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As a novel VTOL unmanned aerial vehicle, lifting-wing multicopter with simple structure, good stability and longer flight range has been unveiled to be in service. But how to make range as long as possible to realize its potential advantages is one of the key issues in the practical application of lifting-wing multicopter. Thus, this study aims to propose a range and transition time performance evaluation to realize a sizing optimization design method for the lifting-wing multicopter. First, the dynamic model and the propulsion system model are established. Based on these models, evaluation methods for range and transition time performance are obtained with the consideration of mounting angle. Second, the influences of the main parameters on range performance are studied to offer references for design. Then, an explicit design process is proposed. Finally, a prototype is implemented, and flight experiments are performed to verify the evaluation and design method. The results indicate that the performance evaluation method has enough accuracy and suggest that it is possible to design a lifting-wing multicopter using the presented design method.

Automated methods have become crucial components when searching for distinguishers against symmetric-key cryptographic primitives. While MILP and SAT solvers are among the most popular tools to model ciphers and perform cryptanalysis, other methods with different performance profiles are appearing. In this article, we explore the use of Constraint Programming (CP) for differential cryptanalysis on the Ascon authenticated encryption family (first choice of the CAESAR lightweight applications portfolio and current finalist of the NIST LWC competition) and its internal permutation. We first present a search methodology for finding differential characteristics for Ascon with CP, which can easily find the best differential characteristics already reported by the Ascon designers. This shows the capability of CP in generating easily good differential results compared to dedicated search heuristics. Based on our tool, we also parametrize the search strategies in CP to generate other differential characteristics with the goal of forming limited-birthday distinguishers for 4, 5, 6 and 7 rounds and rectangle attacks for 4 and 5 rounds of the Ascon internal permutation. We propose a categorization of the distinguishers into black-box and non-black-box to better differentiate them as they are often useful in different contexts. We also obtained limited-birthday distinguishers which represent currently the best known distinguishers for 4, 5 and 6 rounds under the category of non-black-box distinguishers. Leveraging again our tool, we have generated forgery attacks against both reduced-rounds Ascon-128 and Ascon-128A, improving over the best reported results at the time of writing. Finally, using the best differential characteristic we have found for 2 rounds, we could also improve a recent attack on round-reduced Ascon-HAsh. Agency for Science, Technology and Research (A*STAR) Published version The authors are supported by the Temasek Laboratories.

Electronic control systems are becoming more and more complicated, which makes it difficult to test them sufficiently only through experiments. Simulation is an efficient way in the development and testing of complex electronic systems, but the simulation results are always doubtful by people due to the lack of credible simulation platforms and assessment methods. This paper proposes a credible simulation platform based on real-time FPGA-based hardware-in-the-loop (HIL) simulation, and then an assessment method is proposed to quantitatively assess its simulation credibility. By using the FPGA to simulate all sensor chips, the simulation platform can ensure that the tested electronic system maintains the same hardware and software operating environment in both simulations and experiments, which makes it possible to perform the same tests in the simulation platform and the real experiment to compare and analyze the simulation errors. Then, the testing methods and assessment indices are proposed to assess the simulation platform from various perspectives, such as performance, time-domain response, and frequency-domain response. These indices are all normalized to the same scale (from 0 to 1) and mapped to a uniform assessment criterion, which makes it convenient to compare and synthesize different assessment indices. Finally, an overall assessment index is proposed by combining all assessment indices obtained from different tests to assess the simulation credibility of the whole simulation platform. The simulation platform and the proposed assessment method are applied to a multicopter system, where the effectiveness and practicability are verified by simulations and experiments., Comment: 8 pages, 10 figures

The number of Unmanned Aerial Vehicles (UAVs) continues to explode. Within the total spectrum of Unmanned Aircraft System (UAS) operations, Urban Air Mobility (UAM) is also on the way. Dense air traffic is getting ever closer to us. Current research either focuses on traffic network design and route design for safety purpose or swarm control in open airspace to contain large volume of UAVs. In order to achieve a tradeoff between safety and volumes of UAVs, a sky highway with its basic operation for Vertical Take-Off and Landing (VTOL) UAV is proposed, where traffic network, route and swarm control design are all considered. In the sky highway, each UAV will have its route, and an airway like a highway road can allow many UAVs to perform free flight simutaneously. The geometrical structure of the proposed sky highway with corresponding flight modes to support dense traffic is studied briefly one by one. The effectiveness of the proposed sky highway is shown by the given demonstration.

