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 sol...
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| Main Authors | , , , , |
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| Format | Journal Article |
| Language | English |
| Published |
12.08.2025
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| Online Access | Get full text |
| DOI | 10.48550/arxiv.2310.16985 |
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| Abstract | 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 benchmarking 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. |
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| AbstractList | 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 benchmarking 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. |
| Author | Nguyen, Khai Alavilli, Anoushka Plancher, Brian Manchester, Zachary Schoedel, Sam |
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| BackLink | https://doi.org/10.48550/arXiv.2310.16985$$DView paper in arXiv |
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| Snippet | Model-predictive control (MPC) is a powerful tool for controlling highly dynamic robotic systems subject to complex constraints. However, MPC is... |
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| SubjectTerms | Computer Science - Robotics Computer Science - Systems and Control Mathematics - Optimization and Control |
| Title | TinyMPC: Model-Predictive Control on Resource-Constrained Microcontrollers |
| URI | https://arxiv.org/abs/2310.16985 |
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