TinyMPC: Model-Predictive Control on Resource-Constrained Microcontrollers

Model-predictive control (MPC) is a powerful tool for controlling highly dynamic robotic systems subject to complex constraints. However, MPC is computationally demanding, and is often impractical to implement on small, resource-constrained robotic platforms. We present TinyMPC, a high-speed MPC solver with a low memory footprint targeting the microcontrollers common on small robots. Our approach is based on the alternating direction method of multipliers (ADMM) and leverages the structure of the MPC problem for efficiency. We demonstrate TinyMPC’s effectiveness by bench-marking against the state-of-the-art solver OSQP, achieving nearly an order of magnitude speed increase, as well as through hardware experiments on a 27 gram quadrotor, demonstrating high-speed trajectory tracking and dynamic obstacle avoidance. TinyMPC is publicly available at https://tinympc.org.

Authors

• Khai Nguyen
• Sam Schoedel
• Anoushka Alavilli
• Brian Plancher
• Zachary Manchester

Keywords

• Microcontrollers
• Trajectory tracking
• Hardware
• Convex functions
• Collision avoidance
• Robots
• Predictive control
• Model Predictive Control
• Structural Problems
• Obstacle Avoidance
• Memory Footprint
• Dynamic Obstacles
• Model Predictive Control Problem
• Hardware Experiments
• Optimal Control
• Control Input
• Control Problem
• Statistical Properties
• Matrix Factorization
• Lagrange Multiplier
• Convex Optimization
• Quadratic Programming
• Linear Constraints
• Input Dimension
• Reactive Control
• Slack Variables
• State Constraints
• Linear Quadratic Regulator
• Input Constraints
• Riccati Equation
• Flash Memory
• Entire Horizon
• Linear Projection
• Linear Inequality Constraints
• Linear Term