UAV Modeling and Controller Design

Hybrid modeling and control issues provide a flexible powerful tool for Unmanned Aerial Vehicles (UAVs) as complex control platforms. This paper aims to briefly review recent results in this area. Firstly, existing works on hybrid modeling and control of a single UAV are reviewed. Then multiple UAVs control in a synchronously cooperative task planning, which gives much more maneuverability, has been surveyed in the hybrid framework. Formation control of UAVs is a typical cooperative strategy with considerable applications in multi-agent aerial robotics area and can leverage limited abilities of single UAVs into complicated tasks of group missions. There are a few works in this field which we briefly reviewed their results and explored the possible future directions of these research activities.

In this research, a feedback nonlinear controller for an unmanned aerial vehicle octorotor is developed. The proposed controller scheme is designed with a Nonlinear Proportional Velocity (NLPV) term, plus a Linear Integral (LI) term. The tracking controller NLPV plus LI is based on a two layer bounded controller with saturation approach. The Newton Euler formulation is applied to obtain a mathematical model of the octorotor Unmanned Aerial Vehicle (UAV). This modeling approach is applied to obtain the motion equations coupling moments and forces by extracting two sets of interconnected attitude and translational equations. In the design of a flight navigation controller with two layers, the inner layer consists of NLPV plus LI controller that is applied to the attitude dynamics, obtaining the references for the coupling outer layer NLPV plus LI controller, which manipulates the translational dynamics. The navigation NLPV plus LI controller is saturated for bounding in translational forces, to avoid large deviations of commands to Euler angles pitch and roll , another saturated controller is applied for the bounded thrust rotor to avoid the excessive speed of the propellers. The simulation of the flight navigation of an octorotor is performed to test the tracking control of a cylindrical trajectory, which is presented as a validation for the proposed control scheme.

: Unmanned aerial vehicles (UAVs) are critical components of the future naval forces. UAV control and monitoring with autonomous operation will become an absolute necessity and adaptive cooperation of vehicles is the only practical alternative. The objective of this project is to develop and evaluate new methodologies for cooperative (formation) control of multiple unmanned air vehicles. The goal is to have multiple UAVs working together as a group. Instead of separately assigning distinct tasks to each vehicle, the operator would assign tasks to the UAV group, which then determines the best way to accomplish each task, freeing the operator to maintain surveillance over the entire operation. In this project we investigated Path Tracking and obstacle avoidance of UAVs using fuzzy logic method. Algorithms for close formation control of multi-UAVs are developed and simulated. We also investigated fault-tolerant control of single UAVs by neuro-adaptive method. Detailed description of this method is provided in this document. The project has supported S graduate students with 9 technical papers published.

In this paper, a novel control strategy based on model predictive control is proposed for tracking the trajectory of unmanned aerial vehicles (UAV). First, the dynamic model of an unmanned aerial vehicle UAV is established by considering the linear velocities in terms of the analyzed system’s attitude angles. The UAV’s dynamic model is suitable for kinematic control by using two attitude angles as control inputs. The controller design is made up of an output feedback controller formed by the error variables, which are the difference between the measured output and the reference variables. Then, a model predictive control is established by considering an optimization problem with a performance index and the corresponding constraints. Furthermore, the stochastic properties of the UAV dynamic model are taken into account in this research study. The measured outputs provided by the sensors are constrained by the topological characteristics of the unmanned aerial vehicle to be analyzed. Finally, a numerical experiment and its conclusions are presented, demonstrating that the proposed control strategy provides optimal trajectory tracking.

This paper discusses the algorithms of vision systems for automatic UAV (Unmanned Aerial Vehicle) landing. The basic algorithms for finding helipad and adapting the UAV control system to an autonomous UAV landing are presented. The authors consider algorithms to solve navigation problems, building block diagrams of automatic UAV control. Also, the developed algorithms make it possible to implement an automatic landing system for UAVs using technical vision systems, using various camera parameters, algorithms allowing research, as part of the autonomous navigation of unmanned aerial vehicles. The article discusses the developed algorithm that allows you to highlight certain characteristic points for visual navigation. Also, a search system for characteristic points has been developed that allows automatic UAV landing. These algorithms may be useful for the autonomous landing UAV systems. Algorithms can also be used to track a trajectory of the UAV system.

