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SysIDModel.py
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SysIDModel.py
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import numpy as np
from matplotlib import pyplot as plt
import torch
import torch.nn as nn
import torch.optim as optim
from torchdiffeq import odeint
from data_loader import TrainSet
from torch.utils.data import DataLoader
import gc
# SystemID.py: Perform system identification for physics-based quadrotor models
class SysID(nn.Module):
# Optimizes specific parameters within the quadrotor dynamics model (configured for motor thrust params)
def __init__(self):
super().__init__()
# Initialized to previously calculated parameters
self.a = torch.nn.Parameter(torch.FloatTensor([[0.0011], [-0.0005], [0.001], [-0.0001]]))
self.b = torch.nn.Parameter(torch.FloatTensor([[-0.0069], [-0.0069], [-0.0088], [-0.0121]]))
self.c = torch.nn.Parameter(torch.FloatTensor([[2.2929], [2.5556], [2.2989], [2.5572]]))
self.a.requires_grad = True
self.b.requires_grad = True
self.c.requires_grad = True
def forward(self, commands):
# Calculates motor thrust given input commands and learned motor commands
n1 = torch.mul(torch.mul(commands, commands), self.a)
n2 = torch.mul(commands, self.b)
return n1+n2+self.c
class QuadrotorDynamics(nn.Module):
# Physics-based quadrotor model integrating the learned component for system identification
def __init__(self, l, m, d, kt, kr, ixx, iyy, izz):
super().__init__()
self.l = l # Quadrotor arm length
self.m = m # Quadrotor mass
self.d = d # Quadrotor blade coefficient
self.kt = kt # Translational drag coefficient
self.kr = kr # Rotational drag coefficient
self.I = torch.tensor([[ixx, 0, 0], [0, iyy, 0], [0, 0, izz]]) # Quadrotor inertia tensor
self.param_net = SysID() # Initialize system ID learned component
self.torque_mat = torch.tensor([[1, 1, 1, 1], # Vectorized torque calculation matrix
[0.707 * self.l, -0.707 * self.l, -0.707 * self.l, 0.707 * self.l],
[-0.707 * self.l, -0.707 * self.l, 0.707 * self.l, 0.707 * self.l],
[-self.d, self.d, -self.d, self.d]])
self.select = torch.tensor([[0, 0, 0, 0], # Vectorized acceleration calculation matrix
[0, 0, 0, 0],
[1/self.m, 0, 0, 0]]).type(torch.FloatTensor)
self.g = torch.tensor([[0], [0], [9.8067]]) # Gravitational acceleration vector
def forward(self, t, input):
# Calculate quadrotor state derivative for numerical integration
commands = input[-4:, :]
state = input[:-4, :]
ang = state[0:3, 0]
rate = state[6:9, :]
vel = state[9:12, :]
thrusts = self.param_net(commands) # update thrusts
torques = torch.mm(self.torque_mat, thrusts) # update torques
# Calculate rotation matrix
s_phi = (torch.sin(ang[0])).item()
c_phi = (torch.cos(ang[0])).item()
s_theta = (torch.sin(ang[1])).item()
c_theta = (torch.cos(ang[1])).item()
s_psi = (torch.sin(ang[2])).item()
c_psi = (torch.cos(ang[2])).item()
rbi = torch.tensor(
[[c_theta * c_psi, c_psi * s_theta * s_phi - c_phi * s_psi, c_phi * c_psi * s_theta + s_phi * s_psi],
[c_theta * s_psi, s_psi * s_theta * s_phi + c_phi * c_psi, c_phi * s_psi * s_theta - s_phi * c_psi],
[-s_theta, c_theta * s_phi, c_theta * c_phi]])
# Calculate Euler angle time derivatives
M = torch.tensor([[1, 0, -s_phi], [0, c_phi, s_phi * c_theta], [0, -s_phi, c_theta * c_phi]])
m_inv = torch.inverse(M)
ang_dot = torch.mm(m_inv, rate)
# Linear Acceleration
vel_dot = torch.mm(rbi, torch.mm(self.select, torques)) - self.kt * vel - self.g
# Rotational Acceleration
rate_dot = torch.mm(torch.inverse(self.I), torques[1:] - torch.cross(rate, torch.mm(self.I, rate), dim=0)
- self.kr * rate)
# Concatenate into final state derivative vector
state_dot = torch.cat([ang_dot, vel, rate_dot, vel_dot, torch.zeros((4, 1))])
return state_dot
def system_id(loss, model, train_loader, val_loader, device, bs, epochs, lr, wd, train_len, val_len, name):
# Performs nonlinear system ID in PyTorch
optimizer = optim.Adam(list(model.parameters()), lr=lr, weight_decay=wd) # Define Adam optimization algorithm
train_loss = []
val_loss = []
best_loss = 0
best_epoch = 0
print("Training Length: {}".format(int(train_len / bs)))
