Towards Optimization of Energy Consumption of Tello Quad-Rotor with Mpc Model Implementation

Abstract

For the last decade, there has been great interest in studying dynamic control for unmanned aerial vehicles, but drones—although a useful technology in different areas—are prone to several issues, such as instability, the high energy consumption of batteries, and the inaccuracy of tracking targets. Different approaches have been proposed for dealing with nonlinearity issues, which represent the most important features of this system. This paper focuses on the most common control strategy, known as model predictive control (MPC), with its two branches, linear (LMPC) and nonlinear (NLMPC). The aim is to develop a model based on sensors embedded in a Tello quad-rotor used for indoor purposes. The original controller of the Tello quad-rotor is supposed to be the slave, and the designed model predictive controller was created in MATLAB. The design was imported to another embedded system, considered the master. The objective of this model is to track the reference trajectory while maintaining the stability of the system and ensuring low energy consumption. The case study in this paper compares linear and nonlinear model predictive control (MPC). The results show the efficiency of NLMPC, which provides more promising results compared to LMPC. The comparison concentrates on the energy consumption, the tracked trajectory, and the execution time. The main finding of this research is that NLMPC is a good solution to smoothly track the reference trajectory. The controller in this case processes faster, but the rotors consume more energy because of the increased values of control inputs calculated by the nonlinear controller.

Self-Tuning PID Controller for Quadcopter using Fuzzy Logic

“…The difference in lift force is used to generate motion by adjusting the rotor's velocity in pairs. The manipulation of rotors to create forward, backward, right-ward, left-ward, vertical, and hovering motions of the quadcopter is shown in Table 1, [3] The modeling techniques of quadcopter are based on the physics of the system with the system further broken into smaller subsystems for easier analysis, design, and modelling [11]. In order to simplify the mathematical equations in modeling, several different assumptions are made while still constructing a fairly realistic model [11].…”

“…UAVS are widely addressed as drones, which is an acronym for Dynamic Remotely Operated Navigation Equipment [2]. Drones are classified based on a few parameters, such as size, mass, endurance, flight altitude, function, payload, configuration, and flying principle [2] [3]. In the configuration class, drones are divided according to the type of wings and rotors.…”

An optimal hybrid quadcopter control technique with MPC-based backstepping

“…The simulation's findings show that the helicopter can avoid the obstruction by following the best possible path from its starting position to its destination. In [23], a Nonlinear Model Predictive Controller (NLMPC) was designed to track a reference trajectory while preserving system stability and ensuring minimal energy usage. Linear and Nonlinear MPC approaches were compared to demonstrate the effectiveness of the NLMPC, and it was demonstrated that the NLMPC offers better results than Linear MPC (LMPC).…”

Design and Real-Time Implementation of a Cascaded Model Predictive Control Architecture for Unmanned Aerial Vehicles

“…However, the application of large-scale MPC in real-time has its own challenges, mainly due to its reliability, large computational overhead, and the complexity of its software implementation [10]. A literature survey presented recent realtime applications in which the MPC strategy was used to address these concerns [10][11][12][13][14][15][16][17][18]. For these reasons, the control of quadrotors using MPC has become feasible in more sophisticated applications as research of their control algorithms advances.…”

“…Although tremendous effort has been dedicated to implementing MPC in unmanned systems [27], there still exist significant challenges in high-speed real-time applications. An MPC is designed based on the entire nonlinear dynamics model of a quadrotor UAV, which introduces several computational challenges [18,28], and its real-time applicability is reduced. To avoid this, linear control strategies have been implemented while considering the advantages of being relatively easy to understand and design [29].…”