The tilt tri-rotor unmanned aerial vehicle (UAV) has three flight modes: the hover mode, the transition mode, and the fixed-wing mode. Controller design in the hover mode is the premise of realizing stable flight of this kind of UAV. Due to the particular structure with odd rotors and strong nonlinearity, the modeling and control of the tilt tri-rotor UAV remain an active and ongoing research topic. To overcome these problems and achieve stable flight control, this paper proposes a sliding mode-based nonlinear control scheme for the hovering flight of a tilt tri-rotor UAV, consisting of position control, attitude control, and control allocation. First, the mathematical model of the UAV is given by using the Newton–Euler formulation. Second, a cascade flight controller consisting of the position controller and the attitude controller is developed based on sliding mode control (SMC). For the position controller, an auxiliary dynamic system composed of the hyperbolic tangent functions is introduced to the SMC approach for constraining the output magnitude of the thrust and the reference attitudes. Besides, a disturbance observer is applied to the attitude controller to alleviate the chattering and improve robustness. Furthermore, according to the structural characteristics of the tilt tri-rotor UAV, a control allocation algorithm is developed to map the virtual control quantities calculated by the cascade flight controller to the actual actuators. Simulations are conducted to verify the robustness against the external disturbances and parameter variations, and the performance comparisons with two other control schemes are also given. Finally, the experiment is also carried out to validate the performance of the proposed control scheme.

In comparison to the pilot aboard a manned aircraft, the flight operator of an unmanned aerial vehicle (UAV) has less possibilities to supervise its automatic flight guidance and control system and intervene if necessary. This is especially critical during automatic take-off and landing (ATOL), as these phases of flight demand low reaction time and tolerate little error. This paper presents an online self-monitoring algorithm for the ATOL system of a fixed-wing UAV. It assesses systems, controllers, and flight-performance during the maneuvers and enables automatic decision to alter or abort. The algorithm has been successfully implemented in the flight control system of the SAGITTA Demonstrator UAV, the functional software of which has been developed at the Institute of Flight System Dynamics of the Technical University of Munich. Simulation results and ground testing of the SAGITTA Demonstrator have proven the concept of the self-monitoring algorithm and give an outlook to its applicability in flight.

In the control of unmanned aerial vehicles (UAVs), proportional–integral–derivative (PID) controllers continue to play a central role due to their simplicity, rapid implementation, and low computational demand. However, the classical PID approach faces inherent limitations, including sensitivity to disturbances, complex tuning, and limited adaptability, which increasingly highlight the relevance of advanced control methods. Recent research has investigated adaptive, fuzzy, and fractional-order PID (FOPID) solutions, as well as methods based on artificial intelligence and predictive control techniques such as linear quadratic regulator (LQR), model predictive control (MPC), sliding mode control (SMC), and H∞ approaches, many of which provide improved robustness and performance. The purpose of this study is to review the application of PID control in UAV systems, to identify its limitations, and to present alternative control strategies that may become dominant in the future through the development of hybrid and intelligent systems.

The aim of this article was to discuss the modeling and control method of quadrotor unmanned aerial vehicle (UAV). In the process of modeling, mechanism modeling and experimental testing are combined, especially the motor and propeller are modeled in detail. Through the understanding of the body structure and flight principle of the quadrotor UAV, the Newton–Euler method is used to analyze the dynamics of the quadrotor UAV, and the mathematical model of the UAV is established under the small angle rotation. Process identifier (PID) is used to control it. First, the attitude angle of the model is controlled by PID, and based on this, the speed in each direction is controlled by PID. Then, the PID control of the four rotor aircraft with the center of gravity offset is simulated by MATLAB. The results show that the pitch angle and roll angle can be controlled by 5 degrees together without center of gravity deviation, and the PID can effectively control the control quantity and achieve the desired effect in a short time. Classical BP algorithm, classical GA-BP algorithm, and improved GA-BP algorithm were trained, respectively, with a total of 150 sets of training data, training function uses Levenberg-Marquardt (trainlm), and performance function uses mean squared error (MSE). In the background of the same noise, the improved GA-BP algorithm has the highest detection rate, classical GA-BP algorithm is the second, and classical BP algorithm is the worst. The simulation results show that the PID control law can effectively control the attitude angle and speed of the rotor UAV in the case of center of gravity deviation.

Instruction sender authentication of UAV (Unmanned Aerial Vehicle) control is critical for the secure operation of UAV systems. By exploring user touch action characteristics of the user interaction with the UAV control mobile terminal, this paper proposes a novel instruction sender continuous authentication framework for UAV control systems. In particular, we first obtain the touch screen data in real time from the interaction with the mobile device screen of a user when he controls the UAV. Then we create a polynomial to fit the touch trajectory on the screen of the user, and use the least square method to obtain the optimal estimate of the polynomial coefficients. Finally, we mark the grid area covered by the polynomial to extract the user’s touch screen operation action fingerprint(OAF), and employ the fuzzy extractor method to realize the identity authentication of the sender with certain error tolerance. Extensive experiments are conducted to illustrate the authentication performance of the proposed authentication framework in terms of false acceptance rate, false rejection rate and equal-error rate.