The U-shaped network is one of the end-to-end convolutional neural networks (CNNs). In electron microscope segmentation of ISBI challenge 2012, the concise architecture and outstanding performance of the U-shaped network are impressive. Then, a variety of segmentation models based on this architecture have been proposed for medical image segmentations. We present a comprehensive literature review of U-shaped networks applied to medical image segmentation tasks, focusing on the architectures, extended mechanisms and application areas in these studies. The aim of this survey is twofold. First, we report the different extended U-shaped networks, discuss main state-of-the-art extended mechanisms, including residual mechanism, dense mechanism, dilated mechanism, attention mechanism, multi-module mechanism, and ensemble mechanism, analyze their pros and cons. Second, this survey provides the overview of studies in main application areas of U-shaped networks, including brain tumor, stroke, white matter hyperintensities (WMHs), eye, cardiac, liver, musculoskeletal, skin cancer, and neuronal pathology. Finally, we summarize the current U-shaped networks, point out the open challenges and directions for future research.

Flight testbeds with multiple unmanned aerial vehicles (UAVs) are especially important to support research on multi-vehicle-related algorithms. The existing platforms usually lack a generic and complete solution allowing for software and hardware design. For such a purpose, this paper presents the design and implementation of a comprehensive multi-camera-based testbed for 3-D tracking and control of UAVs. First, the testbed software consists of a multi-camera system and a ground control system, which performs image processing, camera calibration, 3-D reconstruction, pose estimation, and motion control. In the multi-camera system, the positions and orientations of UAVs are first reconstructed by using epipolar geometric constraints and triangulation methods and then filtered by an extended Kalman filter (EKF). In the ground control system, a classical proportional–derivative (PD) controller is designed to receive the navigation data from the multi-camera system and then generates control commands to the target vehicles. Then, the testbed hardware employs smart cameras with field-programmable gate array (FPGA) modules to allow for real-time computation at a frame rate of 100 Hz. Lightweight quadcopter Parrot Bebop drones are chosen as the target UAVs, which does not require any modification to the hardware. Artificial infrared reflective markers are asymmetrically mounted on target vehicles and observed by multiple infrared cameras located around the flight region. Finally, extensive experiments are performed to demonstrate that the proposed testbed is a comprehensive and complete platform with good scalability applicable for research on a variety of advanced guidance, navigation, and control algorithms.

An adaptive smooth unsaturated bistable stochastic resonance (ASUBSR) system for bearing fault signal detection is established. Based on the problem of output saturation and poor low-frequency suppression performance of classical bistable stochastic resonance (CBSR) system, an SUBSR with unsaturated characteristics is proposed. An ASUBSR system is designed by extracting the envelope spectrum of the input signal and resampling it to satisfy the adiabatic approximation condition, combining high-pass filter to filter out low-frequency interference, and using genetic algorithm to select the optimal system parameters. Through simulations and experiments, we found that the system can effectively suppress the interference of low-frequency and high-frequency, indicates that the system performs like a band-pass filter, and the output signal-to-noise ratio is better than that of the CBSR system. The proposed ASUBSR system has great application in the field of fault detection of rolling bearings.

Malaria is one of the epidemics that can cause human death. Accurate and rapid diagnosis of malaria is important for treatment. Due to the limited number of data and human factors, the prediction performance and reliability of traditional classification methods are often affected. In this study, we propose an efficient and novel classification network named Attentive Dense Circular Net (ADCN) which based on Convolutional Neural Networks (CNN). The ADCN is inspired by the ideas of residual and dense networks and combines with the attention mechanism. We train and evaluate our proposed model on a publicly available red blood cells (RBC) dataset and compare ADCN with several well-established CNN models. Compared to other best performing CNN model in our experiments, ADCN shows superiority in all performance criteria such as accuracy (97.47% vs 94.61%), sensitivity (97.86% vs 95.20%) and specificity (97.07% vs 92.87%). Finally, we discuss the obtained results and analyze the difficulties of RBCs classification.

