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The purpose of this paper is to present μ-synthesis-based robust attitude trajectory tracking control of three degree-of-freedom four rotor hover vehicle.

Comprehensive modelling of hover vehicle is presented, followed by development of uncertainty model. A μ-synthesis-based controller is designed using the DK iteration method that not only handles structured and unstructured uncertainties effectively but also guarantees robust performance. The performance of the proposed controller is evaluated through simulations, and the controller is also implemented on experimental platform. Simulation and experimental results validate that μ-synthesis-based robust controller is found effective in: solving robust attitude trajectory tracking problem of multirotor vehicle systems, handling parameter variations and dealing with external disturbances.

Performance analysis of the proposed controller guarantees robust stability and also ensures robust trajectory tracking performance for nominal system and for 15-20 per cent variations in the system parameters. In addition, the results also ensure robust handling of wind gusts disturbances.

This research addresses the robust performance of hover vehicle’s attitude control subjected to uncertainties and external disturbances using μ-synthesis-based controller. This is the only method so far that guarantees robust stability and performance simultaneously.

The paper aims to report the development of an Unmanned Aerial Vehicle (UAV) Testbed Training Platform (TTP). The development is to enable users to safely fly and control the UAV in real time within a limited (yet unconstrained) virtually created environment. Thus, the paper introduces a hardware–virtual environment coupling concept, the Panda3D gaming engine utilization to develop the graphical user interface (GUI) and the 3D-flying environment, as well as the interfacing electronics that enables tracking, monitoring and mapping of real-time movement onto the virtual domain and vice verse.

The platform comprises a spring-shuttle assembly fixed to a heavy aluminium base. The spring supports a rotating platform (RP), which is intended to support UAVs. The RP yaw, pitch and roll are measured by an inertial measurement unit, its climb/descend is measured by a low cost infrared proximity sensor and its rotation is measured by a rotary optical encoder. The hardware is coupled to a virtual environment (VE), which was developed using the Panda3D gaming engine. The VE includes a GUI to generate, edit, load and save real-life environments. Hardware manoeuvres are reflected into the VE.

The prototype was proven effective in dynamically mapping and tracking the rotating platform movements in the virtual environment. This should not be confused with the hardware in loop approach, which requires the inclusion of a mathematical model of the hardware in a loop. The finding will provide future means of testing navigation and tracking algorithms.

The work is still new, and there is great room for improvement in many aspects. Here, this paper reports the concept and its technical implementation only.

In the literature, various testbeds were reported, and it is felt that there is still room to come up with a better design that enables UAV flying in safer and unlimited environments. This has many practical implications, particularly in testing control and navigation algorithms in hazardous fields.

The main social impact is to utilise the concept to develop systems that are capable of autonomous rescue mission navigation in disaster zones.

The authors are aware that various researchers have developed various testbeds, at different degrees of freedom. Similarly, the authors are also aware that researchers have used game engines to simulate mobile robots or sophisticated equipment (like the VICON Motion Capture System) to measure to perform complex manoeuvres. However, the cost of this kind of equipment is very high, autonomous movements are planned in restricted environments and tested systems are only autonomous in certain setups. However, the idea of mapping the dynamics of an avatar flying object onto a 3D-VE is novel. To improve productivity and rapid prototyping, this paper proposes the use of commercially available game engines, such as the Panda3D, to create virtual environments.

The purpose of this paper is to facilitate autonomous landing of a multi-rotor unmanned aerial vehicle (UAV) on a moving/tilting platform using a robust vision-based approach.

Autonomous landing of a multi-rotor UAV on a moving or tilting platform of unknown orientation in a GPS-denied and vision-compromised environment presents a challenge to common autopilot systems. The paper proposes a robust visual data processing system based on targets’ Oriented FAST and Rotated BRIEF features to estimate the UAV’s three-dimensional pose in real time.

The system is able to visually locate and identify the unique landing platform based on a cooperative marker with an error rate of 1° or less for all roll, pitch and yaw angles.

The proposed vision-based system aims at on-board use and increased reliability without a significant change to the computational load of the UAV.

The simplicity of the training procedure gives the process the flexibility needed to use a marker of any unknown/irregular shape or dimension. The process can be easily tweaked to respond to different cooperative markers. The on-board computationally inexpensive process can be added to off-the-shelf autopilots.

This paper aims to tele-operate the movement of an unmanned aerial vehicle (UAV) in the obstructed environment with asymmetric time-varying delays. A simple passive proportional velocity errors plus damping injection (P-like) controller is proposed to deal with the asymmetric time-varying delays in the aerial teleoperation system.

This paper presents both theoretical and real-time experimental results of the bilateral teleoperation system of a UAV for collision avoidance over the wireless network. First, a position-velocity workspace mapping is used to solve the master-slave kinematic/dynamic dissimilarity. Second, a P-like controller is proposed to ensure the stability of the time-delayed bilateral teleoperation system with asymmetric time-varying delays. The stability is analyzed by the Lyapunov–Krasovskii function and the delay-dependent stability criteria are obtained under linear-matrix-inequalities conditions. Third, a vision-based localization is presented to calibrate the UAV’s pose and provide the relative distance for obstacle avoidance with a high accuracy. Finally, the performance of the teleoperation scheme is evaluated by both human-in-the-loop simulations and real-time experiments where a single UAV flies through the obstructed environment.

Experimental results demonstrate that the teleoperation system can maintain passivity and collision avoidance can be achieved with a high accuracy for asymmetric time-varying delays. Moreover, the operator could tele-sense the force reflection to improve the maneuverability in the aerial teleoperation.

A real-time bilateral teleoperation system of a UAV for collision avoidance is performed in the laboratory. A force and visual interface is designed to provide force and visual feedback of the slave environment to the operator.

