Experimental quadrotor flight performance using computationally efficient and recursively feasible linear model predictive control

Abstract

A new linear model predictive control (MPC) algorithm in a state-space framework is presented based on the fusion of two past MPC control laws: steady-state optimal MPC (SSOMPC) and Laguerre optimal MPC (LOMPC). The new controller, SSLOMPC, is demonstrated to have improved feasibility, tracking performance and computation time than its predecessors. This is verified in both simulation and practical experimentation on a quadrotor unmanned air vehicle in an indoor motion-capture testbed. The performance of the control law is experimentally compared with proportional-integral-derivative (PID) and linear quadratic regulator (LQR) controllers in an unconstrained square manoeuvre. The use of soft control output and hard control input constraints is also examined in single and dual constrained manoeuvres.

Keywords:

• predictive control
• MPC
• Laguerre functions
• optimal control
• LQR
• PID
• quadrotor
• UAV

Acknowledgements

This research was supported through funding by COMSATS Institute of Information Technology, Lahore, Pakistan.