Control Of Micro Aerial Vehicles Research Articles

Experimental Investigation on Aerodynamic Performance of Inclined Hovering with Asymmetric Wing Rotation.

This study presents a model experiment method that can accurately reproduce the flapping motion of insect wings and measure related unsteady aerodynamic data in real time. This method is applied to investigate the aerodynamic characteristics of inclined hovering, which distinguishes it from normal hovering by having asymmetric wing rotation during the two half strokes. In the study of the aerodynamic influence of the downstroke rotational angle, it is found that the rotational angle affects lift generation by changing the angle between the wing surface and the horizontal plane in the mid-downstroke. When the wing is almost parallel to the horizontal plane in the mid-downstroke, the vortex structure can maintain structural integrity and a large magnitude, which is conducive to the generation of high lift. In the study of the aerodynamic effect of the upstroke rotational angle, the windward conversion mechanism is proposed to explain the influence of the upstroke rotational angle on the direction and magnitude of thrust. Obtaining the rotational angle that is most conducive to maintaining the flight state of hovering in the present study can provide guidance for the structural design and kinematic control of micro aerial vehicles.

Robust and Efficient Velocity Estimation for MAVs With an RGB-D Camera

Micro Aerial Vehicles (MAVs) have become increasingly popular due to their wide applications in many areas. For autonomous navigation and safe control of MAVs, it is essential to have accurate and reliable velocity and position estimation. However, due to limited computational power and payload, it is still challenging for autonomous operation of MAVs in complex environments. In this paper, we propose a robust and efficient velocity estimation framework for MAVs with a single downward-facing RGB-D camera, which is able to provide metric velocity estimation in three dimensions as well as yaw rate in real-time without the fusion of additional sensors. Unlike traditional algorithms, our method uses kernel cross-correlators (KCCs) to efficiently determine optical flow for motion estimation, which does not rely on costly feature extraction and matching process. Moreover, we utilize depth images to estimate the vertical velocity of MAVs without the assumption of a flat ground. Autonomous flight tests on a quadrotor in complex environments demonstrate the robustness and efficiency of our method.

Visual-Inertial Cross Fusion: A Fast and Accurate State Estimation Framework for Micro Flapping Wing Rotors

Real-time and drift-free state estimation is essential for the flight control of Micro Aerial Vehicles (MAVs). Due to the vibration caused by the particular flapping motion and the stringent constraints of scale, weight, and power, state estimation divergence actually becomes an open challenge for flapping wing platforms’ longterm stable flight. Unlike conventional MAVs, the direct adoption of mature state estimation strategies, such as inertial or vision-based methods, has difficulty obtaining satisfactory sensing performance on flapping wing platforms. Inertial sensors offer high sampling frequency but suffer from flapping-introduced oscillation and drift. External visual sensors, such as motion capture systems, can provide accurate feedback but come with a relatively low sampling rate and severe delay. This work proposes a novel state estimation framework to combine the merits from both to address such key sensing challenges of a special flapping wing platform—micro flapping wing rotors (FWRs). In particular, a cross-fusion scheme, which integrates two alternately updated Extended Kalman Filters based on a convex combination, is proposed to tightly fuse both onboard inertial and external visual information. Such a design leverages both the high sampling rate of the inertial feedback and the accuracy of the external vision-based feedback. To address the sensing delay of the visual feedback, a ring buffer is designed to cache historical states for online drift compensation. Experimental validations have been conducted on two sophisticated microFWRs with different actuation and control principles. Both of them show realtime and drift-free state estimation.

Vision Based Output Feedback Control of Micro Aerial Vehicles in Indoor Environments

We present a new image based visual servoing (IBVS) approach for control of micro aerial vehicles (MAVs) in indoor environments. Specifically, we show how a MAV can be stabilized and guided using only corridor lines viewed on a front facing camera and angular velocity measurements. Since the suggested controller does not include explicit attitude feedback it does not require the use of accelerometers which are susceptible to vibrations, nor complex attitude estimation algorithms. The controller also does not require direct velocity measurements which are difficult to obtain in indoor environments. The paper presents the new method, stability analysis, simulations and experiments.