for epoch in range(1, epochs + 1):
model.train(True)
epoch_train_loss = 0
epoch_val_losses = []
i = 0
# Training
for data in train_loader:
batch_inputs = torch.transpose(data["input"].type(torch.FloatTensor), 1, 2).to(device)
batch_labels = torch.transpose(data["label"].type(torch.FloatTensor), 1, 2).to(device)[:, 6:12, :]
optimizer.zero_grad()
# Calculate predicted state across input batch
pred = torch.zeros(batch_labels.shape)
for j in range(batch_inputs.shape[0]):
input = batch_inputs[j, :, :]
output = odeint(model, input, torch.tensor([0, 0.01])) # Forward integrating derivate prediction
pred[j, :, :] = output[1, 6:12, :]
# Calculate loss & backpropagate
loss = loss(pred, batch_labels)
epoch_train_loss += bs * loss.detach().item() / train_len
if loss.requires_grad:
loss.backward()
optimizer.step()
i += 1
if i % 20 == 0:
print("Training {}% finished".format(round(100 * i * bs / train_len, 4)))
print(epoch_train_loss * train_len / (i * bs))
gc.collect() # Check and mitigate memory leaks
train_loss.append(epoch_train_loss)
print("Training Error for this Epoch: {}".format(epoch_train_loss))
# Validation
print("Validation")
model.train(False)
model.eval()
i = 0
with torch.no_grad():
for data in val_loader:
batch_inputs = torch.transpose(data["input"].type(torch.FloatTensor), 1, 2).to(device)
batch_labels = torch.transpose(data["label"].type(torch.FloatTensor), 1, 2).to(device)[:, 6:12, :]
optimizer.zero_grad()
# Calculate predicted state across input batch
pred = torch.zeros(batch_labels.shape)
for j in range(batch_inputs.shape[0]):
input = batch_inputs[j, :, :]
output = odeint(model, input, torch.tensor([0, 0.01])) # Forward integrating derivate prediction
pred[j, :, :] = output[1, 6:12, :]
# Calculate loss
loss = loss(pred, batch_labels)
epoch_val_losses.append(loss.detach().item())
i += 1
if i % 80 == 0:
print(i)
val_loss.append(np.mean(epoch_val_losses))
print("Validation Error for this Epoch: {}".format(np.mean(epoch_val_losses)))
if best_epoch == 0 or np.mean(epoch_val_losses) < best_loss:
best_loss = np.mean(epoch_val_losses)
best_epoch = epoch
torch.save(model.state_dict(), name)
# Plotting
plt.plot(train_loss, linewidth=2)
plt.plot(val_loss, linewidth=2)
plt.xlabel("Epoch")
plt.ylabel("MSE Loss")
plt.legend(["Training Loss", "Validation Loss"])
plt.savefig("MHybrid_1Step_losses_intermediate.png")
plt.show()
print("Training Complete")
print("Best Validation Error ({}) at epoch {}".format(best_loss, best_epoch))
# Plot Final Training Errors
plt.plot(train_loss, linewidth=2)
plt.plot(val_loss, linewidth=2)
plt.xlabel("Epoch")
plt.ylabel("MSE Loss")
plt.legend(["Training Loss", "Validation Loss"])
plt.savefig("MHybrid_1Step_losses.png")
plt.show()
if __name__ == "__main__":
# Simulation Model Parameters
l = 0.211 # length (m)
d = 1.7e-5 # blade parameter
m = 1 # mass (kg)
kt = 2.35e-14 # translational drag coefficient
kr = 0.0099 # rotational drag coefficient
ixx = 0.002 # moment of inertia about X-axis
iyy = 0.002 # moment of inertia about Y-axis
izz = 0.001 # moment of inertia about Z-axis
# Training hyperparameters
bs = 16
lookback = 1
lr = 0.001
wd = 0.0005
epochs = 10
pred_steps = 1
# Define training/validation datasets and dataloaders
tv_set = TrainSet('data/AscTec_Pelican_Flight_Dataset.mat', lookback, pred_steps, full_state=True)
train_len = int(len(tv_set) * 0.8)
val_len = len(tv_set) - train_len
train_set, val_set = torch.utils.data.random_split(tv_set, [train_len, val_len], torch.Generator())
train_loader = torch.utils.data.DataLoader(train_set, batch_size=bs, shuffle=True, num_workers=0)
val_loader = torch.utils.data.DataLoader(val_set, batch_size=bs, shuffle=True, num_workers=0)
print("Data Loaded Successfully")
if torch.cuda.is_available():
device = torch.device("cuda:0")
torch.set_default_tensor_type("torch.cuda.FloatTensor")
print("GPU")
else:
device = torch.device("cpu")
print("CPU")
# Main training loop
model = QuadrotorDynamics(l, m, d, kt, kr, ixx, iyy, izz)
system_id(nn.MSELoss(), model, train_loader, val_loader, device, bs, epochs, lr, wd, train_len, val_len, "SysID")