Design and Implementation of a Constrained Model Predictive Control Approach for Unmanned Aerial Vehicles

This paper designs and implements a robust and Time-varying Constrained Model Predictive Controller (TCMPC) for the translational and attitude control of a real quadrotor. The proposed approach considers online optimization to find solutions through the hard and soft constraints. All the controller parameters were derived from the experimental test setup and took into consideration the various restrictions and physical constraints associated with the hand-made quadrotor. The proposed controller can possibly linearize and discretize the nonlinear dynamic model of the quadrotor at every sampling time if all constraints and physical restrictions are considered. The performance of the proposed approach was assessed using both a simulation study and a practical implementation. The simulation study considered a quadrotor hovering mode in the presence of wind gusts and encompassed a comparison analysis with a well-tuned Proportional-Integral-Derivative (PID) controller, an Advanced Error model predictive control (AEMPC), and an Efficient MPC (EMPC) approach. For the real-time implementation, an online optimization algorithm was used and tested on the high clock processor ARM A53 on a new attitude test setup. The experimental results, showed that the proposed controller outperformed the unconstrained MPC, the well-tuned PID controller, and EMPC, especially in terms of rejecting the external wind disturbances. The proposed method real-time TCMPC) approach has the advantages of greater robustness and is not heavily dependent upon the accurate dynamics of the model.