APPLICATION OF VEHICLE DYNAMIC MODELING IN UAVS FOR PRECISE DETERMINATION OF EXTERIOR ORIENTATION

Advances in unmanned aerial vehicles (UAV) and especially micro aerial vehicle (MAV) technology together with increasing quality and decreasing price of imaging devices have resulted in growing use of MAVs in photogrammetry. The practicality of MAV mapping is seriously enhanced with the ability to determine parameters of exterior orientation (EO) with sufficient accuracy, in both absolute and relative senses (change of attitude between successive images). While differential carrier phase GNSS satisfies cm-level positioning accuracy, precise attitude determination is essential for both direct sensor orientation (DiSO) and integrated sensor orientation (ISO) in corridor mapping or in block configuration imaging over surfaces with low texture. Limited cost, size, and weight of MAVs represent limitations on quality of onboard navigation sensors and puts emphasis on exploiting full capacity of available resources. Typically short flying times (10-30 minutes) also limit the possibility of estimating and/or correcting factors such as sensor misalignment and poor attitude initialization of inertial navigation system (INS). This research aims at increasing the accuracy of attitude determination in both absolute and relative senses with no extra sensors onboard. In comparison to classical INS/GNSS setup, novel approach is presented here to integrated state estimation, in which vehicle dynamic model (VDM) is used as the main process model. Such system benefits from available information from autopilot and physical properties of the platform in enhancing performance of determination of trajectory and parameters of exterior orientation consequently. The navigation system employs a differential carrier phase GNSS receiver and a micro electro-mechanical system (MEMS) grade inertial measurement unit (IMU), together with MAV control input from autopilot. Monte-Carlo simulation has been performed on trajectories for typical corridor mapping and block imaging. Results reveal considerable reduction in attitude errors with respect to conventional INS/GNSS system, in both absolute and relative senses. This eventually translates into higher redundancy and accuracy for photogrammetry applications.

APPLICATION OF VEHICLE DYNAMIC MODELING IN UAVS FOR PRECISE DETERMINATION OF EXTERIOR ORIENTATION

Abstract. Advances in unmanned aerial vehicles (UAV) and especially micro aerial vehicle (MAV) technology together with increasing quality and decreasing price of imaging devices have resulted in growing use of MAVs in photogrammetry. The practicality of MAV mapping is seriously enhanced with the ability to determine parameters of exterior orientation (EO) with sufficient accuracy, in both absolute and relative senses (change of attitude between successive images). While differential carrier phase GNSS satisfies cm-level positioning accuracy, precise attitude determination is essential for both direct sensor orientation (DiSO) and integrated sensor orientation (ISO) in corridor mapping or in block configuration imaging over surfaces with low texture. Limited cost, size, and weight of MAVs represent limitations on quality of onboard navigation sensors and puts emphasis on exploiting full capacity of available resources. Typically short flying times (10-30 minutes) also limit the possibility of estimating and/or correcting factors such as sensor misalignment and poor attitude initialization of inertial navigation system (INS). This research aims at increasing the accuracy of attitude determination in both absolute and relative senses with no extra sensors onboard. In comparison to classical INS/GNSS setup, novel approach is presented here to integrated state estimation, in which vehicle dynamic model (VDM) is used as the main process model. Such system benefits from available information from autopilot and physical properties of the platform in enhancing performance of determination of trajectory and parameters of exterior orientation consequently. The navigation system employs a differential carrier phase GNSS receiver and a micro electro-mechanical system (MEMS) grade inertial measurement unit (IMU), together with MAV control input from autopilot. Monte-Carlo simulation has been performed on trajectories for typical corridor mapping and block imaging. Results reveal considerable reduction in attitude errors with respect to conventional INS/GNSS system, in both absolute and relative senses. This eventually translates into higher redundancy and accuracy for photogrammetry applications.

System for deployment of groups of unmanned micro aerial vehicles in GPS-denied environments using onboard visual relative localization

A complex system for control of swarms of micro aerial vehicles (MAV), in literature also called as unmanned aerial vehicles (UAV) or unmanned aerial systems (UAS), stabilized via an onboard visual relative localization is described in this paper. The main purpose of this work is to verify the possibility of self-stabilization of multi-MAV groups without an external global positioning system. This approach enables the deployment of MAV swarms outside laboratory conditions, and it may be considered an enabling technique for utilizing fleets of MAVs in real-world scenarios. The proposed visual-based stabilization approach has been designed for numerous different multi-UAV robotic applications (leader-follower UAV formation stabilization, UAV swarm stabilization and deployment in surveillance scenarios, cooperative UAV sensory measurement) in this paper. Deployment of the system in real-world scenarios truthfully verifies its operational constraints, given by limited onboard sensing suites and processing capabilities. The performance of the presented approach (MAV control, motion planning, MAV stabilization, and trajectory planning) in multi-MAV applications has been validated by experimental results in indoor as well as in challenging outdoor environments (e.g., in windy conditions and in a former pit mine).

