test_sc, self.dict_cols)
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df_input_test = df_gpr_test.drop(columns = self.dict_cols['w'][1] + self.dict_cols['u'][1] + self.dict_cols['y'][1])
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df_output_test = df_gpr_test[self.dict_cols['y'][1]]
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np_input_test = df_input_test.to_numpy()
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np_output_test = df_output_test.to_numpy()
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###
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# Kernel
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###
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nb_dims = np_input_train.shape[1]
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rational_dims = np.arange(0, (self.dict_cols['t'][0] + 1) * len(self.dict_cols['t'][1]), 1)
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nb_rational_dims = len(rational_dims)
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squared_dims = np.arange(nb_rational_dims, nb_dims, 1)
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nb_squared_dims = len(squared_dims)
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default_lscale = 75
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while True:
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try:
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squared_l = np.linspace(default_lscale, 2*default_lscale, nb_squared_dims)
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rational_l = np.linspace(default_lscale, 2*default_lscale, nb_rational_dims)
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variance = tf.math.reduce_variance(np_input_train)
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k0 = gpflow.kernels.SquaredExponential(lengthscales = squared_l, active_dims = squared_dims, variance = variance)
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k1 = gpflow.kernels.Constant(variance = variance)
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k2 = gpflow.kernels.RationalQuadratic(lengthscales = rational_l, active_dims = rational_dims, variance = variance)
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k3 = gpflow.kernels.Periodic(k2)
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k = (k0 + k1) * k2
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k = k0
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###
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# Model
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###
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m = gpflow.models.GPR(
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data = data_train,
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kernel = k,
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mean_function = None,
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)
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###
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# Training
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###
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print(f"Training a model with lscale:{default_lscale}")
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opt = gpflow.optimizers.Scipy()
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opt.minimize(m.training_loss, m.trainable_variables)
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break
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except:
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print(f"Failed.Increasing lengthscale")
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default_lscale += 5
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###
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# Save model
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###
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self.model = m
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self.n_states = self.model.data[0].shape[1]
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# # Manual model validation
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# import matplotlib.pyplot as plt
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#
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# plt.figure()
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# plt.plot(data_train[1], label = 'real')
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# mean, var = self.model.predict_f(data_train[0])
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# plt.plot(mean, label = 'model')
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# plt.legend()
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# plt.show()
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#
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# plt.figure()
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# plt.plot(np_output_test, label = 'real')
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# mean, var = self.model.predict_f(np_input_test)
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# plt.plot(mean, label = 'model')
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# plt.legend()
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# plt.show()
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#
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# import pdb; pdb.set_trace()
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pass
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###
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# Update measurements
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###
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def add_disturbance_measurement(self, w):
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self.w = np.array(w).reshape(1, -1)
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def add_output_measurement(self, y):
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self.y = np.array(y).reshape(1, -1)
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def _add_input_measurement(self, u):
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self.u = np.array(u).reshape(1, -1)
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def set_weather_forecast(self, W):
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assert (W.shape[0] == self.N_horizon)
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self.weather_forecast = W
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def update_model(self):
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new_data = np.hstack([self.w, self.u, self.y])
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print(f"Adding new data: {new_data}")
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self.data = np.vstack([self.data, new_data])
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print(f"Data size: {self.data.shape[0]}")
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self.w, self.u, self.y = None, None, None
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print("---")
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###
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# Set up optimal problem solver
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###
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def _setup_solver(self):
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###
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# Initialization
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###
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self.cs_model = callbacks.GPR("gpr", self.model)
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T_set = 21
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T_set_sc = self.scaler_helper.scale_output(T_set)
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X = cs.MX.sym("X", self.N_horizon + 1, self.n_states)
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x0 = cs.MX.sym("x0", 1, self.n_states)
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W = cs.MX.sym("W", self.N_horizon, 2)
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g = []
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lbg = []
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ubg = []
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lbx = -np.inf*np.ones(X.shape)
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ubx = np.inf*np.ones(X.shape)
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u_idx = self.dict_cols['w'][0] * len(self.dict_cols['w'][1])
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y_idx = u_idx + self.dict_cols['u'][0] * len(self.dict_cols['u'][1])
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# Stage cost
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u_cost = 0.05 * cs.dot(X[:, u_idx], X[:, u_idx])
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u_cost = 0
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y_cost = cs.dot(X[:, y_idx] - T_set_sc, X[:, y_idx] - T_set_sc)
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J = u_cost + y_cost
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# Set up equality constraints for the first step
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for idx in range(self.n_states):
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g.append(X[0, idx] - x0[0, idx])
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lbg.append(0)
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ubg.append(0)
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# Set up equality constraints for the following steps
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for idx in range(1, self.N_horizon + 1):
