DYNAMICS AND CONTROL OF MINI AERIAL VEHICLE USING MODEL PREDICTIVE CONTROL - Okasha, Mohamed Elsayed Aly Abd Elaziz; maidul, islam; Idres, Moumen (IIUM, 2018)
Nowadays Mini Aerial Vehicles (MAVs) are popular in many areas such as aerial photography, inspection, surveillance and search and rescue missions in complex and dangerous environments due to their low cost, small size, superior mobility, and hover capability. Multifarious applications of MAVs inspire researchers to concentrate on different types of controllers like linear, nonlinear or learning-based. The attention of this work is to design a robust controller and to develop an accurate mathematical model of Quadrotor, a type of MAV as it behaves roughly in uncertain environments. Quadrotor is an under-actuated and highly nonlinear system with six degrees of freedom (DOF). The mathematical model of quadrotor is derived based on Newton-Euler method that includes aerodynamic drag and moment that are sometimes overlooked in literatures. For higher precision modelling, model uncertainties are also included in the system. In addition, the kinematic model is derived utilizing Euler angles and Quaternion methods. Quaternion approach has the advantage of singularity free orientation while Euler angles are easy to visualize. This work investigates the performance of three different controllers which includes Proportional-Integral-Derivative (PID), Linear Quadratic Regulator (LQR) and Model Predictive Control (MPC) based on several performance evaluation factors. PID offers fast response to the system comparing to other controllers although choosing proper gain is challenging for PID. However, it cannot handle directly under-actuated system and due to the fact, some states are required to be decoupled. LQR ensures fast response and can deal with Multiple Input Multiple Output (MIMO) system at the same time. The main drawback of the LQR controller is its incapability of dealing with steady-state error. Conversely, MPC has the functionalities of dealing with MIMO system with constraints and uncertainties while other controllers fail. The performance of the controllers are presented based on tracking accuracy using Root Mean Square Error (RMSE) method and control stability using control input norm method. MATLAB and Simulink environment is considered to carry out the simulations. Based on simulated experiments, it is found that MPC could track the trajectories more accurately with stable control effort comparing to PID controllers and LQR.