Modelling and PSO Fine-tuned PID Control of Quadrotor UAV

This paper describes nonlinear dynamics model of x-configuration quadrotor using Newton-Euler modelling technique. To stabilize quadrotor attitude (roll (ϕ), pitch (θ), yaw (ψ)) during hovering, a PID controller is proposed. There is individual PID controller for each roll, pitch, yaw and z where 12 parameters consist of kp, ki, and kd are fine-tuned using particle swarm optimization algorithms. From the simulation, the sum absolute error fitness function give the best optimize result where quadrotor achieve zero steady state error for hovering with 18.9% overshoot, and 4.42s settling time. Accordingly, for attitude stabilization, roll angle, pitch angle, and yaw angle converge to the set point, zero approximately with settling time 2.76s, 0.1s and 3.2s respectively

Particle swarm optimization based proportional-derivative parameters for unmanned tilt-rotor flight control and trajectory tracking

This paper presents the dynamic modelling and control technique for a tilt-rotor aerial vehicle operating in bi-rotor mode. This kind of aircraft combines two flight envelopes, making it ideal for scenarios that require hovering, vertical take-off/landing and fixed-wing capabilities. In this work, a detailed mathematical model is derived using Newton–Euler formalism. Based on the obtained model, a new control scheme that incorporates six Proportional-Derivative (PD) controllers is proposed for the attitudes (roll (φ), pitch (θ), yaw (ψ)) and the positions (x, y, z) of the aircraft. Then, intelligent Particle Swarm Optimization (PSO) and conventional Reference Model (RM) techniques are applied for optimal tuning of the controllers\u27 parameters. The stability analysis is developed using the Lyapunov approach and its application to the tilt-rotor system in the case of intelligent and conventional PD controllers. Numerical results of two scenarios prove the efficiency of the controllers tuned using the PSO method. Indeed, its ability to track the desired trajectories is demonstrated through 3D path tracking simulations, even in the presence of wind disturbances. Finally, experimental tests of stabilization and trajectory tracking are carried out on our prototype. These testing showed that our tilt-rotor was stable and suitably follows the imposed trajectories

Quadrotor Simulator for Control De-velopment – Application to Autono-mous Landing

In this thesis is studied the landing problem of a VTOL UAV and a 3D sim-ulation environment is built to safely develop control for a quadrotor, resorting to 3D modelling and simulation software. In a time where the development of unmanned vehicles is a trend and it is technologically in growth, the emergent difficulties are challenging when it comes to aviation. In this field, it is useful a tool for researchers to have at their disposal to conduct experiments without putting their real systems to real threat. Also, the landing of UAV’s is currently one of the most serious cases of study with a lot of investigation going on to solve the problems associated with it. In this sense, some problematics are contemplated. Based on a quadrotor in a X8 configuration – 4 frames and 8 propellers –, are applied linear and nonlinear control design techniques with the intent to sta-bilize and control the quadrotor and a 3D simulator is developed

Robust nonlinear trajectory controllers for a single-rotor UAV with particle swarm optimization tuning

This paper presents the utilization of robust nonlinear control schemes for a single-rotor unmanned aerial vehicle (SR-UAV) mathematical model. The nonlinear dynamics of the vehicle are modeled according to the translational and rotational motions. The general structure is based on a translation controller connected in cascade with a P-PI attitude controller. Three different control approaches (classical PID, Super Twisting, and Adaptive Sliding Mode) are compared for the translation control. The parameters of such controllers are hard to tune by using a trial-and-error procedure, so we use an automated tuning procedure based on the Particle Swarm Optimization (PSO) method. The controllers were simulated in scenarios with wind gust disturbances, and a performance comparison was made between the different controllers with and without optimized gains. The results show a significant improvement in the performance of the PSO-tuned controllers.Peer ReviewedPostprint (published version

Modelling and PSO fine-tuned PID control of quadrotor UAV

This paper describes nonlinear dynamics model of x-configuration quadrotor using Newton-Euler modelling technique. To stabilize quadrotor attitude (roll (φ), pitch (θ), yaw (ψ)) during hovering, a PID controller is proposed. There is individual PID controller for each roll, pitch, yaw and z where 12 parameters consist of kp, ki, and kd are fine-tuned using particle swarm optimization algorithms. From the simulation, the sum absolute error fitness function give the best optimize result where quadrotor achieve zero steady state error for hovering with 18.9% overshoot, and 4.42s settling time. Accordingly, for attitude stabilization, roll angle, pitch angle, and yaw angle converge to the set point, zero approximately with settling time 2.76s, 0.1s and 3.2s respectively