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base_col = 0
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# w
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nb_cols = self.dict_cols['w'][0]
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for w_idx in range(W.shape[1]):
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w_base_col = w_idx * nb_cols
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g.append(X[idx, base_col + w_base_col] - W[idx - 1, w_idx])
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lbg.append(0)
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ubg.append(0)
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for w_lag_idx in range(1, nb_cols):
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g.append(X[idx, base_col + w_base_col + w_lag_idx] - X[idx - 1, base_col + w_base_col + w_lag_idx - 1])
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lbg.append(0)
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ubg.append(0)
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base_col += nb_cols * W.shape[1]
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# u
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nb_cols = self.dict_cols['u'][0]
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lbx[idx, base_col] = -1 #lower bound on input
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ubx[idx, base_col] = 1 #upper bound on input
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for u_lag_idx in range(1, nb_cols):
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g.append(X[idx, base_col + u_lag_idx] - X[idx - 1, base_col + u_lag_idx - 1])
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lbg.append(0)
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ubg.append(0)
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base_col += nb_cols
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# y
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nb_cols = self.dict_cols['y'][0]
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g.append(X[idx, base_col] - self.cs_model(X[idx - 1, :]))
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lbg.append(0)
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ubg.append(0)
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for y_lag_idx in range(1, nb_cols):
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g.append(X[idx, base_col + y_lag_idx] - X[idx - 1, base_col + y_lag_idx - 1])
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lbg.append(0)
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ubg.append(0)
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p = cs.vertcat(cs.vec(W), cs.vec(x0))
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prob = {'f': J, 'x': cs.vec(X), 'g': cs.vertcat(*g), 'p': p}
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options = {"ipopt": {"hessian_approximation": "limited-memory", "max_iter": 100,
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#"acceptable_tol": 1e-6, "tol": 1e-6,
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#"linear_solver": "ma57",
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#"acceptable_obj_change_tol": 1e-5,
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#"mu_strategy": "adaptive",
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"print_level": 0
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}}
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self.lbg = lbg
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self.ubg = ubg
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self.lbx = lbx
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self.ubx = ubx
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self.solver = cs.nlpsol("solver","ipopt",prob, options)
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print("Initialized casadi solver")
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###
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# Compute control signal
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###
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def get_control_input(self):
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# Recovery pre-loop
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if self.recover_from_crash:
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try:
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u = next(self.recovery_signal)
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self._add_input_measurement(u)
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return u
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except StopIteration:
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# Finished passing in the pre-existing control
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# Switching to normal operation
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self.recover_from_crash = False
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# Training pre-loop
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if self.model is None:
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try:
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# No model yet. Sending next step of identification signal
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u = next(self.ident_signal)
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self._add_input_measurement(u)
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return u
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except StopIteration:
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# No more identification signal. Training a model and proceeding
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self._train_model()
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self._setup_solver()
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# Continue now since model exists
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# Model exists. Compute optimal control input
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data_scaled = self.scaler.transform(self.data)
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df = pd.DataFrame(data_scaled, columns = self.data_cols)
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x0 = data_to_gpr(df, self.dict_cols).drop(
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columns = self.dict_cols['w'][1] + self.dict_cols['u'][1] + self.dict_cols['y'][1]
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).to_numpy()[-1, :]
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x0 = cs.vec(x0)
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W = self.scaler_helper.scale_weather(self.weather_forecast)
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W = cs.vec(W)
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p = cs.vertcat(W, x0)
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res = self.solver(
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x0 = 1,
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lbx = cs.vec(self.lbx),
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ubx = cs.vec(self.ubx),
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p = p,
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lbg = cs.vec(self.lbg),
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ubg = cs.vec(self.ubg)
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)
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X = np.array(res['x'].reshape((self.N_horizon + 1, self.n_states)))
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self.X_log.append(X)
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u_idx = self.dict_cols['w'][0] * len(self.dict_cols['w'][1])
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# Take the first control action and apply it
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u = X[1, u_idx]
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# Unpack u after scaling since ScalerHelper returns a list/array
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[u] = self.scaler_helper.inverse_scale_input(u)
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self._add_input_measurement(u)
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return u
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def save_data(self):
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df = pd.DataFrame(self.data, columns = self.data_cols)
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df.to_pickle("controller_df.pkl")
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pickle.dump(self.scaler, open(Path("controller_scaler.pkl"), 'wb'))
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pickle.dump(self.model, open(Path("controller_model.pkl"), 'wb'))
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pickle.dump(self.X_log, open(Path("controller_X_log.pkl"), 'wb'))
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return
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class TestController:
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def __init__(self):
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return
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def get_control_input(self): return 2 * 3600 * 5.0 * np.random.rand()
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def add_disturbance_measurement(self, w): return
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def set_weather_forecast(self, W): return
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def add_output_measurement(self, y): return
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def update_model(self): return
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def save_data(self): return
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