Designing a controller for the docking maneuver in Probe-Drogue Refueling (PDR) is an important but challenging task, due to the complex system model and the high precision requirement. In order to overcome the disadvantage of only feedback control, a feedforward control scheme known as Iterative Learning Control (ILC) is adopted in this paper. First, Additive State Decomposition (ASD) is used to address the tight coupling of input saturation, nonlinearity and the property of NonMinimum Phase (NMP) by separating these features into two subsystems (a primary system and a secondary system). After system decomposition, an adjoint-type ILC is applied to the Linear Time-Invariant (LTI) primary system with NMP to achieve entire output trajectory tracking, whereas state feedback is used to stabilize the secondary system with input saturation. The two controllers designed for the two subsystems can be combined to achieve the original control goal of the PDR system. Furthermore, to compensate for the receiver-independent uncertainties, a correction action is proposed by using the terminal docking error, which can lead to a smaller docking error at the docking moment. Simulation tests have been carried out to demonstrate the performance of the proposed control method, which has some advantages over the traditional derivative-type ILC and adjoint-type ILC in the docking control of PDR. Keywords: Additive state decomposition, Adjoint operator, Docking control, Iterative learning control, Probe-drogue refueling, Stable inversion

In order to handle undesirable failures of a multicopter which occur in either the pre-flight process or the in-flight process, a failsafe mechanism design method based on supervisory control theory is proposed for the semi-autonomous control mode. Failsafe mechanism is a control logic that guides what subsequent actions the multicopter should take, by taking account of real-time information from guidance, attitude control, diagnosis, and other low-level subsystems. In order to design a failsafe mechanism for multicopters, safety issues of multicopters are introduced. Then, user requirements including functional requirements and safety requirements are textually described, where function requirements determine a general multicopter plant, and safety requirements cover the failsafe measures dealing with the presented safety issues. In order to model the user requirements by discrete-event systems, several multicopter modes and events are defined. On this basis, the multicopter plant and control specifications are modeled by automata. Then, a supervisor is synthesized by monolithic supervisory control theory. In addition, we present three examples to demonstrate the potential blocking phenomenon due to inappropriate design of control specifications. Also, we discuss the meaning of correctness and the properties of the obtained supervisor. This makes the failsafe mechanism convincingly correct and effective. Finally, based on the obtained supervisory controller generated by TCT software, an implementation method suitable for multicopters is presented, in which the supervisory controller is transformed into decision-making codes., 33 pages, 15 figures

Object detection is an important topic for visual data processing in the visual computing area. Although a number of approaches have been studied, it still remains a challenge. There is a suitable way to promote image classifiers by blending training with blended images and corresponding blended labels. However, our experiments show that directly moving existing blending methods from classification to object detection will cause the training process become harder and eventually will lead to a bad performance. Inspired by our discovery, this paper presents a multi-phase blending method with incremental blending intensity to improve the accuracy of object detectors and achieve remarkable improvements. Firstly, to adapt blending method to detection task, we propose a smoothly scheduled and incremental blending intensity to control the degree of multi-phase blending. Based on the above dynamic coefficient, we propose an incremental blending method, in which the blending intensity is smoothly increased from zero to full. Therefore, more complex and various data can be created to achieve the goal of regularizing the network. Secondly, we also design an incremental hybrid loss function to replace the original loss function. The blending intensity in our loss function increases smoothly, which is controlled by our scheduled coefficient. Thirdly, we further discard more negative examples in our multi-phase training process than other typical training methods and processes. By doing so, we can regularize the neural network to enhance generalization capability with data diversity and eventually to improve the accuracy in object detection. Another advantage is that there is no negative effect on evaluation because our method is just applied during the training process. Typical experiments show the proposed method improves the generalization of the detection networks. On PASCAL VOC and MS COCO, our method outperforms the state-of-the-art RFBNet of one-stage detectors for real-time processing.

The deployment of robot swarms that perform a task cooperatively is attracting more and more attention in these years. There is a big challenge for existing methods to transfer information without direct communication. In this work, inspired by the phenomenon of bee swarm convening recruits to the food resource with waggle dance, we present a behavior-based approach to transfer information, using the UAV’s trajectory pattern rather than direct communication. Two trajectory patterns are designed to transfer information. Furthermore, both of them are optimized subject to the constraint on UAVs. We show that both patterns are feasible through simulation, and the 8-type performs better than the b-type under given indexes. Our information transfer strategy can be used for search and rescue in extreme environments and other communication-denied scenarios to meet the transmission needs of target position information.