This study presents an advanced approach to modeling Unmanned Aerial Vehicle (UAV) dynamics by integrating Artificial Neural Networks (ANNs) and the Output Error Method (OEM). Moreover, the research analyzes the longitudinal flight characteristics of the Multiplex FunCub R/C, using data from designed flight tests complemented by simulations incorporating sensor noise and drift effects. Furthermore, the study captures a comprehensive range of aerodynamic responses essential for precise system identification by employing a multistep elevator input signal. In addition, the OEM approach, a traditional parameter estimation method, offers robust statistical estimation by minimizing the discrepancies between measured and predicted outputs. However, due to the nonlinear complexities inherent in UAV flight dynamics, the study also explores ANNs, leveraging their capability to model intricate nonlinear behaviors without requiring predefined aerodynamic parameters. Subsequently, the performance of both methodologies is critically evaluated against measured aerodynamic coefficients, revealing ANNs’ superior adaptability in accurately predicting complex aerodynamic interactions compared to OEM. Consequently, results indicate notable reductions in relative error, particularly in challenging aerodynamic coefficients. Overall, this research not only highlights the comparative advantages of ANNs in UAV system identification but also lays an initial structure for future advancements in UAV modeling and control systems.

An intelligent PID controller based on RBF neural network was designed for longitudinal attitude control of a small unmanned aerial vehicle (UAV) according to the feature of complexity and nonlinear; RBF neural network is used to real-time update the PID parameters on line by its learning function for achieving the longitudinal channel control of the UAV; Simulation results show that the design of the RBF neural network PID controller has better control performance and it better satisfies the requirements of the longitudinal channel flight control of small UAV.

Aiming at the time-varying and nonlinear problems of fixed-wing unmanned aerial vehicle (UAV) system, a UAV flight control algorithm was proposed based on PID control algorithm and the adaptive genetic algorithm (GA). On the basis of the traditional algorithm, the settling time was considered as the influencing factors of the objective function. The optimal index was determined by using the overshoot, rise time and settling time of the system response as variables, and adjusting the corresponding weight according to the actual situation to change the system response result. Moreover, the crossover probability and mutation probability in GA could be adaptively changed, so that the optimal parameters in the PID control process could be quickly and accurately found. Based on the rigid body dynamics model and kinematic model of the fixed-wing UAV, the simulation models of five control loops were designed in MATLAB/Simulink, namely the pitch angle, roll angle, yaw angle, speed and height control loops. Compared with the traditional PID controller, the system responses of the five control loops were significantly improved with faster response speed, and less than 5% overshoot. The proposed algorithm had strong adaptability and anti-interference ability.

This paper presents a methodology to implement a flight control system based on PID control design for PX4 autopilot system. The objective of the method is to find out the optimal controller gains on the same control structure of PX4 flight stack software without iterative controller tuning. This is achieved through a two-step procedure which consist of aircraft system identification and PID optimized control design. The first step to implement an autopilot system on an Unmanned Aerial Vehicle (UAV) relates to characterizing the UAV's dynamics using a mathematical model. To accomplish the accuracy of the particular UAV control, the process of system identification, which is the estimation of the parameters of the equation of motion, is essential. The measurement of inputs and outputs during manual flight is utilized to determine the unknown parameters of SISO mathematical model using a software of comprehensive identification from frequency responses. Subsequently, the model is utilized for an optimization-based tuning of these PID controller gains offline in order to minimize the requirement of numerous in-flight tuning. The controller is implemented on PX4 autopilot system. The results are demonstrated that the tracking control system has excellent dynamic performance in respect of simple design, high precision, and easy implement.

Unmanned vehicles (UAVs) are technological tools whose application is carried out in the civil and military areas for surveillance, recognition and intelligence tasks. Given its versatility and features, in this paper the implementation of an autopilot is determined, after an analysis of commercial models. Said device must comply with technical requirements for the automatic flight control of a medium-sized UAV, considering the operation scenarios in the northern border of Ecuador. In addition, the parameters that intervene in the control loops and the relevant simulations are specified. The results obtained from the experimental flights of the aircraft are reflected in graphs, where the stability of the plant properly calibrated for autonomous flight is verified.