Control Of Quadcopter Research Articles

Simulation-based evaluation of model-free reinforcement learning algorithms for quadcopter attitude control and trajectory tracking

General use quadcopters have been under development for over a decade but many of their potential applications are still under evaluation and have not yet been adopted in many of the areas that could benefit from their use. While the current generation of quadcopters use a mature set of control algorithms, the next steps, especially as autonomous features are developed, should involve a more complex learning capability to be able to adapt to unknown circumstances in a safe and reliable way. This paper provides baseline quadcopter control models learnt using eight general reinforcement learning (RL) algorithms in a simulated environment, with the object of establishing a reference performance, both in terms of precision and generation cost, for a simple set of trajectories. Each algorithm uses a tailored set of hyperparameters while, additionally, the influence of random seeds is also studied. While not all algorithms converge in the allocated computing budget, the more complex ones are able to provide stable and precise control models. This paper recommends the use of the TD3 algorithm as a reference for comparison with new RL algorithms. Additional guidance for future work is provided based on the weaknesses identified in the learning process, especially regarding the strong dependence of agent performance on random seeds.

Trajectory Planning and Control of Multiple Quadcopters for Mars Exploration

Trajectory Planning and Control of Multiple Quadcopters for Mars Exploration

Enhancing Quadcopter Autonomy: Implementing Advanced Control Strategies and Intelligent Trajectory Planning

In this work, an in-depth investigation into enhancing quadcopter autonomy and control capabilities is presented. The focus lies on the development and implementation of three conventional control strategies to regulate the behavior of quadcopter UAVs: a proportional–integral–derivative (PID) controller, a sliding mode controller, and a fractional-order PID (FOPID) controller. Utilizing careful adjustments and fine-tuning, each control strategy is customized to attain the desired dynamic response and stability during quadcopter flight. Additionally, an approach called Dyna-Q learning for obstacle avoidance is introduced and seamlessly integrated into the control system. Leveraging MATLAB as a powerful tool, the quadcopter is empowered to autonomously navigate complex environments, adeptly avoiding obstacles through real-time learning and decision-making processes. Extensive simulation experiments and evaluations, conducted in MATLAB 2018a, precisely compare the performance of the different control strategies, including the Dyna-Q learning-based obstacle avoidance technique. This comprehensive analysis allows us to understand the strengths and limitations of each approach, guiding the selection of the most effective control strategy for specific application scenarios. Overall, this research presents valuable insights and solutions for optimizing flight stability and enabling secure and efficient operations in diverse real-world scenarios.

Research on the Application of Neural Network PID in Quadcopter Aircraft

As a highly maneuverable and flexible unmanned aerial vehicle, quadcopters have broad application prospects in both civilian and military fields. However, due to their complex dynamic characteristics, nonlinearity, and strong coupling, achieving stable and precise control of quadcopters is a challenging task. Traditional control methods often fail to meet the control performance requirements for such complex nonlinear systems. Neural networks provide a new approach to solving control problems in complex systems due to their powerful learning and adaptive capabilities. Neural networks can improve the performance of control systems by learning from large amounts of data, capturing the dynamic characteristics of the system, and adjusting control strategies online. Combining neural networks with PID control is expected to fully leverage the advantages of both. Neural networks can adjust the parameters of PID controllers in real-time, enabling them to better adapt to the complex dynamic changes and external disturbances of quadcopter aircraft. This fusion control method brings new possibilities for improving the control performance of quadcopter aircraft. This study explores the application of neural network PID in quadcopter aircraft to achieve stable and precise control, laying the foundation for its widespread promotion in practical applications.