In this paper, we propose and open a rapid development platform– –RflySim based on Pixhawk/PX4 and MATLAB/Simulink for UAV education and research. This platform adopts model-based development ideas and uses software-in-the-loop simulation and hardware-in-the-loop simulation to accelerate physical deployment. With that, beginners and developers can directly use MATLAB/Simulink to design low-level controllers (such as attitude control, position control) and high-level applications (such as decision-making, autonomous flight), and then deploy them into a multicopter autopilot system with no need to access the C/C++ underlying code. Three demonstrations are presented to verify the ease of use and the high efficiency of the proposed platform.

Currently, the time of endurance of electric multicopters is still too short for many mission requirements, and optimizing the efficiency of the propulsion system is considered as an effective way to overcome this problem. This paper proposes a practical method to help designers quickly select the optimal products of the propulsion system to maximize the multicopter efficiency under the desired flight condition. First, the modeling methods for the components of the propulsion system are studied respectively to describe the optimization problem mathematically. Second, methods are proposed to find optimal motor and propeller combination with the maximum thrust efficiency according to the given design requirements. Finally, factors that may affect the hovering time of multicopters are analyzed, and the optimal battery parameters are obtained for maximizing the multicopter endurance. Experiments and simulations are performed to demonstrate the effectiveness and practicability of the proposed method.

Autonomous Aerial Refueling (AAR) is vulnerable to various failures and involves cooperation among autonomous receivers, tankers and remote pilots. Dangerous flight maneuvers may be executed when unexpected failures or command conflicts happen. To solve this problem, a failsafe mechanism based on State Tree Structures (STS) is proposed. The failsafe mechanism is a control logic that guides what subsequent actions the autonomous receiver should take, by observing real-time information of internal low-level subsystems such as guidance and drogue&probe and external instructions from tankers and pilots. To generate such a controller using STS, the AAR procedure is decomposed into several modes, and safety issues related with seven low-level subsystems are summarized. Then common functional demands and safety requirements are textually described. On this basis, the AAR plants and specifications are modeled by STS, and a supervisor is synthesized to control the AAR model. To prove its feasibility and correctness, a simulation environment incorporating such a logic supervisor is built and tested. The design procedures presented in this paper can be used in decision-making strategies for similar flight tasks. Supporting materials can be downloaded in Github, [ https://github.com/KevinDong0810/Failsafe-Design-for-AAR-using-STS ] including related software, input documents and output files.

Feature selection is a critical preprocess for constructing model in computer vision and machine learning, yet it is difficult to simultaneously satisfy both reducing features’ number and maintaining classification accuracy. Toward this problem, we propose dividing-based many-objective evolutionary algorithm for large-scale feature selection (DMEA-FS). Firstly, four novel objectives are established for exploring the optimal feature’s subsets. Meanwhile, we design two structures of wrapper for high accuracy and filter for low computation cost in DMEA-FS. Secondly, two new recombination methods are presented for rapid convergence. Mapping-based variable dividing is presented for precise related variables. Thirdly, based on minimum Manhattan distance, a triangle-approximating decision-making is proposed for assisting users’ determination with/without preference information. Numerical experiments against several state-of-the-art feature selection algorithms demonstrate that the proposed DMEA-FS outperforms its competitors in terms of both classification accuracy and metrics of features’ number.

There exist at least two viewpoints on the internal model principle (IMP), namely the cancelation viewpoint and the geometrical viewpoint. However, neither of them is applicable to repetitive contr...

