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The purpose of this paper is to introduce an improved system identification method for small unmanned helicopters combining adaptive ant colony optimization algorithm and Levy’s method and to solve the problem of low model prediction accuracy caused by low-frequency domain curve fitting in the small unmanned helicopter frequency domain parameter identification method.

This method uses the Levy method to obtain the initial parameters of the fitting model, uses the global optimization characteristics of the adaptive ant colony algorithm and the advantages of avoiding the “premature” phenomenon to optimize the initial parameters and finally obtains a small unmanned helicopter through computational optimization Kinetic models under lateral channel and longitudinal channel.

The algorithm is verified by flight test data. The verification results show that the established dynamic model has high identification accuracy and can accurately reflect the dynamic characteristics of small unmanned helicopter flight.

This paper presents a novel and improved frequency domain identification method for small unmanned helicopters. Compared with the conventional method, this method improves the identification accuracy and reduces the identification error.

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 purpose of this paper is to develop a hybrid algorithm using differential evolution (DE) and prediction error modeling (PEM) for identification of small-scale autonomous helicopter state-space model.

In this study, flight data were collected and analyzed; MATLAB-based system identification algorithm was developed using DE and PEM; parameterized state-space model parameters were estimated using the developed algorithm and model dynamic analysis.

The proposed hybrid algorithm improves the performance of the PEM algorithm in the identification of an autonomous helicopter model. It gives better results when compared with conventional PEM algorithm inside MATLAB toolboxes.

This study is applicable to only linearized state-space model.

The identification algorithm is expected to facilitate the required model development for model-based control design for autonomous helicopter development.

This study presents a novel hybrid algorithm for system identification of an autonomous helicopter model.

The purpose of this paper is to find a solution for the unmanned aerial vehicle (UAV) rendezvous problem, which should be feasible, optimal and not time consuming. In the existing literatures, the UAV rendezvous problem is always presented as a matter of simultaneous arrival. They focus only on the time consistency. However, the arrival time of UAVs can vary according to the rendezvous position. The authors should determine the best rendezvous position with considering UAVs’ maneuver constraint, so that UAVs can construct a formation in a short time.

The authors present a decentralized method in which UAVs negotiate with each other for the best rendezvous positions by using Nash bargain. The authors analyzed the constraints of the rendezvous time and the UAV maneuver, and proposed an objective function that allows UAVs to get to their rendezvous positions as fast as possible. Bezier curve is adopted to generate smooth and feasible flight trajectories. During the rendezvous process, UAVs adjust their speed so that they can arrive at the rendezvous positions simultaneously.

The effectiveness of the proposed method is verified by simulation experiments. The proposed method can successfully and efficiently solve the UAV rendezvous problem.

As far as the authors know, it is the first time Nash bargain is used in the UAV rendezvous problem. The authors modified the Nash bargain method and make it distributed, so that it can be computed easily. The proposed method is much less consuming than ordinary Nash bargain method and ordinary swarm intelligence based methods. It also considers the UAV maneuver constraint, and can be applied online for its fast calculation speed. Simulations demonstrate the effectiveness of the proposed method.

The purpose of this study is to design a flight control model for a control surface-less (CSL) tri-tilt-rotor (TTR) unmanned aerial vehicle (UAV) based on a Proportional Integral Derivative (PID) controller to stabilize the altitude and attitude of the UAV subjected to various flying conditions.

First, the proposed UAV with a tilting mechanism is designed and analyzed to obtain the aerodynamic parameters. Second, the dynamics of the proposed UAV are mathematically modeled using Newton-Euler formation. Then, the PID controller is implemented in the simulation model to control flight maneuvers. The model parameters were implemented in a mathematical model to find the system’s stability for various flight conditions. The model was linearized to determine the PID gain values for vertical take-off and landing, cruise and transition mode. The PID controller was tuned to obtain the desired altitude and attitude in a short period. The tuned PID gain values were implemented in the PID controller and the model was simulated.

The main contribution of this study is the mathematical model and controller for a UAV without any control surface and uses only a thrust vector control mechanism which reduces the complexity of the controller. The simulation has been carried out for various flight conditions. The altitude PID controller and the attitude PID controller for CSL-TTR-UAV were tuned to obtain desired altitude and attitude within the optimum duration of 4 s and deviation in the attitude of 8%, which is within the allowable limit of 14%. The findings obtained from the simulation revels that the altitude and attitude control of the CSL-TTR-UAV was achieved by controlling the rpm of the rotor and tilt angle using the PID controller.

A novel CSL TTR UAV mathematical model is developed with a dual tilting mechanism for a tail rotor and single axis tilt for the rotors in the wing. The flight control model controls the UAV without a control surface using a PID controller for the thrust vector mechanism.

The purpose of this paper is to study the application of entropy based optimized fuzzy logic control for a real-time non-linear system. Optimization of the fuzzy membership function (MF) is one of the most explored areas for performance improvement of the fuzzy logic controllers (FLC). Conversely, majority of previous works are motivated on choosing an optimized shape for the MF, while on the other hand the support of fuzzy set is not accounted.

The proposed investigation provides the optimal support for predefined MFs by using genetic algorithms-based optimization of fuzzy entropy-based objective function.