Cascade PID Control for Altitude and Angular Position Stabilization of 6-DOF UAV Quadcopter

UAVs are commonly used in transportation, especially in the express delivery of light cargo parcels. However, controlling UAVs is difficult because of their complex structure and wide range of operations in space. The research contribution is proposed a cascade control structure using six PID controllers for the 6-DOF UAV quadcopter, that ensures the altitude angulars positions control at the desired values and maintains flight balance stability for the 6-DOF UAV quadcopter. First, the mathematical dynamic models for the 6-DOF UAV quadcopter have been researched and developed, including the translational dynamic mathematical model and the rotational dynamic mathematical model of the 6-DOF UAV quadcopter. This is a complex object with strong nonlinearity and difficult control. And then, the article introduces the method of designing six PID controllers for 6-DOF UAV quadcopter to meet the requirements, based on applying the Ziegler-Nichols experimental method. Applying the Ziegler-Nichols experimental method makes the process of designing a UAV quadcopter control system simple, straightforward and heuristics with fast controller parameters tuning. Next, the article presents the results of modeling and simulation of the 6-DOF UAV quadcopter control system on Matlab/Simulink. The simulation results show that the six proposed PID controllers have ensured the flight balance stability at the desired altitude and angular positions with overshoot less than 20%, steady-state error less than 1%. This shows the prospect of applying the proposed PID control method to physical UAVs, easily adjusting PID parameters to suit the flight environment.

Innovative Fuzzy PID Algorithm: Advancing Quadcopter Control with Adaptive Precision

The integration of fuzzy logic techniques into PID controllers shows promising advantages in quadcopter control. By incorporating fuzzy logic, the PID controller achieves enhanced adaptability and robustness, resulting in more dynamic and resilient control performance. This is achieved through the automatic adjustment of PID parameters based on real-time system feedback, enabling a control strategy that closely resembles human reasoning. Overall, the combination of fuzzy logic and PID control offers significant potential for improving quadcopter control in various environmental conditions.

Safety-critical control of planar quadcopters under dynamic event-triggered mechanism

This paper aims to solve the problem of safety-critical control, i.e., safe flight of planar quadcopters in complex environments. By introducing input-to-state safe barrier function (ISSf-BF), the attitude, position, speed, and other key parameters of planar quadcopters are fully considered to realize safety-critical control. Also, a dynamic event-triggered mechanism (DETM) is constructed, and Zeno phenomenon is ruled out based on the constructed DETM. By monitoring the relative distance and speed changes between planar quadcopters and the boundary of the safe zone, the control action is triggered only when the control strategy needs to be adjusted, which effectively reduces the unnecessary waste of computation and communication resources. Finally, a series of simulation experiments are demonstrated to verify the feasibility of the proposed method.

An optimal hybrid quadcopter control technique with MPC-based backstepping

Quadcopter unmanned aerial vehicle is a multivariable, coupled, unstable, and underactuated system with inherent nonlinearity. It is gaining popularity in various applications and has been the subject of numerous research studies. However, modelling and controlling a quadcopter to follow a trajectory is a challenging issue for which there is no unique solution. This study proposes an optimal hybrid quadcopter control with MPC-based backstepping control for following a reference trajectory. The outer-loop controller (backstepping controller) regulates the quadcopter’s position, whereas the inner-loop controller (Model Predictive Control) regulates its attitude. The translational and rotational dynamics of the quadcopter are analyzed utilizing the Newton-Euler method. After that, the backstepping controller (BC) is created, which is a recurrent control method according to Lyapunov’s theory that utilizes a genetic algorithm (GA) to choose the controller parameters automatically. In order to apply a linear control technique in the presence of nonlinearities in the quadcopter dynamics, Linear Parameter Varying (LPV) Model Predictive Control (MPC) structure is developed. Simulation validated the dynamic performance of the proposed optimal hybrid MPC-based backstepping controller of the quadcopter in following a given reference trajectory. The simulations demonstrate the fact that using a command control input in trajectory tracking, the proposed control algorithm offers suitable tracking over the assigned position references with maximum appropriate tracking errors of 0.1 m for the �� and �� positions and 0.15 m for the �� position.

VIDEO REPEATER DESIGN CONCEPT FOR UAV CONTROL

This study is aimed at the utilization of a video repeater for unmanned aerial vehicle (UAV) control, involving a comparative analysis of existing scientific literature, methodologies, and available solutions. Through comparative analysis, the method of using an external repeater emerged as the most promising, offering flexibility in selecting repeaters with superior performance and advanced capabilities. Notably, this approach allows for the utilization of a single repeater across multiple UAVs and facilitates convenient modification or upgrade of the repeater without necessitating alterations to the UAV itself. Following component selection, an experimental prototype was designed to facilitate empirical investigations. Additionally, a frequency transmission scheme was devised for quadcopter control employing the repeater. This research contributes to advancing UAV control systems by introducing a novel approach to video repeater integration, poised to enhance operational efficiency and adaptability in diverse settings. The findings from this study are anticipated to inform further developments in UAV technology, particularly in optimizing video transmission for improved situational awareness and mission effectiveness.