Microgrid (MG) usually operates in medium/low-voltage systems, where the line impedance parameters are mainly resistive, and traditional P–f / Q–U droop control is no longer applicable. When the virtual complex impedance method is adopted, the resistance component of line impedance can be counteracted by a virtual negative resistance. Unfortunately, the improper design of the virtual negative resistance will result in an unstable system due to the problem of line impedance parameter drift and estimation error. According to the line parameters characteristics of the off-grid MG with medium/low voltage, the P–U / Q–f droop control is adopted in this study, where the virtual complex impedance composed of a virtual negative inductance and a virtual resistance is introduced in the control loop. The virtual negative inductance is used to reduce the power coupling caused by the inductive component of the system impedance. The virtual resistance is implemented to enhance the resistive component and adjust the impedance matching degree for raising the accuracy of power sharing. However, the power sharing is still affected by the system hardware parameters; meanwhile, the voltage deviation caused by the droop control and the virtual impedance exists. In this study, a novel voltage stabilization and power sharing control method based on the virtual complex impedance is investigated to achieve accurate power sharing without the impact of hardware parameters variations and to improve the voltage quality. Moreover, the small-signal model of the inverter-based off-grid MG with the proposed controller is established, which can be utilized to analyze the stability and dynamic performance of the system. Meanwhile, the control parameters can be sequentially determined. Analysis shows that the strategy is robust against the line-impedance parameter drift and the estimation error and has a large stability margin and fast dynamic-response speed. Finally, numerical simulations and experimental results are provided to verify the effectiveness of the proposed control method in comparison with traditional frameworks.

Contraction mapping methods do not need to know about the concrete form of plant models like the Lyapunov method. This is the biggest advantage over other methods in the field of iterative learning control for example. However, it is difficult to use such a tool to analyze repetitive control systems. This paper proposes a contraction mapping method based on spectral theory to design a saturated repetitive controller for a class of nonlinear systems, where the derived necessary and sufficient condition on the spectral radius can reduce the conservatism as much as possible. The feasibility of our work is demonstrated through a robotic manipulator tracking example.

With a focus on the problems of active power sharing and voltage deviation of parallel-connected inverters in an islanded micro-grid (MG), in this study, the power-voltage droop controller and the inner voltage regulator are redesigned based on a total sliding-mode control (TSMC) technique. As for the power-voltage droop control loop, a droop control relation error between the active power and the point-of-common-coupling (PCC) voltage amplitude is defined. Then, the TSMC scheme is adopted to reach the new droop control relation, where the active power sharing and voltage amplitude recovery can be achieved simultaneously. Owing to the faster dynamic response and strong robustness provided by the TSMC framework, high-precision active power sharing during transient state also can be ensured without the influence of line impedances. The power allocation error can be improved by more than 81.2% and 50% than the conventional and proportional-integral (PI)-based droop control methods, respectively, and the voltage deviation rate can be reduced to 0.16%. Moreover, a small-signal model of the TSMC-based droop-controlled system is established, and the influences of control parameters on the system stability and the dynamic response are also investigated. The effectiveness of the proposed control method is verified by numerical simulations and experimental results.

Unmanned Aerial Vehicles (UAVs) are now becoming increasingly accessible to amateur and commercial users alike. An air traffic management (ATM) system is needed to help ensure that this newest entrant into the skies does not collide with others. In an ATM, airspace can be composed of airways, intersections and nodes. In this paper, for simplicity, distributed coordinating the motions of Vertical TakeOff and Landing (VTOL) UAVs to pass an airway is focused. This is formulated as a virtual tube passing problem, which includes passing a virtual tube, inter-agent collision avoidance and keeping within the virtual tube. Lyapunov-like functions are designed elaborately, and formal analysis based on invariant set theorem is made to show that all UAVs can pass the virtual tube without getting trapped, avoid collision and keep within the virtual tube. What is more, by the proposed distributed control, a VTOL UAV can keep away from another VTOL UAV or return back to the virtual tube as soon as possible, once it enters into the safety area of another or has a collision with the virtual tube during it is passing the virtual tube. Simulations and experiments are carried out to show the effectiveness of the proposed method and the comparison with other methods.

Unmanned Aerial Vehicles (UAVs) are now becoming increasingly accessible to amateur and commercial users alike. The main task for UAVs is to keep a prescribed separation with obstacles in the air. In this paper, a collision-avoidance control method for non-cooperative moving obstacles is proposed for a multicopter with the altitude hold mode by using a Lyapunov-like barrier function. Lyapunov-like functions are designed elaborately, based on which formal analysis and proofs of the proposed control are made to show that the collision-avoidance control problem can be solved if the moving obstacle is slower than the multicopter. The result can be extended to some cases of multiple obstacles. What is more, by the proposed control, a multicopter can keep away from obstacles as soon as possible, once obstacles enter into the safety area of the multicopter accidentally, and converge to the waypoint. Simulations and experiments are given to show the effectiveness of the proposed method by showing the distance between UAV and waypoint, obstacles respectively.

