Inverted-Pendulum-Neural-Ne.../analysis/simulation.py

59 lines
2.1 KiB
Python

import os
import numpy as np
import torch
from PendulumController import PendulumController
def pendulum_ode_step(state, dt, desired_theta, controller):
theta, omega, alpha = state
def compute_torque(th, om, al):
inp = torch.tensor([[th, om, al, desired_theta]], dtype=torch.float32)
with torch.no_grad():
torque = controller(inp)
torque = torch.clamp(torque, -250, 250)
return float(torque)
def derivatives(state, torque):
th, om, al = state
a = (9.81 / 1.0) * np.sin(th) + torque / (10.0 * 1.0**2)
return np.array([om, a, 0]) # dtheta, domega, dalpha
# RK4 integration
torque1 = compute_torque(theta, omega, alpha)
k1 = dt * derivatives(state, torque1)
state_k2 = state + 0.5 * k1
torque2 = compute_torque(state_k2[0], state_k2[1], state_k2[2])
k2 = dt * derivatives(state_k2, torque2)
state_k3 = state + 0.5 * k2
torque3 = compute_torque(state_k3[0], state_k3[1], state_k3[2])
k3 = dt * derivatives(state_k3, torque3)
state_k4 = state + k3
torque4 = compute_torque(state_k4[0], state_k4[1], state_k4[2])
k4 = dt * derivatives(state_k4, torque4)
new_state = state + (k1 + 2 * k2 + 2 * k3 + k4) / 6.0
# Calculate the pseudo torque applied at the intervale based on the torques at each substep
torque = (torque1 + 2 * torque2 + 2 * torque3 + torque4) / 6.0
return new_state, torque
def run_simulation(params):
controller_file, initial_condition, controller_dir, dt, num_steps = params
controller_path = os.path.join(controller_dir, controller_file)
controller = PendulumController()
controller.load_state_dict(torch.load(controller_path))
controller.eval()
theta0, omega0, alpha0, desired_theta = initial_condition
state = np.array([theta0, omega0, alpha0])
state_history = []
torque_history = []
for _ in range(num_steps):
state, torque = pendulum_ode_step(state, dt, desired_theta, controller)
state_history.append(state)
torque_history.append(torque)
epoch = int(controller_file.split('_')[1].split('.')[0])
return epoch, state_history, torque_history