The experimental results obtained indicate an improvement in the performance of the controller which includes improvement in error indices, transient and steady-state parameters. The applicability of proposed algorithm has been verified through real-time control of the twin rotor multiple-input, multiple-output system (TRMS).

The proposed algorithm has been used for the optimization of triangular sets, and can also be used for the optimization of other fussy sets, such as Gaussian, s-function, etc.

The proposed optimization can be combined with other algorithms which optimize the mathematical function (shape), and a potent optimization tool for designing of the FLC can be formulated.

This paper presents the application of a new optimized FLC which is tested for control of pitch and yaw angles in a TRMS. The performance of the proposed optimized FLC shows significant improvement when compared with standard references.

The purpose of this paper is to describe imperialist competitive algorithm (ICA), a novel socio-politically inspired optimization strategy for proposing a fuzzy variant of this algorithm. ICA is a meta-heuristic algorithm for dealing with different optimization tasks. The basis of the algorithm is inspired by imperialistic competition. It attempts to present the social policy of imperialisms (referred to empires) to control more countries (referred to colonies) and use their sources. If one empire loses its power, among the others making a competition to take possession of it.

In fuzzy imperialist competitive algorithm (FICA), the colonies have a degree of belonging to their imperialists and the top imperialist, as in fuzzy logic, rather than belonging completely to just one empire therefore the colonies move toward the superior empire and their relevant empires. Simultaneously for balancing the exploration and exploitation abilities of the ICA. The algorithms are used for optimization have shortcoming to deal with accuracy rate and local optimum trap and they need complex tuning procedures. FICA is proposed a way for optimizing convex function with high accuracy and avoiding to trap in local optima rather than using original ICA algorithm by implementing fuzzy logic on it.

Therefore several solution procedures, including ICA, FICA, genetic algorithm, particle swarm optimization, tabu search and simulated annealing optimization algorithm are considered. Finally numerical experiments are carried out to evaluate the effectiveness of models as well as solution procedures. Test results present the suitability of the proposed fuzzy ICA for convex functions with little fluctuations.

The proposed evolutionary algorithm, FICA, can be used in diverse areas of optimization problems where convex functions properties are appeared including, industrial planning, resource allocation, scheduling, decision making, pattern recognition and machine learning (optimization techniques; fuzzy logic; convex functions).

The purpose of this paper is to propose a control approach for small unmanned helicopters, and a novel swarm intelligence algorithm is used to optimize the parameters of the proposed controller.

Small unmanned helicopters have many advantages over other unmanned aerial vehicles. However, the manual operation process is difficult because the model is always instable and coupling. In this paper, a novel optimized active disturbance rejection control (ADRC) approach is presented for small unmanned helicopters. First, a linear attitude model is built in hovering condition according to small perturbation linearization. To realize decoupling, this model is divided into two parts, and each part is equipped with an ADRC controller. Finally, a novel Levy flight-based pigeon-inspired optimization (LFPIO) algorithm is developed to find the optimal ADRC parameters and enhance the performance of controller.

This paper applies ADRC method to the attitude control of small unmanned helicopters so that it can be implemented in practical flight under complex environments. Besides, a novel LFPIO algorithm is proposed to optimize the parameters of ADRC and is proved to be more efficient than other homogenous methods.

The model of proposed controller is built in the hovering action, whereas it cannot be used in other flight modes.

The optimized ADRC method can be implemented in actual flight, and the proposed LFPIO algorithm can be developed in other practical optimization problems.

ADRC method can enhance the response and robustness of unmanned helicopters which make it valuable in actual environments. The proposed LFPIO algorithm is proved to be an effective swarm intelligence optimizer, and it is convenient and valuable to apply it in other optimized systems.

The purpose of this paper is to use an ABC algorithm to improve the thrust–torque ratio of a rotating-wing unmanned aerial vehicle (UAV) model.

The design of UAVs, such as aircraft, drones, helicopters, has become one of the popular engineering areas with the development of technology. This study aims to improve the value of thrust–torque ratio of an unmanned helicopter. For this purpose, an unmanned helicopter was built at the Faculty of Aeronautics and Astronautics, Erciyes University. The maximum thrust–torque ratio was calculated considering the blade length, blade chord width, blade mass density and blade twist angle. For calculation, artificial bee colony (ABC) algorithm was used. By using ABC algorithm, the maximum thrust–torque ratio was obtained against the optimum input values. For this purpose, a model with four inputs and a single output is formed. In the generated system model, optimum thrust–torque ratio was calculated by changing the input values used in the ±5% range. As a result of this study, approximately 31% improvement was achieved. According to these results, the proposed approach will provide convenience to the designers in the design of the rotating-wing UAV.

According to these results, approximately 31% improvement was achieved, and the proposed approach will provide convenience to the designers in the design of the rotating-wing UAV.

It takes a long time to obtain the optimum thrust–torque ratio value through the ABC algorithm method.

Using ABC algorithm provides to improve the value of thrust–torque ratio of an unmanned helicopter. With this algorithm, unmanned helicopter flies more than ever. Thus, the presented method based on the ABC algorithm is more efficient.

The application of the ABC algorithm method can be used effectively to calculate the thrust–torque ratio in UAV.

Providing an original and penetrating a method that saves time and reduces the cost to improve the value of thrust–torque ratio of an unmanned helicopter.