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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.

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.

The purpose of this paper is to make performance improvements and timely critical execution enhancements for operational flight program (OFP). The OFP is core software of autonomous control system of small unmanned helicopter.

In order to meet the time constraints and enhance control application performance, two major improvements were done at real‐time operating system (RTOS) kernel. They are thread scheduling algorithm and lock‐free thread message communication mechanism. Both of them have a direct relationship with system efficiency and indirect relationship with helicopter control application execution stability through improved deadline keeping characteristics.

In this paper, the suitability of earliest deadline first (EDF) scheduling algorithm and non‐blocking buffer (NBB) mechanism are illustrated with experimental and practical applications. Results of this work show that EDF contributes around 15 per cent finer‐timely execution and NBB enhances kernel's responsiveness around 35 per cent with respect to the number of thread context switch and CPU utilization. These apply for OFP implemented over embedded configurable operating system (eCos) RTOS on x86 architecture‐based board.

This paper illustrates an applicability of deadline‐based real‐time scheduling algorithm and lock‐free kernel communication mechanism for performance enhancement and timely critical execution of autonomous unmanned aerial vehicle control system.

This paper illustrates a novel approach to extend RTOS kernel modules based on unmanned aerial vehicle control application execution scenario. Lock‐free thread communication mechanism is implemented, and tested for applicability at RTOS. Relationship between UAV physical and computation modules are clearly illustrated via an appropriate unified modelling language (UML) collaboration and state diagrams. As experimental tests are conducted not only for a particular application, but also for various producer/consumer scenarios, these adequately demonstrate the applicability of extended kernel modules for general use.

In the previous decade, unmanned aerial vehicles (UAVs) have turned into a subject of enthusiasm for some exploration associations. UAVs are discovering applications in different regions going from military applications to activity reconnaissance. The purpose of this paper is to overview a particular sort of UAV called quadrotor or quadcopter.

This paper includes the dynamic models of a quadrotor and the distinctive model-reliant and model-autonomous control systems and their correlation.

In the present time, focus has moved to outlining autonomous quadrotors. Ultimately, the paper examines the potential applications of quadrotors and their part in multi-operators frameworks.

This investigation deals with the review on various quadrotors, their applications and motion control strategies.

The purpose of this paper is to develop a multiobjective differential evolution (MODE)-based extended H-infinity controller for autonomous helicopter control.

Development of a MATLAB-based MODE suitable for controller synthesis. Formulate the H-infinity control scheme as an extended H-infinity loop shaping design procedure (H _∞ -LSDP) with incorporation of v-gap metric for robustness to parametric variation. Then apply the MODE-based algorithm to optimize the weighting function of the control problem formulation for optimal performance.

The proposed optimized H-infinity control was able to yield set of Pareto-controller candidates with optimal compromise between conflicting stability and time-domain performances required in autonomous helicopter deployment. The result of performance evaluation shows robustness to parameter variation of up to 20 per cent variation in nominal values, and in addition provides satisfactory disturbance rejection to wind disturbance in all the three axes.

The formulated H-infinity controller is limited to hovering and low speed flight envelope. The optimization is focused on weighting function parameters for a given fixed weighting function structure. This thus requires a priori selection of weighting structures.

The proposed MODE-infinity controller algorithm is expected to ease the design and deployment of the robust controller in autonomous helicopter application especially for practicing engineer with little experience in advance control parameters tuning. Also, it is expected to reduce the design cycle involved in autonomous helicopter development. In addition, the synthesized robust controller will provide effective hovering/low speed autonomous helicopter flight control required in many civilian unmanned aerial vehicle (UAV) applications.

The research will facilitate the deployment of low-cost, small-scale autonomous helicopter in various civilian applications.

The research addresses the challenges involved in selection of weighting function parameters for H-infinity control synthesis to satisfy conflicting stability and time-domain objectives. The problem of population initialization and objectives function computation in the conventional MODE algorithm are addressed to ensure suitability of the optimization algorithm in the formulated H-infinity controller synthesis.

