Nonlinear Model Predictive Control of a Human-sized Quadrotor


This paper discusses the design, implementation and deployment of an attitude controller for a quadrotor based on nonlinear model predictive control on a low-power embedded system equipped with a Cortex A9 CPU running at 800 MHz. Due to the limited computational power of the available hardware, a modified interior-point solver for the so-called partially tightened Real-Time Iteration is used. The algorithm splits the prediction horizon in two sections. A Riccati-like recursion is exploited that relies on a single linearization of the complementarity conditions per sampling-time for the terminal section. In this way, it is possible to achieve a speedup of a factor 3 with respect to a standard real-time iteration formulation for the application under consideration. Simulation results that show the improvement in performance obtained by using NMPC over standard control techniques are discussed and experimental results using the proposed implementation are presented.

2018 European Control Conference (ECC)
Andrea Zanelli
PhD Researcher

Interested in nonlinear optimization, model predictive control and embedded systems.