transformermpc 0.1.7

Accelerating Model Predictive Control via Neural Networks

TransformerMPC: Accelerating Model Predictive Control via Transformers [ICRA '25]

Vrushabh Zinage¹ · Ahmed Khalil¹ · Efstathios Bakolas¹

¹University of Texas at Austin

Overview

TransformerMPC improves the computational efficiency of Model Predictive Control (MPC) problems using neural network models. It employs the following two prediction models:

1. Constraint Predictor: Identifies inactive constraints in MPC formulations
2. Warm Start Predictor: Generates better initial points for MPC solvers

By combining these models, TransformerMPC significantly reduces computation time while maintaining solution quality.

Package Structure

The package is organized with a standard Python package structure:

transformermpc/
├── transformermpc/       # Core package module
│   ├── data/             # Data generation utilities
│   ├── models/           # Model implementations
│   ├── utils/            # Utility functions and metrics
│   ├── training/         # Training infrastructure
│   └── demo/             # Demo scripts
├── scripts/              # Demo and utility scripts
├── tests/                # Testing infrastructure
├── setup.py              # Package installation script
└── requirements.txt      # Dependencies

Installation

Install directly from PyPI:

pip install transformermpc

Or install from source:

git clone https://github.com/Vrushabh27/transformermpc.git
cd transformermpc
pip install -e .

Dependencies

• Python >= 3.7
• PyTorch >= 1.9.0
• OSQP >= 0.6.2
• NumPy, SciPy, and other scientific computing libraries
• Additional dependencies specified in requirements.txt

Running the Demos

The package includes several demo scripts to showcase its capabilities:

Boxplot Demo (Recommended)

python scripts/boxplot_demo.py

This script provides a visual comparison of different QP solving strategies using randomly generated problems, without requiring model training. It demonstrates the core concepts behind TransformerMPC by showing the performance impact of:

• Removing inactive constraints
• Using warm starts with different qualities
• Combining these strategies

The visualizations include boxplots, violin plots, and bar charts comparing solve times across different strategies.

Simple Demo

python scripts/simple_demo.py

This script demonstrates the complete pipeline: generating QP problems, training models, and evaluating performance. After completion, it saves performance comparison plots in the demo_results/results directory.

Verify Package Structure

To check that the package is installed correctly:

python scripts/verify_structure.py

Customizing Demo Parameters

You can customize the boxplot demo by modifying parameters:

# Generate more problems with different dimensions
python scripts/boxplot_demo.py --num_samples 50 --state_dim 6 --input_dim 3 --horizon 10

# Save results to a custom directory 
python scripts/boxplot_demo.py --output_dir my_results

Similarly, for the simple demo:

# Generate QP problems with specific parameters
python scripts/simple_demo.py --num_samples 200 --state_dim 6 --input_dim 3 --horizon 10

# Customize training parameters
python scripts/simple_demo.py --epochs 20 --batch_size 32

# Use GPU for training if available
python scripts/simple_demo.py --use_gpu

Usage in Projects

Basic Example

from transformermpc import TransformerMPC
import numpy as np

# Define your QP problem parameters
Q = np.array([[4.0, 1.0], [1.0, 2.0]])
c = np.array([1.0, 1.0])
A = np.array([[-1.0, 0.0], [0.0, -1.0], [-1.0, -1.0], [1.0, 1.0]])
b = np.array([0.0, 0.0, -1.0, 2.0])

# Initialize the TransformerMPC solver
solver = TransformerMPC()

# Solve with model acceleration
solution, solve_time = solver.solve(Q, c, A, b)

print(f"Solution: {solution}")
print(f"Solve time: {solve_time} seconds")

General Usage

from transformermpc import TransformerMPC, QPProblem
import numpy as np

# Define your QP problem parameters
Q = np.array([[4.0, 1.0], [1.0, 2.0]])
c = np.array([1.0, 1.0])
A = np.array([[-1.0, 0.0], [0.0, -1.0], [-1.0, -1.0], [1.0, 1.0]])
b = np.array([0.0, 0.0, -1.0, 2.0])
initial_state = np.array([0.5, 0.5])  # Optional: initial state for MPC problems

# Create a QP problem instance
qp_problem = QPProblem(
    Q=Q,
    c=c,
    A=A,
    b=b,
    initial_state=initial_state  # Optional
)

# Initialize with custom settings
solver = TransformerMPC(
    use_constraint_predictor=True,
    use_warm_start_predictor=True,
    fallback_on_violation=True
)

# Solve the problem
solution, solve_time = solver.solve(qp_problem=qp_problem)
print(f"Solution: {solution}")
print(f"Solve time: {solve_time} seconds")

# Compare with baseline
baseline_solution, baseline_time = solver.solve_baseline(qp_problem=qp_problem)
print(f"Baseline time: {baseline_time} seconds")

If you find our work useful, please cite us

@article{zinage2024transformermpc,
  title={TransformerMPC: Accelerating Model Predictive Control via Transformers},
  author={Zinage, Vrushabh and Khalil, Ahmed and Bakolas, Efstathios},
  journal={arXiv preprint arXiv:2409.09266},
  year={2024}
}

License

This project is licensed under the MIT License.