The purpose of this paper is to build nonlinear model of a small rotorcraft‐based unmanned aerial vehicles (RUAV), using nonlinear system identification method to estimate the parameters of the model. The nonlinear model will be used in robust control system design and aerodynamic analysis.

The nonlinear model is built based on mechanism theory, aerodynamics and mechanics, which can reflect most dynamics in large flight envelop. Genetic algorithm (GA) and time domain flight data is adopted to estimate unknown parameters of the model. The flight data were collected from a series of fight tests. The identification results were also analyzed and validated.

The nonlinear model of RUAV has better accuracy, the parameters are physical quantities, and having distinctly recognizable values. The GA is suitable for nonlinear system identification. And the results proved the identified model can reflect the dynamic characteristics in extensive area of flight envelop.

The GA requires much more computing power, to identify 12 unknown parameters with 30 iterations, will takes more than 18 hours of a four cores desktop computer. Because of this is an off‐line identification process, and has more accuracy, extra time is acceptable.

GA method has significantly increased the accuracy of the model. The previous work of system identification used a ten states linear model, and using PEM identified 23 coefficients. By carefully building the nonlinear model, it has only 21 unknown parameters, but if the model is linearized, it will get a linear model more than 35 states, which shows nonlinear model contain more dynamics than linear model.

This paper aims to propose output feedback-based control algorithms for the flight control system of a scaled, un-crewed helicopter in its hover flight mode.

The proposed control schemes are based on H_∞ control and composite nonlinear control. The gains of the output feedback controllers are obtained as the solution of a set of linear matrix inequalities (LMIs).

In the proposed schemes, the finite-time convergence of system states to trim condition is achieved with minimum deviation from the steady-state. As the proposed composite nonlinear output feedback design improves the transient response, it is well suited for a scaled helicopter flight. The use of measured output vector instead of the state vector or its estimate for feedback provides a simple control structure and eliminates the need for an observer in real-time application. The proposed control strategies are relevant to situations in which a simple controller is essential due to economic factors, reliability and hardware implementation constraints.

The proposed control strategies are relevant to situations in which a simple controller is essential due to economic factors, reliability and hardware implementation constraints. They also have significance in applications where the number of measurement quantities needs to be minimized such as in a fully functional rotor-craft unmanned aerial vehicle.

The developed output feedback control algorithms can be used in small-scale helicopters for numerous civilian and military applications.

This work addresses the LMI-based formulation and solution of an output feedback controller for a hovering un-crewed helicopter. The stability and robustness of the closed-loop system are proved mathematically and the performance of the proposed schemes is compared with an existing strategy via simulation studies.

This paper aims to address three major issues in the development of a vision‐based navigation system for small unmanned aerial vehicles (UAVs) which can be characterized as follows: technical constraints, robust image feature matching and an efficient and precise method for visual navigation.

The authors present and evaluate methods for their solution such as wireless networked control, highly distinctive feature descriptors (HDF) and a visual odometry system.

Proposed feature descriptors achieve significant improvements in computation time by detaching the explicit scale invariance of the widely used scale invariant feature transform. The feasibility of wireless networked real‐time control for vision‐based navigation is evaluated in terms of latency and data throughput. The visual odometry system uses a single camera to reconstruct the camera path and the structure of the environment, and achieved and error of 1.65 percent w.r.t total path length on a circular trajectory of 9.43 m.

The originality/value lies in the contribution of the presented work to the solution of visual odometry for small unmanned aerial vehicles.

The purpose of this paper is to investigate the stability of a small scale six-degree-of-freedom nonlinear helicopter model at translator velocities and angular displacements while it is transiting to hover with different initial conditions.

In this study, model predictive controller and linear quadratic regulator are designed and compared within each other for the stabilization of the open loop unstable nonlinear helicopter model.

This study shows that the helicopter is able to reach to the desired target with good robustness, low control effort and small steady-state error under disturbances such as parameter uncertainties, mistuned controller.

The purpose of using model predictive control for three axes of the autopilot is to decrease the control effort and to make the close-loop system insensitive against modeling uncertainties.