There are nonlinear control methods for tracking of UAV in the literature but their performances were not satisfactory. In this study a position and attitude tracking nonlinear controller is developed for a quadrotor UAV including the disturbance terms in the model. The design method is based on Lyapunov stability theory, combining sliding mode control with backstepping. The control implementation has been exercised for varying the positions and angles in a flight. The tracking performance and robustness of the proposed control method has been demonstrated and compared with (i) standard LQR control, (ii) conventional backstepping control, and (iii) conventional PID control from the literature. From the simulation results it has been concluded that the proposed approach is effectively promising for both the position and attitude trackingcontrol of the quadrotor to their desired/ reference values in finite time. Also results show that, the effect of aerodynamic forces, moments and external disturbances are invisible on all the states variables, controller and sliding variables. The tracking capability of the controller can further be improved by some modifications as, by tuning parameters using optimisation technique, by using higher sliding mode control, using nonlinear sliding surface as future work.
This project titled “ROTOR-CRAFT” is connected with the development and control of an UnmannedAerialVehicle. Using different components and developing the design, for betterment of the society which would be useful for multipurpose applications.
2 Inertial Measurement Unit (IMU). IMU is an electronic device and will provide information about the velocity, orientation, and gravitational forces of rigid body. An IMU system has three types of inertial sensors which are gyroscopes, accelerometer and magnetometer will provide three inertial measurement for three different axis (x, y and z axis). Due to the low cost sensors, may be affected by noise. For example, Micro-electrical mechanical system (MEMS) gyroscopes is used to measure angular rate of rotation along roll, pitch and yaw axis, it usually suffer from measurement noise due to vibration. Besides that, poor implementation process for the linear controllers will also affect the attitude and stability of the UAVs. Hence, this study is going to design and fabricate robust attitude control with low cost hexacopter that propose a stable attitude state feedback linear controller model used for behavior and control algorithm testing under indoor environment which neglect all the disturbance, before implementation on the experimental setup.
Some UAV currently transmit housekeeping data to the control station where it is recorded. The data is obtained from sensors in the aircraft, and may include aircraft position data, communications status, airspeed, control information, fuel state, electrical power status, temperatures, etc. This information is used to alert the operators to any deterioration in the aircraft condition so that appropriate action can be taken to prevent or minimise the risk of failure. Where the aircraft is operating for periods of autonomy in radio silence and during that period suffers a fatal failure, the data may not have been received. In those
With the increase in available UAV technology for civilian and military applications, the present work sought to assess the validity of an objective methodology for UAV interface design evaluation. A pilot’s ability to navigate, monitor vehicle status, and manipulate flight parameters is essential for successfully accomplishing UAV missions – no matter what the objective may be. Taking off, navigating a given flight path, and dealing with emergencies are all actions UAV pilots are expected to be able to perform, and are especially important as any error can have serious financial or even life-threatening consequences. The design of UAV supervisory control interfaces mediates pilot capability to effectively complete such tasks. Beyond this, pilot experience, or lack thereof, can also be a critical factor. The FAA, under Part 107, licenses a 16 years old, who passes an aeronautical test, to fly a 55 pound UAV up to 100 miles per hour (FAA, 2016) – with no live demonstration of competence; basically, anyone can become an UAV pilot and occupy air space. This situation further emphasizes the importance of development of system interfaces that make necessary
In previous related works, a first flight of the UAV over the inspected area is always performed before the beginning of the mission. A traversability map is then processed to provide a trajectory to the UGV. A first contribution of our work consists in providing a real-time navigation scheme: the UAV flies over the area to provide a global coverage, and to assist a UGV in real-time to navigate safely avoiding obstacles. The UAV follows continuously and autonomously the UGV using the developed visual servoing algorithm and controller. The second contribution of our work is to use an off-the-shelf technology (commercial UAVs and open source libraries) to propose an efficient control law in order to develop a GPS denied tracking system. The result of this work is used to provide the UGV with an aerial coverage view of the navigation area to facilitate its navigation through obstacles. The UGV goes through waypoints provided by a human operator. For numerous reasons, monitoring, surveillance and inspection of industrial sites belonging to Seveso category (highly critical sites) cannot be performed without a constant and careful attention of human experts. Related works and the choice of the controller will be discussed further in this paper.
SMC simulations showed that the nonlinear technique was able to track the input signal even when accompanied by fast dynamics and abrupt changes. The SMC control effort however was very high with significant chattering. This was tackled by chattering alleviation technique from the literature. Chattering alleviation for SMC allowed the use of higher gains which in turn damped the overshoot and allowed for better performance. SMC also dealt significantly better than the PD in the presence of disturbance. Non- holonomic constraints that enabled the trajectory tracking of the underactu- ated system.