The success of deep learning methods relies on the availability of a large number of datasets with annotations; however, curating such datasets is burdensome, especially for medical images. To relieve such a burden for a landmark detection task, we explore the feasibility of using only a single annotated image and propose a novel framework named Cascade Comparing to Detect (CC2D) for one-shot landmark detection. CC2D consists of two stages: 1) Self-supervised learning (CC2D-SSL) and 2) Training with pseudo-labels (CC2D-TPL). CC2D-SSL captures the consistent anatomical information in a coarse-to-fine fashion by comparing the cascade feature representations and generates predictions on the training set. CC2D-TPL further improves the performance by training a new landmark detector with those predictions. The effectiveness of CC2D is evaluated on a widely-used public dataset of cephalometric landmark detection, which achieves a competitive detection accuracy of 86.25.01% within 4.0 mm, comparable to the state-of-the-art semi-supervised methods using a lot more than one training image. Our code is available at https://github.com/ICT-MIRACLE-lab/Oneshot_landmark_detection.

At CRYPTO’19, Gohr proposed a new cryptanalysis strategy based on the utilisation of machine learning algorithms. Using deep neural networks, he managed to build a neural based distinguisher that surprisingly surpassed state-of-the-art cryptanalysis efforts on one of the versions of the well studied NSA block cipher SPECK (this distinguisher could in turn be placed in a larger key recovery attack). While this work opens new possibilities for machine learning-aided cryptanalysis, it remains unclear how this distinguisher actually works and what information is the machine learning algorithm deducing. The attacker is left with a black-box that does not tell much about the nature of the possible weaknesses of the algorithm tested, while hope is thin as interpretability of deep neural networks is a well-known difficult task.

Unmanned Aerial Vehicle (UAV) has a rapid growth in recent years. An efficient air traffic system for UAVs working at a low altitude which is rich of obstacles is desired. However, as far as we know, the work on the generation of such an air traffic network at a low altitude lacks attention. It is expected to develop an algorithm in order to generate a traffic network automatically similar to the complex road traffic network by taking a matrix or image marked with obstacles as input. Considering those situations above, this paper addresses (i) automatic air traffic network generation that is suitable for UAVs working at a low altitude, (ii) several line segments (airlines) fitting for a curve, and (iii) a graph modeling. A simple experiment is also performed to show the effectiveness of the method proposed.

This paper presents a lifting-wing multirotor UAV that allows long-range flight. The UAV features a lifting wing in a special mounting angle that works together with rotors to supply lift when it flies forward, achieving a reduction in energy consumption and improvement of flight range compared to traditional multirotor UAVs. Its dynamic model is built according to the classical multirotor theory and the fixed-wing theory, as the aerodynamics of its multiple propellers and that of its lifting wing are almost decoupled. Its design takes into consideration aerodynamics, airframe configuration and the mounting angle. The performance of the UAV is verified by experiments, which show that the lifting wing saves 50.14% of the power when the UAV flies at the cruise speed (15m/s).

We investigate quantum teleportation in the two-copy setting based on GHZ measurement and propose the detailed protocol. The output state after the teleportation is analyzed, and the protocol is proved to be trace preserving. The general expression of the optimal teleportation fidelity is derived. The optimal teleportation fidelity is shown to be a linear function of two-copy fully entangled fraction, which is invariant under local unitary transformations. At last, we show two-copy teleportation based on GHZ measurement can be better than one-copy teleportation by an explicit example, which is amenable to demonstration in experiments. Our study is significant for improving the fidelity of teleportation for some limited resource which cannot be significantly distilled. Moreover, it can inspire us to find many other more efficient protocols for teleportation.

For most people on the ground, facing an unwanted drone buzzing around overhead, there is not a lot that we can do, especially if it is out of gun (radio wave gun or shotgun) range. A solution to this is to use intercept drones that seek out and bring down other drones. In order to make the interception autonomous, an image-based visual servo algorithm is designed with a forward-looking monocular camera. The control command, namely the angular velocity and thrust, is generated for intercept drones to implement accurate and fast interception. The proposed method is demonstrated in both hardware-in-the-loop simulation and demonstrative flight experiments.

