Speaker
Description
The growing use of quadrotors in fields like delivery services, infrastructure inspection, industrial agriculture, and surveillance has increased the demand for their autonomous capabilities, including path-following control. Existing methods, such as backstepping, feedback linearization, and learning-based techniques like deep learning, have proven effective. The present work novelly uses model predictive path-following control to address the path-following problem for quadrotors. This approach has, so far, mostly been applied to planar and industrial robots.
The proposed model predictive path-following controller extends prior work on trajectory tracking model predictive control. Therein, a timing law is introduced to render the path parameter time-dependent. This timing law includes a virtual input to steer the path parameter and its associated dynamics within the optimal control problem. The deviation of the predicted output compared to the predicted evolution of the path is then penalized through a quadratic cost function, similar to established results from model predictive control. Stability is ensured through stabilizing terminal conditions designed using the quasi-infinite horizon approach. The key advantage of this control scheme over previous approaches is its explicit inclusion of state and input constraints while using the quadrotor’s full potential to track the desired path. Unlike conventional methods, which often rely on complex pre-processing algorithms to transform a path into a trajectory under such constraints, this approach integrates trajectory generation directly into the optimal control problem.
The control scheme is validated on three geometric paths, a spiral, a lemniscate, and a straight line. Simulations are first conducted in MATLAB/Simulink and then in greater detail using CrazySim, a Gazebo-based simulation framework that incorporates firmware and communication infrastructure. Additionally, the numerical properties of the optimal control problem are analysed to improve computation time by leveraging the inherent structure of the resulting nonlinear program.
It is also investigated how the control scheme can be employed for real-world hardware, using the CrazyFlie platform from Bitcraze, to validate the simulation results.
