torchcontrol 0.1.0

A parallel control system simulation and control library based on PyTorch.

torchcontrol

Introduction

torchcontrol is a parallel control system simulation and control library based on PyTorch. It supports batch simulation, classical and modern control, nonlinear systems, GPU acceleration, and visualization. The library is modular and extensible, suitable for both research and teaching.

Features

• Batch simulation: Simulate multiple environments in parallel (vectorized, GPU-friendly)
• Classical & modern control: Built-in PID controller, state-space, transfer function, and nonlinear system support
• Custom plants: Easily define linear or nonlinear plants (systems) with custom dynamics
• GPU acceleration: All computations support CUDA (if available)
• Visualization: Example scripts for step response, PID control, and nonlinear systems with matplotlib output
• Extensible: Modular design for adding new controllers, observers, or plants

Installation

From the project root, run:

pip install .

Or for development mode (auto-reload on code change):

pip install -e .

Directory Structure

• torchcontrol/ # Main package (controllers, plants, system, observers)
• examples/ # Example scripts (PID, nonlinear, batch, visualization)
  • results/ # Output results (figures, logs, etc.)
• README.md
• setup.py
• pyproject.toml

How to Run Examples

After installation, run example scripts from the project root:

python3 examples/pid_with_internal_plant.py
python3 examples/pid_with_external_plant.py
python3 examples/second_order_plant_step_response.py
python3 examples/nonlinear_plant_step_response.py

• All import paths use package-level imports (e.g., from torchcontrol.controllers import PID).
• Output files will be automatically saved in examples/results/.
• All examples support both CPU and GPU (CUDA) if available.

Example: Batch PID Control of Second-Order System

from torchcontrol.controllers import PID, PIDCfg
from torchcontrol.plants import InputOutputSystem, InputOutputSystemCfg
import torch

# System: G(s) = 1/(s^2 + 2s + 1)
dt = 0.01
num_envs = 16
num = [1.0]
den = [1.0, 2.0, 1.0]
initial_states = torch.rand(num_envs, 1) * 2
plant_cfg = InputOutputSystemCfg(numerator=num, denominator=den, dt=dt, num_envs=num_envs, initial_state=initial_states)
plant = InputOutputSystem(plant_cfg)
pid_cfg = PIDCfg(Kp=120.0, Ki=600.0, Kd=30.0, dt=dt, num_envs=num_envs, state_dim=1, action_dim=1, plant=plant)
pid = PID(pid_cfg)
# ...simulate and visualize...

Dependencies

• Python 3.8+
• torch
• torchdiffeq
• scipy
• numpy
• matplotlib

For GPU support, please ensure you have installed the CUDA version of PyTorch.

Customization & Extension

• To add new controllers, inherit from ControllerBase and register a config.
• To add new plants, inherit from PlantBase or use InputOutputSystem/NonlinearSystem.
• See examples/ for advanced usage and batch simulation.

---

For batch simulation, RL interface, or custom controllers/systems, please refer to the code structure in the torchcontrol/ directory.