DESIGN AND DEVELOPMENT OF FLIGHT CONTROLLER FOR QUADCOPTER DRONE CONTROL

UAV (Unmanned Aerial Vehicle), also commonly called drone, is a flying robot technology that can be controlled remotely and can also fly autonomously based on the mission given by the operator. Drones are usually used for various purposes such as package delivery, watering plants, land mapping, natural disaster monitoring, photography, videography and others. Drones have many types, one of which is a drone with four motors as the main drive, commonly called a quadcopter drone. Quadcopter drones have evolved a lot based on current needs. Although quadcopter drones have many uses, the development of quadcopter drone research in Indonesia is quite slow, one of the quadcopter drone components whose development is quite slow is the flight controller. Flight controller (FC) is a main controller brain in drones that has complex functions in quadcopter drone control. The function of the FC is to regulate motor speed, stabilize and maintain altitude. In this research, FC is designed to control the stability of quadcopter drones while flying. This FC was developed by applying LoRa technology as an internal receiver. LoRa technology is used to receive control data from the remote control (RC) and simultaneously send sensor data. The purpose of this research is to design FC to improve local products in the field of technology and participate in the development of flying robot technology, especially on quadcopter drones and to determine the performance of LoRa technology after being integrated as an internal transceiver in FC for remote control of quadcopter drones.

Sliding Surface-Based Integral Reinforcement Learning for Optimal Tracking Control of Quadcopters Considering Uncertainties

Sliding Surface-Based Integral Reinforcement Learning for Optimal Tracking Control of Quadcopters Considering Uncertainties

Innovative Fuzzy PID Algorithm: Advancing Quadcopter Control with Adaptive Precision

Innovative Fuzzy PID Algorithm: Advancing Quadcopter Control with Adaptive Precision

Matlab sımulatıon of quadcopter dynamıcs and PID attıtude controller

Due to the ever-growing popularity of quadcopters, they are used in various fields such as surveillance, military, surveillance and courier services and are of great importance. Ideal handling of quadcopters is essential for safe maneuvering and high precision flight performance. This research provides simulation of quadcopter control in MATLAB software, development and review of various control principles.
 Dynamic development in unmanned aerial vehicles (UAVs) has led to the rapid spread of quadcopters in many fields, from airspace control to courier services. This research study investigates the Proportional-Integral-Derivative (PID) attitude controller by simulating the quadcopter dynamic object in MATLAB software. The heart of this research is to create and develop a PID attitude controller, which is a critical component for accurate control of quadcopter.

Wearable brain-computer interface technology and its application

Wearable Brain-Computer Interfaces (BCIs) signify a critical evolution in human-machine communication, driven by the convergence of neuroscience, engineering, and information technology. With applications that span across industrial, medical, and recreational domains, BCIs hold potential to redefine our interaction with the technological landscape. This manuscript elucidates this transformative juncture, bifurcating into passive and active BCIs. In passive BCIs, innovations leveraging Virtual Reality (VR) and Augmented Reality (AR) are delineated, progress has been demonstrated in the classification of passively induced emotional signals, highlighting the emergence of hands-free control systems like quadcopter control and industrial inspections. The discourse on active BCIs unveils advancements such as real-time handwriting and speech decoding, driver drowsiness detection, and emotion recognition, aided by machine learning techniques. Despite groundbreaking progress, challenges in algorithm optimization, adaptability, multimodal signal complexity, and ethics persist. Future directions emphasize the potential of deep learning and multimode input signals collaboration. The manuscript underscores the societal implications, particularly in rehabilitation, communication, and entertainment. The review, therefore, serves as both an appraisal and a roadmap for the burgeoning field of wearable BCIs, underlining its role as a pathway to enhance human capabilities and quality of life.