N cmodern aircraft design it is vital to quickly be able to develop a controller that will give dynamics which are reasonably close to the desired dynamics in order to gain sufficient understanding of various aspects of the proposed aircraft concept. The main focus is normally on maneuver- and control properties, including handling qualities and actuator effectiveness, properties which are commonly appraised via simulations. To be able to design a controller at an early stage in an aircraft design is especially important in the design of modern fighters and unmannedaerial vehicles (UAVs) since such aircraft can be aerodynamically unstable, at certain speeds, in both pitch and yaw. This means that even basic maneuvering properties cannot be evaluated without the existence of a stabilizing controller. Aircraft dynamics are most often described (without concern of aero elastic effects) by Newton-Euler’s nonlinear rigid body equations, formulated in a vehicle fixed coordinate system. The forces and moments that act on the body, and which can be altered by the control surfaces, are nonlinear functions of the states in the equations of motion. This makes it natural to use nonlinear control theory in the design of controllers. Key aspects of concern are then stability, robustness and performance. In the present work we concentrate on these aspects for the State Dependent Riccati Equation (SDRE) nonlinear design method applied to a realistic model of an UAV is illustrated in Fig.1.
The rest of the paper is organized as follows: Section 2 demonstrates the dynamic model of the transportation quadrotor helicopters. In Section 3, we present the RBFNN-based backstepping sliding mode control algorithm and also discuss the stability analysis of the transportation platform. The prototype of the UAV and experimental setup used to evaluate the reliability of the proposed architecture is demonstrated in Section 4. Finally, the conclusion is given in Section 5.
Abstract — A Micro AerialVehicle (MAV) is known as a drone or in a bigger size is called UnmannedAerialVehicle (UAV). Quadrotors are leading edge of a huge development in military and civilian such as disaster search and rescue, surveillance, aerial mapping and others. However, those applications limits by the payload delivered and long execution time. Hence, this study focuses on Leader-Follower approach of Quadrotor MAV. The study covers the development of quadrotor platform, modelling, controller design and leader-follower implementation. As the preliminary study, an Android phone is used as a leader which is used to provide the desired position and orientation to the follower quadrotor. The follower will be an autonomous quadrotor. Proportional Integral Derivative (PID) controller for the position and attitude control are first designed and tested via simulation. Then, a real flight implementation is conducted. The result shows that the follower can follow the leader on a circular path and straight line path. The settling time for X, Y and Z position of the follower is 10.22, 10.90 and 19.45 seconds, respectively. Additionally, the overshoot percentage for X, Y and Z position are 7%, 0% and 0%, respectively.
physiological workload response measures (i.e., BD decrease ratio) and subjective ratings. Although the BD decrease ratio successfully revealed operator workload differences in using the three interface designs, individual response values did not vary closely with subjective workload ratings. The lack of significant might also suggest the complementarity of eye- tracking measure to subjective approach in workload assessment. Related to the analysis on BD decrease ratio and TLX sub-component ratings, results revealed significant and positive correlation between the BD decrease ratio and the Physical Demand ratings. The Physical Demand was rated based on how much physical activity was required and if the task was easy vs. demanding, slow vs. brisk, slack vs. strenuous and restful vs. laborious. Therefore, higher Physical Demand ratings were likely associated with more demanding control actions during the task performance, which possibly produced greater visual attention needs and, therefore, higher BD decrease ratio for participants. The BD decrease ratio also showed significant positive correlation with the Performance Demand, indicating the decrement in performance under higher cognitive workload. With respect to DKQ accuracy, a significant negative correlation was found with both the BD decrease ratio and TLX scores. This finding also suggested that the increase in cognitive workload led to a decrement in performance and cognitive resources.
Medeiros et al.  described PHM-based multi-UAV task assignment for identifying the application of integrated vehicle health management (IVHM) ideas focused on prognostics and health monitoring (PHM) procedures to multi-UAV frameworks. Considering UAV as a mission basic framework, it was required and needed to fulfil its operational goals with insignificant unscheduled interferences. So, it does bode well for UAV to exploit those strategies as empowering agents for the availability of multi-UAV. The principle objective was to apply data from a PHM framework to perform decision making with the help of IVHM structure. UAV RUL was processed by the method for a fault tree examination that it was nourished by a circulation capacity from a likelihood thickness capacity relating time and disappointment likelihood for every UAV discriminating parts. The IVHM system, for this situation, it was the assignment task focused on UAV wellbeing condition (RUL data) utilizing the receding horizon task assignment (RHTA) algorithm. The study case was created considering a group of electrical little UAVs and pitch control framework was picked as the basic framework.
Descent using switching descent law Since it was evident that using a normal decent law would not be robust enough, testing turned towards using a switching strategy. The biggest drawback of such a method is that unexpected behavior might occur when going between different flight modes. Guaranteeing that a switching controller will perform a certain way can be difficult because of the hybrid nature of the total system. It is known that even a combination of stable subsystems might become unstable when used together with a switching control if used incorrectly . Another reason why it might be difficult to design such a system is that it is difficult to foresee what might happen with different combinations of system states and control modes. An example of this type of problematic behavior can be seen in Figure 8.4, where the retry mode and the coupling in velocity and altitude causes the x position to oscillate as the altitude is going between h 1 = 10 m and h 0 = 5 m. The change in x causes further retries,
We have presented a model and control methodology to construct an unmannedaerialvehicle using the standard design procedures. By characterizing the forces and torques experienced by the UAV during both flight and manipulation, we have designed the structure to handle the before mentioned forces and also to compensate for reactionary forces.