An adaptive neural network approach to the tracking control of micro aerial vehicles in constrained space

ABSTRACTThis paper presents an adaptive neural network approach to the trajectory tracking control of micro aerial vehicles especially when they are flying in a limited indoor area. Differing from conventional controllers, the proposed controller employs the outer position loop to directly generate angular velocity commands in the presence of unknown aerodynamics and disturbances and then the fast inner loop to handle the angular rate control. Adaptive neural networks are deployed to estimate all the uncertain factors with the adaptation law derived from the Lyapunov function. To achieve a real-time performance, a norm estimation approach of ideal weights is designed to achieve a high bandwidth and lighten the burden of computation burden. Meanwhile, a barrier Lyapunov function is introduced to guarantee the constraint of vehicle positions as well as the validity of the neural network estimation. Simulations and practical flight tests are conducted to verify the feasibility and effectiveness of the proposed control strategy.

Modified Cascade Kalman Filter for Sensor Data Fusion in Micro Aerial Vehicle∗∗This work is funded by the DRDO/NPMICAV project.

Precise attitude estimation is important for navigation, guidance and control of Micro Aerial Vehicles (MAV) as they are mostly equipped with low integrity sensors due to the constraints on MAV payload and able power. The MAY sensors such as accelerometer and magnetometer are prone to noise as they are placed in proximity to the motor due to constraints on centre of gravity (CG). Data from a single sensor are not reliable for all operating points of flight envelope as the motor vibration and magnetic flux due to the motor vary with RPM. Hence accurate attitude estimation of MAV is achieved through multisensor data fusion. In this paper, a modification to the classical cascade Kalman filter is proposed. Modified Cascade Kalman Filter (MCKF) has better estimation performance as it is a single Kalman filter similar to measurement fusion technique and also it restores the flexibility and computational efficiency of state vector fusion method. A numerical example is presented wherein the derived MCKF is implemented for the attitude estimation of MAV in the 6 DOF simulation model developed in MATLAB/SIMULINK and also for a set of calibrated accelerometer and magnetometer sensor data With motor noise acquired from the autopilot mounted on the rate table of the notion simulator. It was found that MCKF exhibits substantially improved performance compared to extended Kalman filter with only accelerometer data with motor noise. (C) 2016, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

Study of Performance of ADRC for Longitudinal Control of MAV

Abstract Most classical control design methods are based on mathematical description of plant dynamics. However many physical plants in real world are not only nonlinear and time varying but also highly uncertain. Accurate mathematical description of physical plants are usually not available. Disturbance rejection is another issue in control design. Active Disturbance Rejection Control (ADRC) offers an inherently robust controller which requires very little information of the plant. This control algorithm actively estimates and compensates for the effects of the unknown dynamics and disturbances using extended state observer (ESO). Advantages of ESO and ADRC motivated to implement it on micro-aerial vehicle (MAV), which is highly nonlinear and prone to parametric uncertainties and disturbances while flying. This paper talks about the nonlinear control scheme for longitudinal control of MAV using ADRC. Further the gains of observer and controller are designed using bandwidth parameterization. There are mainly two contributions of this paper: design of observer and controller gains as a function of aerodynamic coefficient and airspeed of MAV and validation of robustness of control algorithm for parametric uncertainties, measurement noise and wind disturbance.

A Cross-Platform Comparison of Visual Marker Based Approaches for Autonomous Flight of Quadrocopters

In this paper, we compare three different marker based approaches for six degrees of freedom (6DOF) pose estimation, which can be used for position and attitude control of micro aerial vehicles (MA...

