Nonlinear flight navigation controller for an octorotor unmanned aerial vehicle

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.

As a complicated multi-input, multi-output, and time-varying nonlinear system, flight control system of unmanned aerial vehicle (UAV), which determines the whole system’s performance directly, is crucial for the simulation training system design. This chapter mainly focuses on parameter identification of flight control system based on the modeling of UAVs and a specific controller design for the pendulum-like oscillation in micro aerial vehicle (MAV). A predator–prey particle swarm optimization (PSO) algorithm for identifying parameters of UAV flight control system is presented, with the aim of reducing the workload of the designers during the process of designing complicated UAV control system. Besides, a software environment for UAV controller design was developed based on the UAV model and the proposed method. Then a specific kind of controller design involving the pendulum-like oscillation in the hover and stare state for a MAV in the presence of external disturbances is discussed in detail, since the pendulum-like oscillation caused by uncertainty and external disturbances badly jeopardize the performance of hover and stare, resulting in blurred images or even MAV’s overturning. A novel type of PSO-based linear-quadratic regulator (LQR) controller for stabilizing the pendulum-like oscillation is developed, which can enhance the MAV’s performance efficiently.

: 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.

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.

This study investigates the longitudinal motion control of unmanned aerial vehicles through a simulation in MATLAB. The linear model of an unmanned aerial vehicle is applied to controllers to explicate the longitudinal motion of the unmanned aerial vehicle. The ideal performance for an unmanned aerial vehicle is to achieve the desired response instantly with 100% precision. However, currently available controllers need further improvements. Thus, a proportional-derivative linear quadratic regulator controller is developed and compared with a proportional-integral-derivative controller, a linear quadratic regulator controller, and a proportional linear quadratic regulator controller. The proportional-derivative linear quadratic regulator controller enhances the response of the system by reducing settling time by more than 95% compared with other available controllers. Additionally, the proportional-derivative linear quadratic regulator improves the root mean square error by almost 50% compared with the proportional linear quadratic regulator controller and improves rise time by almost 96% with reasonable overshoot.

This paper describes an attempt to develop an automatic unmanned aerial vehicle (UAV) control system for emergency rescue and investigation. A cascade classifier system using Haar-like features for object recognition from a free viewpoint is also presented. Conventional emergency rescue systems have a shortcoming in terms of their performance. Thus, we introduce an automatic UAV control system in order to overcome the shortcoming of conventional emergency rescue systems in an emergency situation. The proposed system involves two processing stages. One involves a capturing and recognition of target objects, and the other involves controling the UAV using digital intelligence. Object detection in many types of images as well as real-time simulation methods continue to be one of the most common techniques for information processing and applications. The fundamental concepts of the proposed system and some experimental results obtained by using related processing techniques are also described.

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.

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.

Accurate dynamic modeling is difficult for aerobatic unmanned aerial vehicles flying at their physical limit, due to the model uncertainty caused by unobservable hidden states like airflow and vibrations. Although some progresses have been made, these hidden states are still not properly characterized, rendering system identification problem for aerobatic unmanned aerial vehicle extremely challenging. To address this issue, a novel spectrally normalized adaptive neural identifier is proposed for the dynamic modeling of aerobatic unmanned aerial vehicles. Specifically, to characterize the model uncertainty, we propose a spectrally normalized adaptive neural network (SNANet) to extract deep features representing the hidden states of the system. Particularly, the proposed SNANet adopts a multi-model adaptive structure, quickly and dynamically updating the model online. Furthermore, the spectral normalization constraint is introduced into the training process to ensure the Lipschitz stability of the SNANet. Consequently, a trajectory tracking control scheme including the sliding mode controller and SNANet is presented. The modeling effectiveness of the proposed method is verified on a real flight dataset. The results demonstrate that our method has high modeling accuracy, short training time, and fast model response speed.KeywordsSpectral normalizationModel uncertaintyAdaptive neural identifierAerobatic unmanned aerial vehicle

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.

Visual tracking is an important component in the navigation and control of unmanned aerial vehicles (UAVs). This paper is concerned with the visual tracking problem for the navigation and control of UAVs and focuses on real-time image processing methods. We review previous research based on geometric active contours which are automatic processes to track object boundaries in an image. Test results and some limitations of the previous tracking system are discussed. Then, a fast and robust implementation of the Chan-Vese active contour model is proposed to overcome these limitations.

The four - rotor unmanned aerial vehicle (UAV) is the object of study, in this paper. In order to solve the problem of poor robustness and low control precision of the four-rotor unmanned aerial vehicle (UAV) control system, and realized the stability control problem of the four-rotor UAV attitude. First, the dynamic model of the four-rotor unmanned aerial vehicle is established. And on this basis, a fuzzy controller is designed, and used to control the channel. Then, the simulation platform is built by Matlab / Simulink simulation software, and the performance of the designed fuzzy controller is analyzed comprehensively. It is also determined whether the algorithm can control the attitude of the four rotor unmanned aerial vehicle. The simulation results fully verify the accuracy of the model, and proved fuzzy controller has better dynamic performance and robustness under appropriate parameters so that UAVs can fly stable. The algorithm can improve the anti-jamming performance and control accuracy of the system, it has a certain significance for the actual four-rotor aircraft attitude control.

The Coanda effect is a natural phenomenon, which is that flowing fluid on a certain surface is pulled to the surface due to the viscosity of the fluid. This phenomenon can be utilized to produce the lift force of a flying object. This paper presents a new type of unmanned aerial vehicle (UAV) lifted by the Coanda effect, called SCoanda. We introduce the empirical study of its design and implementations. The principle of the Coanda effect is briefly explained in the first section. The design optimization and control of the UAV, and implementation issues are explained. The structural design of SCoanda is devised to use the maximum lift force produced by the Coanda effect. Since there are a plenty of cases about the shape of SCoanda, it has been limited to oval. It is investigated through computational fluid dynamics. As the result, an optimized design is determined among several candidates and appropriate structural materials are selected via tests. Also, dynamic modeling is suggested under several conditions of SCoanda before simulations are conducted to build up a controller and determine gains according to the dynamic modeling. Finally, the UAV is manufactured and its performances are successfully demonstrated in the indoor and the outdoor environments.

Lyapunov based trajectory tracking controller for a quadrotor UAV with nonholonomic constraints

Ensuring UAV safety: A deep learning-based fault-tolerant approach with control barrier functions