A qLPV-MPC Control Strategy for Trajectory Tracking of Quadrotors

Abstract

This article proposes a model predictive control (MPC) strategy for a quadrotor drone trajectory tracking based on a compact state-space model based on a quasi-linear parameter varying (qLPV) representation of the nonlinear quadrotor. The use of a qLPV representation allows for faster execution times, which can be suitable for real-time applications and for solving the optimization problem using quadratic programming (QP). The estimation of future values of the scheduling parameters along the prediction horizon is made by using the planned trajectory based on the previous optimal control actions. The performance of the proposed approach is tested by following different trajectories in simulation to show the effectiveness of the proposed control scheme.

Fast Nonlinear Predictive Control Using Classical and Parallel Wiener Models: A Comparison for a Neutralization Reactor Process

“…MPC algorithms have been used for years in process control; typical applications include chemical reactors [2], olefin metathesis processes [3], distillation towers [4] and power plants [5]. Nowadays, as a result of the availability of fast and relatively cheap hardware platforms necessary to carry out all online calculations, MPC algorithms are used in smart buildings [6] and several embedded systems; example applications include autonomous ground vehicle [7], autonomous driving vehicle [8], planning vehicle-parking trajectories for vertical parking spaces [9] and quadrotors [10,11]. Finally, MPC algorithms may control distributed parameter systems [12].…”