Real-Time Certified MPC for a Nano Quadcopter

Abstract: There is a constant demand to use more advanced control methods in a wider field of applications. Model Predictive Control (MPC) is one such control method, based on recurrently solving an optimization problem for determining the optimal control signal. To solve an optimization problem can be a complex task, and it is difficult to determine beforehand how long time it will take. For a high-speed application with limited computational power, it is necessary to have an efficient algorithm to solve the optimization problem and an accurate estimation of the longest solution time. Recent research has given methods both to solve quadratic programs efficiently and to find an upper limit on the solution times. These methods are in this thesis applied to a control system based on linear MPC for the Crazyflie 2.0 nano quadcopter. The implementation is made completely online on the processor of the quadcopter, with limited computational power. A problem with the size of 36 optimization variables and 60 constraints is solved at a frequency of 100 Hz on the quadcopter. Apart from implementing MPC, a framework for computing an upper limit to the solution time has been tested. This gives a possibility to certify the formulation for real-time applications up to a well-defined maximum frequency. An implementation is shown where the framework has been used in practice to control a quadcopter flying with a real-time certified implementation of MPC.