A cooperative particle swarm optimization approach for tuning an MPC-based quadrotor trajectory tracking scheme

Abstract

This paper presents a novel method for tuning a quadrotor trajectory-tracking model predictive control (MPC) framework, based on cooperative particle swarm optimization (PSO). The overall control strategy is decomposed into two different schemes; the first scheme, which is responsible for position control, is based on MPC, whereas the second one, which regulates the attitude of the quadrotor, makes use of three standard PID controllers. To optimize the trajectory tracking ability of the quadrotor, a cooperative PSO framework is introduced for tuning the large number of parameters of diverse nature, which are employed by the different controllers. The method makes use of two distinct swarms, with the first one containing the position controller parameters and the second one the attitude controller parameters. By exchanging information, the two swarms work together in a cooperative way in order to explore more efficiently the search space and discover tuning parameters that improve the trajectory tracking performance. Simulation results on a detailed quadcopter model over trajectories with different geometric characteristics, and comparisons to other tuning approaches verified by statistical testing, illustrate the efficiency of the proposed scheme in optimizing the control parameters. The scheme's robustness is also verified by testing the performance of the resulting controller on trajectories different than the ones used for tuning it.