The composition of a multicopter system is both simple and complex. This chapter aims to introduce the composition of multicopter systems and performance modeling of a propulsion system by using three step-by-step experiments from shallow to deep, namely a basic experiment, an analysis experiment, and a design experiment. In the basic experiment, readers can configure propulsion systems and understand the function of a propulsion system on flight performance. In the analysis experiment, readers can calculate the hover endurance, and analyze the hover endurance with respect to temperature, altitude, propeller size, and the number of propellers.

In Semi-Autonomous Control (SAC), a multicopter’s attitude stabilization or hover is realized by the autopilot, while position control is often manually realized by a remote pilot with a Radio Control (RC) transmitter. The SAC is based on the attitude controller and position controller designed in the previous chapters. This chapter will involve three modes (stabilize mode, altitude hold mode, loiter mode) of SAC of multicopters. Readers will gradually master this part of the knowledge through three step-by-step experiments, namely, basic, analysis and design experiments from shallow to deep. In the basic experiment, readers will repeat the simulation of the stabilize mode and observe its control performance. In the analysis experiment, readers will change the stabilize mode to altitude hold mode and then analyze their difference. Finally, in the design experiment, readers will further realize the loiter mode and design a state machine to switch among the three modes.

This chapter introduces the composition of the experimental platform released with this book and provides a thorough explanation of the role of each platform component. Examples with detailed experimental procedure are also presented in this chapter to familiarize readers with the basic functions and usage of the offered software and hardware, thereby laying a foundation for subsequent experiments to improve the learning efficiency.

The state estimation [13] is fundamental because it denotes the base for control and decision-making. This chapter introduces the principle and method of attitude estimation in detail via three step-by-step experiments from shallow to deep, namely a basic experiment, an analysis experiment, and a design experiment.

There are many sensors mounted in one multicopter. A multicopter can obtain its position and attitude using these sensors. However, these sensors, based on Micro-Electro-Mechanical System (MEMS), are inaccurate. This chapter will introduce the calibration principles and methods for some sensors in detail, with the hope that readers gradually master this part of the knowledge through three step-by-step experiments from shallow to deep, namely a basic experiment, an analysis experiment, and a design experiment.

For multicopters, several types of failure cannot be avoided, including communication breakdown, sensor failure, and propulsion system anomalies. These failures may cause the abortion of missions, multicopter crashes, and injuries or even death. To guarantee safety, the multicopter’s decision-making module should prevent or mitigate unsafe consequences of system failures.

This chapter is mainly for readers or experimental course teachers who need to deploy a code generation environment and prepare a platform for practical flight experiments. If the experimental platform has already been configured, readers can skip this chapter and directly start the experimental courses.

The objective of Chap. 8 is to estimate flight state accurately. In this chapter, it is assumed that the attitude of the multicopter has been estimated accurately. Feedback signals used in the controllers are the estimated values. However, for simplicity, true values are used instead of feedback according to the separation principle. The purpose of this chapter is to enable the multicopter to maintain a specific attitude. The performance of the attitude controller directly determines whether or not the multicopter can fly smoothly; further it is also the basis of position control, which will be discussed in Chap. 10. Control performance is determined by the rise time, overshoot, settling time, steady-state error in the time domain, and the cut-off frequency and stability margin in the frequency domain.

In this chapter, multicopter dynamic modeling is studied and realized by MATLAB/Simulink. A deep understanding of the multicopter dynamic model aids in understanding its motion and further designing filters and controllers. The aim of the chapter is to introduce the multicoper dynamic modeling via three step-by-step experiments from shallow to deep, namely a basic experiment, an analysis experiment, and a design experiment. In the basic experiment, readers can intuitively understand multicopters’ parameters on dynamic response during flight. In the analysis experiment, readers can analyze the dynamic response with respect to multicopter parameters during flight in theory. Finally, in the design experiment, readers can build a multicopter dynamic model in MATLAB and add a multicopter 3D model to FlightGear for visualization purposes.