Control of Quadcopter Based on Adaptive Fast Nonsingular Terminal Sliding Mode

This paper presents a novel approach to address the trajectory tracking control problem for quadcopter systems subjected to multiple disturbances. The proposed method integrates sliding mode control with adaptive control techniques, resulting in an effective adaptive fast nonsingular terminal sliding mode (AFNTSM) controller. The controller structure can be divided into an internal attitude control loop and an external position control loop. In the altitude control loop, a strategy employing AFNTSM is utilized to tackle time-varying external disturbances. In the attitude control loop, given the existence of uncertain system parameters, an attitude controller is designed by combining a PID controller with an extended state observer. The asymptotic stability of the system is verified by using Lyapunov’s theorem. Finally, experimental flights validate the superiority of this control strategy over the traditional PID algorithm.

End-to-end neural network based optimal quadcopter control

Developing optimal controllers for aggressive high-speed quadcopter flight poses significant challenges in robotics. Recent trends in the field involve utilizing neural network controllers trained through supervised or reinforcement learning. However, the sim-to-real transfer introduces a reality gap, requiring the use of robust inner loop controllers during real flights, which limits the network’s control authority and flight performance. In this paper, we investigate for the first time, an end-to-end neural network controller, addressing the reality gap issue without being restricted by an inner-loop controller. The networks, referred to as G&CNets, are trained to learn an energy-optimal policy mapping the quadcopter’s state to rpm commands using an optimal trajectory dataset. In hover-to-hover flights, we identified the unmodeled moments as a significant contributor to the reality gap. To mitigate this, we propose an adaptive control strategy that works by learning from optimal trajectories of a system affected by constant external pitch, roll and yaw moments. In real test flights, this model mismatch is estimated onboard and fed to the network to obtain the optimal rpm command. We demonstrate the effectiveness of our method by performing energy-optimal hover-to-hover flights with and without moment feedback. Finally, we compare the adaptive controller to a state-of-the-art differential-flatness-based controller in a consecutive waypoint flight and demonstrate the advantages of our method in terms of energy optimality and robustness.

Optimal fault resilient autonomous quadcopter control based on dynamic partial reconfigurable FPGA

Optimal fault resilient autonomous quadcopter control based on dynamic partial reconfigurable FPGA

Farklı performans ölçümleri kullanılarak kararlı durum görsel uyarılmış potansiyel kontrollü quadcopter yolunun değerlendirilmesi

This study focuses on controlling a quadcopter system using a steady-state visual evoked potential (SSVEP)-based brain-computer interface system. In the literature, researchers report the accuracy and information transfer rate. However, these measures do not provide sufficient information about the predicted and target path similarity. The drone is expected to follow a certain square-shaped path and return to its starting position. We calculated the final and mean distances as additional outcome measures using several classifiers. The results emphasize the importance of having a balanced confusion matrix in the performance of quadcopter control and provide a more complete picture in the evaluation of the quadcopter’s performance. Focusing on the relationship between classification accuracy and spatial deviation might create a new perspective for BCI-based control systems.

Self-Tuning PID Controller for Quadcopter using Fuzzy Logic

Tracking has become a necessary feature of a drone. This is due to the demand for drones, especially quadcopters, to be used for activities such as surveillance, monitoring, and filming. It is crucial to ensure the quadcopters perform the tracking with stable flight. Despite the advantages of having VTOL ability and great maneuverability, quadcopters require an effective controller to overcome their under-actuation and instability behavior. Even though a PID controller is commonly used and promising with its simple mechanism, it requires very proper tuning to ensure the stability of the system is not affected. In this paper, a simple Fuzzy algorithm is proposed to be incorporated into a PID controller to form a self-tuning Fuzzy PID controller. The Fuzzy logic controller works as the self-adjuster to the PID parameters. A mathematical model of the DJI Tello quadcopter is derived with position and attitude control loops that are designed to track a variety of trajectories with stable flight. The proposed method uses a simple architecture where the ranges of PID parameters are used as scaling factors for Fuzzy controller outputs. The results of the simulations show the tracking error performance metrics, which are IAE, ISE, and RMSE, are smaller compared to the values of the PID controller. Beyond its impact on quadcopter control, the proposed self-tuning approach holds promise for broader applications in nonlinear systems.

Altitude control of quadcopter with symbolic limited optimal discrete control

Altitude control of quadcopter with symbolic limited optimal discrete control