Model predictive control (MPC) is a powerful tool for controlling highly dynamic robotic systems subject to complex constraints. However, it is computationally expensive and often requires a large memory footprint. Larger robotic systems are capable of carrying and powering sophisticated computational hardware onboard. On the other hand, smaller robots typically have faster dynamics that require higher-frequency controllers while being restricted to computers with less computational power and memory. Existing algorithms for embedded model-predictive control at small scales are generally inefficient and inaccessible.

This thesis tackles both problems by introducing TinyMPC, a lightweight and convex model-predictive control solver with open-source software packages that facilitates easy implementation with high-level interfaces and code generation. We benchmark TinyMPC against state-of-the-art quadratic and conic programming solvers on different microcontrollers, demonstrating nearly an order-of-magnitude speedup and memory reduction. Finally, we demonstrate TinyMPC’s real world efficacy by deploying it on the Crazyflie 2.1, a 27-gram nano-quadrotor with fast dynamics.

Prof. Zachary Manchester (advisor, chair)

Prof. Aaron Johnson

Brady Moon