Nonlinear control of quadrotor UAV under rotor failure for robust trajectory tracking

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

Accurate and precise trajectory tracking is crucial for unmanned aerial vehicles (UAVs) to operate in disturbed environments. This paper presents a novel tracking hybrid controller for a quadrotor UAV that combines the robust adaptive neuro-fuzzy inference system (ANFIS) and particle swarm optimization (PSO) algorithm. The ANFIS-PSO controller is implemented to govern the behavior of three degrees of freedom quadrotor UAV. The ANFIS controller allows controlling the movement of UAV to track a given trajectory in a 2D vertical plane. The PSO algorithm provides an automatic adjustment of the ANFIS parameters to reduce tracking error and improve the quality of the controller. The results showed perfect behavior for the control law to control a UAV trajectory tracking task. To show the effectiveness of the intelligent controller, simulation results are given to confirm the advantages of the proposed control method, compared with ANFIS and PID control methods.

: 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.

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.

<p>This paper presents a 3D guidance scheme for<br>\nUnmanned Aerial Vehicles (UAVs). The proposed guidance scheme<br>\nis based on the sliding mode approach using nonlinear sliding<br>\nmanifolds. Generalized 3D kinematic equations are considered<br>\nhere during the design process to cater for the coupling between<br>\nlongitudinal and lateral motions. Sliding mode based guidance<br>\nscheme is then derived for the multiple-input multiple-output<br>\n(MIMO) system using the proposed nonlinear manifolds. Instead of<br>\ntraditional sliding surfaces, nonlinear sliding surfaces are proposed<br>\nhere for performance and stability in all flight conditions. In the<br>\nreaching phase control inputs, the bang-bang terms with signum<br>\nfunctions are accompanied with proportional terms in order to reduce<br>\nthe chattering amplitudes. The Proposed 3D guidance scheme is<br>\nimplemented on a 6-degrees-of-freedom (6-dof) simulation of a UAV<br>\nand simulation results are presented here for different 3D trajectories<br>\nwith and without disturbances.</p>

Unmanned Aerial Vehicles (UAVs) have become essential tools in various industries for tasks such as inspection, maintenance, and surveillance. An Online Impedance Adaptive Controller (OIAC) is proposed for the online modulating of UAV control gains to obtain better performance and stability of tracking curved trajectories than the traditional methods, Model Reference Adaptive Controller (MRAC) and Proportional–Integral–Derivative (PID). Two UAV path planners with minimal jerk and snap were integrated into OIAC, MRAC, and PID. These six controllers were implemented and compared in a simulated UAV with perceptional noise, which follows curved pipelines and avoids obstacles. Experimental results show that the OIAC controller achieves at least an 80% improvement over the PID controller across all trajectory types in terms of the trajectory tracking error. Additionally, OIAC demonstrates an over 20% improvement in jerk trajectories and a more than 30% improvement in snap trajectories when compared to the MRAC controller. These results indicate that OIAC offers enhanced trajectory tracking accuracy and robustness against perceptual noise. Our work presents an advanced controller of a UAV and its preliminary validation in accurate and robust path tracking.

The propulsion subsystem of multi-rotor Unmanned Aerial Vehicles (UAV) is one of the most complex due to the aerodynamic, aero-elastic and electromechanical elements it comprises. Therefore, accurate models of this subsystem can be difficult to work with. Therefore, simplified models are normally used for the design of control and navigation algorithms. Considering this, the effectiveness of these algorithms is heavily dependent on the identification process used for the estimation of the parameters of the simplified propulsion model. On the other hand, the novel method of fuzzy adaptive neurons (FANs) have interesting characteristics that make them attractive for applications in which a fast response and good precision are required. In this article, the identification of the parameters of the propulsion system and the trajectory tracking of a multi-rotor UAV using FANs is explored. The efficient learning algorithm of the FANs is applied to identify the parameters of a simplified model of the propulsion system and to the self-tuning proportional integral derivative (PID) controllers of the trajectory tracking system. The proposed simplified model with the identified parameters is tested with experimental data obtained with low speed wind tunnels. The proposed PID controllers with self-tuning gains defined by the algorithm of the FANs for trajectory tracking system, are verified with simulations in MATLAB/Simulink® environment. The results showed that the parameter identification and trajectory tracking with PID controllers with self-tuning gains defined by the algorithm of the FANs, are suitable for estimating the parameters of the simplified model and track the trajectory with better error reduction than a classical PID controller.