The purpose of this paper is to develop, extend and propose an improved proportional integral derivative (PID) rate control of a quadrotor unmanned aerial vehicle based on a convexity-based surrogated firefly algorithm.

An improved PID controller structure is proposed for the rate dynamics of the quadrotor. Optimality of the controller is ensured by a recent, simple yet efficient firefly optimization method. The hybrid structure is further enhanced with a convexity-based surrogated model function.

Monte Carlo, transient response, error metrics and histogram distribution analyzes are conducted to show the performance of the proposed controller. The performance of the proposed method is evaluated under various convex combination values to further investigate the effect of the proposed surrogated model. According to the results, the proposed method is capable of controlling the rate quadrotor dynamics with the steady-state error of 0.0023 (rad/s) for P, −0.0024 (rad/s) for Q and 0 (rad/s) for the R state, respectively. Also, the least mean objective value is achieved at = 0 value of convexity in Monte Carlo trials.

The originality of this paper is to propose an improved PID rate controller with a convexity-based surrogated firefly algorithm.

This paper aims to study a neural network-based fault-tolerant controller to improve the tracking control performance of an unmanned autonomous helicopter with system uncertainty, external disturbances and sensor faults, using the prescribed performance method.

To ensure that the tracking error satisfies the prescribed performance, the authors adopt an error transformation function method. A control scheme based on the neural network and high-order disturbance observer is designed to guarantee the boundedness of the closed-loop system. A simulation is performed to prove the validity of the control scheme.

The developed adaptive fault-tolerant control method makes the system with sensor fault realize tracking control. The error transformation function method can effectively handle the prescribed performance requirements. Sensor fault can be regarded as a type of system uncertainty. The uncertainty can be approximated accurately using neural networks. A high-order disturbance observer can effectively suppress compound disturbances.

The tracking performance requirements of unmanned autonomous helicopter system are considered in the design of sensor fault-tolerant control. The inequality constraint that the output tracking error must satisfy is transformed into an unconstrained problem by introducing an error transformation function. The fault state of the velocity sensor is considered as the system uncertainty, and a neural network is used to approach the total uncertainty. Neural network estimation errors and external disturbances are treated as compound disturbances, and a high-order disturbance observer is constructed to compensate for them.

The objective of this research is to investigate various neural network (NN) observer techniques for sensors fault identification and diagnosis of nonlinear system in consideration of numerous faults, failures, uncertainties and disturbances. For the importunity of increasing the faults diagnosis and reconstruction preciseness, a new technique is used for modifying the weight parameters of NNs without enhancement of computational complexities.

Various techniques such as adaptive radial basis functions (ARBF), conventional radial basis functions, adaptive multi-layer perceptron, conventional multi-layer perceptron and extended state observer are presented. For increasing the fault detection preciseness, a new technique is used for updating the weight parameters of radial basis functions and multi-layer perceptron (MLP) without enhancement of computational complexities. Lyapunov stability theory and sliding-mode surface concepts are used for the weight-updating parameters. Based on the combination of these two concepts, the weight parameters of NNs are updated adaptively. The key purpose of utilization of adaptive weight is to enhance the detection of faults with high accuracy. Because of the online adaptation, the ARBF can detect various kinds of faults and failures such as simultaneous, incipient, intermittent and abrupt faults effectively. Results depict that the suggested algorithm (ARBF) demonstrates more confrontation to unknown disturbances, faults and system dynamics compared with other investigated techniques and techniques used in the literature. The proposed algorithms are investigated by the utilization of quadrotor unmanned aerial vehicle dynamics, which authenticate the efficiency of the suggested algorithm.

The proposed Lyapunov function theory and sliding-mode surface-based strategy are studied, which shows more efficiency to unknown faults, failures, uncertainties and disturbances compared with conventional approaches as well as techniques used in the literature.

For improvement of the system safety and for avoiding failure and damage, the rapid fault detection and isolation has a great significance; the proposed approaches in this research work guarantee the detection and reconstruction of unknown faults, which has a great significance for practical life.

In this research, two strategies such Lyapunov function theory and sliding-mode surface concept are used in combination for tuning the weight parameters of NNs adaptively. The main purpose of these strategies is the fault diagnosis and reconstruction with high accuracy in terms of shape as well as the magnitude of unknown faults. Results depict that the proposed strategy is more effective compared with techniques used in the literature.

The purpose of this paper is to propose a novel target automatic recognition method for unmanned aerial vehicle (UAV), which is based on backpropagation – artificial neural network (BP-ANN) algorithm, with the objective of optimizing the structure of backpropagation network, to increase the efficiency and decrease the recognition time. A hardware-in-the-loop system for UAV target automatic recognition is also developed.

The hybrid model of BP-ANN structure is established for aircraft automatic target recognition. This proposed method identifies controller parameters and reduces the computational complexity. Approaching speed of the network is faster and recognition accuracy is higher. This kind of network combines or better fuses the advantages of backpropagation artificial neural algorithm and Hu moment. with advantages of two networks and improves the speed and accuracy of identification. Finally, a hardware-in-the-loop system for UAV target automatic recognition is also developed.

The double hidden level backpropagation artificial neural can easily increase the speed of recognition process and get a good performance for recognition accuracy.

The proposed backpropagation artificial neural algorithm can be ANN easily applied to practice and can help the design of the aircraft automatic target recognition system. The standard backpropagation algorithm has some obvious drawbacks, namely, converging slowly and falling into the local minimum point easily. In this paper, an improved algorithm based on the standard backpropagation algorithm is constructed to make the aircraft target recognition more practicable.

A double hidden levels backpropagation artificial neural algorithm is presented for automatic target recognition system of UAV.