Lift evaluation of a two-dimensional pitching flat plate

Several previous experimental and theoretical studies have shown that a leading edge vortex (LEV) on an airfoil or wing can provide lift enhancement. In this paper, unsteady two-dimensional (2D) potential flow theory is employed to model the flow field of a pitching flat plate wing. A multi-vortices model is developed to model both the leading edge and trailing edge vortices (TEVs), which offers improved accuracy compared with using only single vortex at each separation location. The lift is obtained by integrating the unsteady Blasius equation. It is found that the motion of vortices contributes significantly to the overall aerodynamic force on the flat plate. A Kutta-like condition is used to determine the vortex intensity and location at the leading edge for large angle of attack cases; however, it is proposed to relax this condition for small angle of attack cases and apply a 2D shear layer model to calculate the circulation of the new added vortex. The results of the simulation are then compared with classical numerical, theoretical, and experimental data for canonical unsteady flat plat problems. Good agreement with these data is observed. Moreover, these results suggested that the leading edge vortex shedding for small angles of attack should be modeled differently than that for large angles of attack. Finally, the results of vortex motion vs. lift indicate that the slow convection of the LEV creates less negative lift while the rapid shedding of the TEV creates more positive lift. The difference between these two contributions of lift results in a total positive lift that lasts for about two chord-length travel of the plate. It is therefore concluded that the lift enhancement during the LEV “stabilization” above the wing is a combined effect of both the LEV and TEV motion. This also provides the insights for future active flow control of micro aerial vehicles (MAVs) that the formation and shedding process of LEVs and TEVs can be manipulated to provide lift enhancement.

Passive mitigation of roll stall for low aspect ratio wings

This investigation addresses an issue with the lateral stability and control of Micro Aerial Vehicles (MAVs) which is created by the presence of ‘roll stall’ and its effects on the aircraft’s stability derivatives. This behavior is a result of the developing tip vortex asymmetry of a low aspect ratio (LAR) wing in sideslip which creates an asymmetric spanwise load and a resulting roll moment. Experimental wind tunnel results indicate that roll stall produces a roll stability derivative, , for both the MAV models tested as well as canonical tapered flat wing cases; furthermore, even at small sideslip angles, the cross-coupled derivative links the longitudinal aerodynamics and the lateral loads of LAR wings, effects which are not present for large aircraft. The roll stall creates nonlinearites in lateral loads in an angle of attack regime in which the lift variation is linear; thus, control techniques for MAVs must consider the fact that lateral and longitudinal equations of motion can not necessarily be linearized about the same equilibrium flight conditions. Finally, the addition of winglets below the plane of the wing is seen to reduce by at least 50% in some cases, as well as essentially eliminating the derivative for a taper ratio of . This provides a passive control technique which can potentially be employed to significantly reduce the lateral instabilities of MAVs.

Effects of display mode and input method for handheld control of micro aerial vehicles for a reconnaissance mission

Micro aerial vehicles (MAVs) are small lightweight unmanned aerial vehicles used by dismounted soldiers for aerial reconnaissance and acquiring information for local situation awareness. MAVs require a portable handheld ground control station (GCS) that allows the operator to control and monitor the flight of the MAV. This paper investigated two methods of presenting map and sensor information, either simultaneously on one display or separately on two displays, requiring operator navigation. In addition, two input devices are evaluated: a touch screen display with a stylus versus a joystick with an OK button. The findings suggest that MAV GCSs that use touch screen inputs and simultaneous presentation of map and sensor information will result in better operator performance and reduced operator workload.

MAV Circular Leader-Follower Formation Control Utilizing Mass-Spring-Damper with Centripetal Force Consideration

Past research has dealt with numerous formation control problems related to leader-follower formation. In the move towards bio-inspired formation, this paper introduces flock formation based on migrating birds. Flock formation development can be subdivided into shape control, shape entrance control and leader change control. Shape control or keeping is the first part of development. Shape keeping in this paper utilizes a virtual spring and damper model to interconnect all the Micro Aerial Vehicles (MAVs) in the formation. Besides that, the algorithm also considers the centripetal force acting on each MAV since a circular/curve motion is being evaluated. The circular leader-follower formation control of multiple MAVs using virtual mass-spring-damper system with the consideration of centripetal force for flock formation shape keeping has been successfully designed and implemented. Based on the experimental result, it is seen that the performance of the algorithm is reliable.