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.

This paper presents the development of direct adaptive control laws to enable an autonomous unmanned aerial vehicle (UAV) to track reference trajectories in the presence of external disturbances. A maneuver database of reference trajectories is computed offline from a linear UAV dynamic model using an optimization routine. Each maneuver in the database is therefore represented as a state trajectory, as well as the control input sequence that generates that trajectory. The trajectory tracking control laws are designed to enable a UAV to follow reference trajectories from this maneuver database. A disturbance accommodating control law is first developed and applied for the tracking of reference trajectories while the UAV is subjected to a persistent external disturbance. This control design requires a state-space disturbance generator model and the derivation of ideal trajectories from a reference model, which requires the solution of a set of matching conditions. A direct adaptive controller is then developed to enable the UAV to track reference trajectories while subject to modeling error and external disturbances. The adaptive controller also requires a disturbance model and the existence of ideal trajectories, but it does not require the explicit solution of the matching conditions. Simulation results for the disturbance accommodating controller and the adaptive controller are presented using a linear six-degree-of-freedom dynamic model that is representative of typical UAV dynamics. The results show that, although both controllers track reference trajectories and provide disturbance rejection, the adaptive controller is better suited for handling modeling errors or uncertainties.

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.

In order to improve the endurance of unmanned aerial vehicles (UAVs), a power supply controller with automatic switching between solar energy and battery was designed. The controller is mainly composed of an AT89C51 single chip microcomputer. By collecting ambient light intensity and comparing it with the preset light intensity of the system, the controller completes automatic switching of the power supply for solar unmanned aerial vehicles, thereby achieving continuous and stable power supply for solar unmanned aerial vehicles. In addition, the implementation method of the single-chip microcomputer program in the controller is also introduced. The control core is to use Schmidt de shake control to switch between solar energy and battery, and to test the various functions of the controller through the flight experiment of a small solar powered unmanned aerial vehicle. Tests have found that solar powered drones using this controller can navigate for a long time in weather with varying light intensity, and the device operates well, effectively improving the power stability of solar powered drones.

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.

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.

In this article simultaneous arrival of multiple unmanned aerial vehicle (UAV) at a common point was studied in a fixed network hierarchy is used. By considering the model of fixed wing UAV the decentralized based control algorithm is developed for the formation control of all UAV’s. The tree based networked geometry is developed for the formation control of multiple UAV’s with respect to the leader. Whereas, UAV’s agent or (leader / follower) are autonomously connected with each other. Formation of flight via decentralized control structure which contains two parts, one is to achieve the arrival of expected meet-point for the formation geometry and other is to maintain the synchronization of all UAV’s. Moreover, the leader is supposed and able to regulate the speed or velocity of UAV’s. Simulated results showed that arrival of multiple UAV’s at a common point for the formation is achieved successfully and the above designed control algorithm has good robustness and flexible.

The design of reliable navigation and control systems for Unmanned Aerial Vehicles (UAVs) based only on visual cues and inertial data has many unsolved challenging problems, ranging from hardware and software development to pure control-theoretical issues. This chapter addresses these issues by developing and implementing an adaptive vision-based autopilot for navigation and control of small and mini rotorcraft UAVs. The proposed autopilot includes a Visual Odometer (VO) for navigation in GPS-denied environments and a nonlinear control system for flight control and target tracking. The VO estimates the rotorcraft ego-motion by identifying and tracking visual features in the environment, using a single camera mounted on-board the vehicle. The VO has been augmented by an adaptive mechanism that fuses optic flow and inertial measurements to determine the range and to recover the 3D position and velocity of the vehicle. The adaptive VO pose estimates are then exploited by a nonlinear hierarchical controller for achieving various navigational tasks including take-off, landing, hovering, trajectory tracking, target tracking, etc. Furthermore, the asymptotic stability of the entire closed-loop system has been established using systems in cascade and adaptive control theories. Experimental flight test data over various ranges of the flight envelope illustrate that the proposed vision-based autopilot performs well and allows a mini rotorcraft UAV to achieve autonomously advanced flight behaviours by using vision.