Comparison of Metaheuristic Optimization Algorithms for Quadrotor PID Controllers

In the present study, different solution methods are discussed in order to control the quadrotor with the most optimal PID parameters for the determined purposes. One of these methods is to make use of meta-heuristic algorithms in control systems. There are some limitations of using a PID controller as a classical construct. However, it is thought that more successful results will be obtained by optimizing its parameters through meta-heuristic algorithms. Initially, the mathematical model of the vehicle was created in MATLAB/Simulink. Then, genetic algorithms (GA), artificial bee colony (ABC), particle swarm optimization (PSO) and firefly algorithms (FA) were determined respectively as optimization methods. And these optimization methods used to determine the PID control parameters are applied to the developed mathematical model in the MATLAB/Simulink environment. In addition, the performances of the optimization methods are evaluated according to the comparison criteria. As a result of the comparison carried out according to ITAE (Integral Time Absolute Error) fitness criteria, ABC (1.2% - 4.4%) in terms of altitude, FA (4% - 13%) in terms of roll angle, GA (13% - %21) in terms of pitch angle, and PSO (4% - %15) in terms of yaw angle has been more successful than other methods

A Metaheuristic Optimization Using Explosion Method On A Hybrid Pd2-Lqr Quadcopter Controller

The popularity of the rotorcraft type UAV, the quadrotor, has grown rapidly in recent years due to its advantages and capability to perform various applications such as environment monitoring, surveillance, and inspection. However, the quadrotor’s dynamics are highly nonlinear and underactuated since it has 6 DOF that need to be controlled by only 4 actuators. Besides, it is also crucial that the controller’s gains are tuned appropriately since it can affect the quadrotor’s performance. This study aims to develop an effective optimal control technique to control and stabilize the quadrotor's altitude and attitude motion. A simulation-based experiment in MATLAB/Simulink environment was conducted to test and verify the proposed algorithm and controller performance. The mathematical model of the quadrotor was derived based on the Newton-Euler approach and linearized using a small angle approximation. In this study, a Hybrid PD2-LQR controller was proposed for quadrotor control and stabilization. Conventionally, the controller’s gains were tuned using the trial-anderror method. The problem with this method was that it very time-consuming, and the control designer could never tell which gains are the optimal solution for the controller. Therefore, an optimization algorithm based on the explosion method called REA was proposed and implemented on the proposed Hybrid PD2-LQR control structure. A comparative study with 8 well-known algorithms, PSO, ABC, GA, DE, MVO, MFO, FA, and STOA, was performed to evaluate the performance of the proposed algorithm. Similarly, the proposed controller was evaluated by a comparative study with 6 conventional controllers, PD, PID, LQR, Hybrid P-LQR, Hybrid PD-LQR, and Hybrid PD2-LQR. The findings show that the REA could perform well in exploiting the global optimum and exploring the search space. The convergence speed of the REA was also faster than other algorithms. Similarly, for the controller, the findings show that the REA-based Hybrid PD2-LQR controller has a faster rise time with a shorter settling time than the conventional controllers, while there was no overshoot and steady-state error produced. On average, the rise time, settling time, overshoot, steady-state error and RMSE was improved by 95%, 95.3%, 100%, 100%, and 43.5% respectively for roll and pitch motion, while 96.5%, 96.5%, 100%, 97.2%, and 47.3% respectively for yaw motion. For altitude motion, the rise time, settling time, overshoot, and steadystate error were improved by 84.5%, 85.5%, 100%, and 100%, respectively. The RMSE for altitude motion was not improved but still could be accepted since the difference with the conventional controllers was not too much. Therefore, based on these findings, it could be concluded that the proposed REA-based Hybrid PD2-LQR controller was the best among the tested controller and suited for controlling and stabilizing the quadrotor’s altitude and attitude motion since it could significantly improve the performance of the quadrotor’s response

An Overview of Drone Energy Consumption Factors and Models

At present, there is a growing demand for drones with diverse capabilities that can be used in both civilian and military applications, and this topic is receiving increasing attention. When it comes to drone operations, the amount of energy they consume is a determining factor in their ability to achieve their full potential. According to this, it appears that it is necessary to identify the factors affecting the energy consumption of the unmanned air vehicle (UAV) during the mission process, as well as examine the general factors that influence the consumption of energy. This chapter aims to provide an overview of the current state of research in the area of UAV energy consumption and provide general categorizations of factors affecting UAV's energy consumption as well as an investigation of different energy models