Evolving a Multiagent Controller for Micro Aerial Vehicles

Micro aerial vehicles (MAVs) are notoriously difficult to control as they are light, susceptible to minor fluctuations in the environment, and obey highly nonlinear dynamics. Indeed, traditional control methods, particularly those relying on difficult to obtain models of the interaction between an MAV and its environment, have been unable to provide adequate control beyond simple maneuvers. In this paper, we address the problem of controlling an MAV (which has segmented control surfaces) by evolving a neurocontroller and fine tuning it using multiagent coordination techniques. This approach is based on a control strategy that learns to map MAV states (position and velocity) to MAV actions (e.g., actuator position) to achieve good performance (e.g., flight time) by maximizing an objective function. The main difficulty with this approach is defining the objective functions at the MAV level that allow good performance. In addition, to provide added robustness, we investigate a multiagent approach to control where each control surface aims to optimize a local objective. Our results show that this approach not only provides good MAV control, but provides robustness to: 1) wind gusts by a factor of 6; 2) turbulence by a factor of 4; and 3) hardware failures by a factor of 8 over a traditional control method.

Modeling Synchronous Muscle Function in Insect Flight: a Bio-Inspired Approach to Force Control in Flapping-Wing MAVs

Micro-aerial vehicles (MAV) and their promising applications--such as undetected surveillance or exploration of environments with little space for land-based maneuvers--are a well-known topic in the field of aerial robotics. Inspired by high maneuverability and agile flight of insects, over the past two decades a significant amount of effort has been dedicated to research on flapping-wing MAVs, most of which aim to address unique challenges in morphological construction, force production, and control strategy. Although remarkable solutions have been found for sufficient lift generation, effective methods for motion control still remain an open problem. The focus of this paper is to investigate general flight control mechanisms that are potentially used by real insects, thereby providing inspirations for flapping-wing MAV control. Through modeling the insect flight muscles, we show that stiffness and set point of the wing's joint can be respectively tuned to regulate the wing's lift and thrust forces. Therefore, employing a suitable controller with variable impedance actuators at each wing joint is a prospective approach to agile flight control of insect-inspired MAVs. The results of simulated flight experiments with one such controller are provided and support our claim.

Robust control of quadrotor MAV using self‐organizing interval type‐II fuzzy neural networks (SOIT‐IIFNNs) controller

PurposeQuadrotor micro aerial vehicle (MAV) is nonlinear and under actuated plant, and it is difficult to obtain an accurate mathematical model for quadrotor MAV due to uncertainties. The purpose of this paper is to propose one robust control strategy for quadrotor MAV to accommodate system uncertainties, variations, and external disturbances.Design/methodology/approachThe robust control strategy is composed of two self‐organizing interval type‐II fuzzy neural networks (SOIT‐IIFNNs) and one PD controller: the PD controller is adopted to control the attitude and position; one of the SOIT‐IIFNNs is designed to learn the inverse model of quadrotor MAV online; the other SOIT‐IIFNNs is the copy of the former one to compensate for model errors, system uncertainties and external disturbances, both structure and parameters of SOIT‐IIFNNs are tuned online at the same time, and then the stability of the resulting quadrotor MAV closed‐loop control system is proved using Lyapunov stability theory.FindingsThe validity of the proposed control method has been verified through real‐time experiments. The experimental results show that the performance of SOIT‐IIFNNs is significantly improved compared with Backstepping‐based controller.Practical implicationsThis approach has been used in quadrotor MAV, the controller works well, and it could guarantee quadrotor MAV control system with good performances under uncertainties, variations, and external disturbances.Originality/valueThe proposed SOIT‐IIFNNs controller is interesting for the design of an intelligent control scheme. The main contributions of this paper are: the overall closed‐loop control system is globally stable, demonstrated by Lyapunov stable theory; the tracking error can be asymptotically attenuated to a desired small level around zero by appropriate chosen parameters and learning rates; and the quadrotor MAV control system based on SOIT‐IIFNNs controller can achieve favorable tracking performance.

Euler Angle Based Attitude Estimation Avoiding the Singularity Problem

AbstractPrecise control of UAV (Unmanned Aerial Vehicle) or MAV (Micro Aerial Vehicle), requires the accurate determination of vehicle orientation. Attitude and heading reference systems (AHRS) are popularly used for vehicle orientation determination. In these systems, Euler angle estimation is more appropriate than quaternion estimation because the accelerometer measurement and magnetometer measurement are separated. Furthermore, the accelerometer and magnetometer measurements have independent effects on the tilt and heading angles. An Euler based attitude estimation system that uses two-stage extended Kalman filtering is proposed. To avoid singularity of Euler angle, a new heading estimation parameter is introduced and a filter mode switching algorithm is proposed. The experimental results indicated that the proposed system performed better than a quaternion algorithm based on magnetometer disturbance. Furthermore, the results indicated that the proposed algorithm was immune to the singularity problem.