The position control described in this chapter is concerned with set-point control, which is the basic issue in the trajectory tracking and path following problems. The experimental flow of set-point position controller design is similar to that of the attitude controller design. This chapter introduces the design principles and methods of constructing a position controller for multicopters. It is expected that readers can gradually master this part through three step-by-step experiments, namely basic, analysis and design experiments. In the basic experiment, readers will repeat the simulation of the position control of a multicopter to understand how a set-point position controller works. In the analysis experiment, readers will adjust the PID parameters of the set-point position controller so that the multicopter exhibits good control performance in the time domain. In the design experiment, readers will design compensators based on well-known principles of automatic control, allowing multicopters to achieve good control performance in the frequency domain.

In a probe–drogue aerial refueling system, the drogue is affected not only by wind disturbances but also by strong disturbances from the tanker vortex and receiver forebody bow wave. Along with the aerodynamic disturbances acting on the receiver aircraft, it is difficult for the probe to capture the moving drogue in the docking stage. This paper studies the model of the probe–drogue aerial refueling system under aerodynamic disturbances, and proposes docking control method based on iterative learning control to compensate for the docking errors caused by aerodynamic disturbances. For receiver aircraft with different maneuverability, three control strategies are proposed to achieve a trade-off between safety and control precision. Furthermore, a practical method is proposed to predict the initial value of the learning controllers, which can significantly improve the iterative learning speed of the proposed methods. Finally, simulations demonstrate that the proposed control methods are simple and efficient for the docking control of autonomous probe–drogue aerial refueling.

Velocity estimation is essential for multicopters to guarantee flight stability and maneuverability. For such a purpose, this paper proposes a new method for multicopter velocity estimation based on visual and inertial information in GPS-denied or confined environments. In this method, no map, artificial landmark of the environment is required, and only the off-the-shelf onboard sensors in a multicopter including a low-cost Inertial Measurement Unit (IMU), a downward-looking monocular camera and an ultrasonic range finder facing downwards are exploited to constitute the vision motion constraint. This constraint connects metric velocity with the point correspondences between successive images in which an efficient approach based on Mean Shift (MS) algorithm is developed to detect outliers and select optimal matching points. Then, it is theoretically verified that the estimation system is observable based on observability analysis. Furthermore, combined with the vision motion constraint and a multicopter dynamic model, the metric velocity is estimated using a standard Linear Kalman Filter (LKF). Finally, the proposed method is tested with a collection of synthetic data from simulation as well as flight experiments using real data from DJI Matrice 100 and Guidance. The simulation and experimental results indicate that the proposed method can accurately estimate the velocity of the multicopter in GPS-denied or confined environments.

We investigate two-copy scenario of quantum teleportation based on Bell measurements. The detailed protocol is presented and the general expression of the corresponding optimal teleportation delity is derived, which is given by the two-copy fully entangled fraction that is invariant under local unitary transformations. We prove that under a speci c case of the protocol, which is signi cant for improving the optimal delity, the set of states with their two-copy fully entangled fractions bounded by a threshold value that required for useful two-copy teleportation is convex and compact. Hence the witness operators exist to separate states that are useful for two-copy teleportation from the rest ones. Moreover, we show that the optimal delity of two-copy teleportation surpasses that of the original one copy teleportation., 8 pages, 2 figures

In the previous chapter, continuous-time filtered repetitive control (FRC, or filtered repetitive controller, which is also designated as FRC), was proposed using an additive-state-decomposition-based method for nonlinear systems. In this chapter, the sampled-data FRC is proposed for the same nonlinear system, but with a different problem under the additive-state-decomposition-based tracking control framework [1].

The internal model principle (IMP) was first proposed by Francis and Wonham [2, 3]. It states that if any exogenous signal can be regarded as the output of an autonomous system, then the inclusion of this signal model, namely, internal model, in a stable closed-loop system can assure asymptotic tracking or asymptotic rejection of the signal. Until now, to the best of the authors’ knowledge, there exist at least two viewpoints on IMP. In the early years, for linear time-invariant (LTI) systems, IMP implies that the internal model is to supply closed-loop transmission zeros which cancel the unstable poles of the disturbances and reference signals. This is called cancelation viewpoint here and only works for problems able to be formulated in terms of transfer functions. In the mid-1970s, Francis and Wonham proposed the geometric approach [4] to design an internal model controller [2, 3]. The purpose of internal models is to construct an invariant subspace for the closed-loop system and make the regulated output zero at each point of the invariant subspace. This is called geometrical viewpoint here.