An unmannedaerialvehicle (UAV), commonly known as a drone, is an aircraft without a human pilot aboard. It can climb, fall, hover, yaw, etc. UAVs are relatively small and convenient to use. UAVs have broad application prospects in military and civilian areas, including intelligence access, target tracking, monitoring, etc. The UAV is an underactuated sys- tem  that has six degrees-of-freedom (position and orientation) and multiple control inputs (e.g., rotor speed). It also has multivariable, non-linear, and strong coupling characteristics, all of which make its flight-controldesign very difficult. In UAV simulation systems, the interaction between a UAV and its possibly-complex surrounding environment must be considered; hence, accurate collision detection is an- other focus. Accurate collision detection can improve the authenticity and reliability of the UAV simulation system, giving the user a better sense of immersion.
Franco et al (2017 introduces a low-cost embedded system design and implementation for a two-axis camera platform control used in UAVs. The main goal is to have a gimbal able to stabilize a camera, with respect to UAV attitude, using permanent magnet synchronous motors as actuators. Their design also uses an inertial measurement unit as a sensor and a proportional-integral-derivative controller. Simulated and experimental results show that the developed embedded system was able to successfully respond to a simulated UAV flight envelope, reacting in real-time and minimizing those disturbances .
Quadrotor helicopter is a kind of vertical take-off and landing (VTOL) multi-rotor unmannedaerial vehicles (UAV). This kind of helicopter has many characteristics e.g., stable hovering and maneuverable flight in tough environment, its important advantage is load capacity. As these advantages, quadrotor helicopter has many utilities, such as search and rescue, building exploration, security and inspection, etc. especially in dangerous and inacces- sible environments. So the aircraft has been widely used in various fields due to the advantages, such as air trans- port, crop monitoring and military reconnaissance. Con- ventional fixed-wing aircraft can fly at very high flight speed, but must rely on the runway in taking off and landing process. On the contrary, the helicopter can take off vertically out of the shackles of the runway, but its flight speed has been greatly hindered. After the Second World War, some companies in the United States, Can- ada and Europe started to develop a kind of vertical take- off and landing (VTOL) aircraft which has the advan- tages of both fixed-wing aircraft and helicopter, obtain- ing high speed at the same time getting rid of the de- pendence on the runway.
that the yaw of the quadcopter is inherently known and thus there is no need to estimate. From this research, an understanding is developed to implement a consistent quadcopter controller for attitude stabilization and position navigation. Coordinated landing of a quadcopter on a moving ground vehicle is discussed in ,  and . In these works the camera is mounted on the ground vehicle or at the ground station but the camera is stationary. Conventionally, quadcopters carry a camera on them for vision based navigation, but for the work in this a thesis camera is mounted on the moving ground vehicle. The camera faces up and forwards and provides images of the moving quadcopter. This simplifies the problem statement as it aids in pose estimations from ground vehicle and simplifies the quadcopter control problem. This also helps in ground air interactions for multi-agent systems. Also, to deal with situations of temporary target loss the vision-based tracking algorithm was coupled with Kalman filter to estimate position.
experiments is to use MEMs devices to control attitude and close the loop around position, even though many of the estimation schemes can extract pose directly. Alt˘ ug et al use a camera on the ground observing the quadrotor to estimate pose and position, although a set of onboard MEMS gyros were also used to control attitude [Alt˘ ug et al 2002]. The attitude regulator used was a feedback linearisation controller. This system was expanded to include an onboard camera that tracks the position of the ground station — this improved the attitude con- trol to a ±5 degrees and the position tracking to ±130 mm [Alt˘ ug et al 2003]. Although it has yet to be implemented in hardware, Shakernia et al have shown that a UAV can land using motion reconstruction from visual observation of markers on a landing pad [Shakernia et al 1999], although onboard accelerome- ters are still required for pose extraction. Their egomotion estimator was used inside the control loop to recover pose and position information. In simulation their system was able to control both quadrotor position and pose given noisy target data. Romero et al use a similar ‘N-points’ and ‘2D planar’ technique, but with full 6-axis INS data for control [Romero et al 2003]. The first method uses a set of points of known relative position, and the second uses a cluster of unknown points constrained to lie in a known 2D plane to extract pose. Both of these provide ±100 mm position tracking, but no pitch-roll accuracy is given.
Finally, when the information processed by the AC Attitude Control and the AC PosControl libraries arrives to the AP Motors, library, it is translated into absolute motor values, which are in PWM (Pulse Wide Modulation). PWM are modulation signal that encode the motor speed into a pulsing signal. These signals have two possible states: high (usually 5V) or low (usually 0V) voltage. The amount of time that the signal voltage is high every 2000µs indicates the value of the motor speed, being minimum for 1000µs of high voltage and maximum when the high voltage last the 2000µs time. Then, the Output armed function is called, which checks the physical validity of the PWM obtained and passes these values to the AP HAL libraries layer. These last libraries are responsible to send the PWM output to the right pin of the board. A schematic description of this operation procedure is presented in Figure 4.5.