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This research study aims to minimize autonomous flight cost and maximize autonomous flight performance of a slung load carrying rotary wing mini unmanned aerial vehicle (i.e. UAV) by stochastically optimizing autonomous flight control system (AFCS) parameters. For minimizing autonomous flight cost and maximizing autonomous flight performance, a stochastic design approach is benefitted over certain parameters (i.e. gains of longitudinal PID controller of a hierarchical autopilot system) meanwhile lower and upper constraints exist on these design parameters.

A rotary wing mini UAV is produced in drone Laboratory of Iskenderun Technical University. This rotary wing UAV has three blades main rotor, fuselage, landing gear and tail rotor. It is also able to carry slung loads. AFCS variables (i.e. gains of longitudinal PID controller of hierarchical autopilot system) are stochastically optimized to minimize autonomous flight cost capturing rise time, settling time and overshoot during longitudinal flight and to maximize autonomous flight performance. Found outcomes are applied during composing rotary wing mini UAV autonomous flight simulations.

By using stochastic optimization of AFCS for rotary wing mini UAVs carrying slung loads over previously mentioned gains longitudinal PID controller when there are lower and upper constraints on these variables, a high autonomous performance having rotary wing mini UAV is obtained.

Approval of Directorate General of Civil Aviation in Republic of Türkiye is essential for real-time rotary wing mini UAV autonomous flights.

Stochastic optimization of AFCS for rotary wing mini UAVs carrying slung loads is properly valuable for recovering autonomous flight performance cost of any rotary wing mini UAV.

Establishing a novel procedure for improving autonomous flight performance cost of a rotary wing mini UAV carrying slung loads and introducing a new process performing stochastic optimization of AFCS for rotary wing mini UAVs carrying slung loads meanwhile there exists upper and lower bounds on design variables.

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.

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 introduce a new danger‐aware Operational Flight Program (OFP) for the unmanned helicopter's auto‐navigation based on the well‐known time‐triggered message‐triggered object (TMO) model.

In this design with the TMO, the danger‐awareness means two things. First, an unmanned helicopter maneuvers on safe altitudes to avoid buildings or mountains when navigating to the target position. It is assumed that minimum safe altitudes are given on evenly spaced grids and on the center points of every four adjacent grids. A three‐dimensional (3D) path‐finding algorithm using this safe‐altitude information is proposed. Second, a helicopter automatically avoids a zone with very high temperature caused by a fire.

Since the auto‐flight control system requires componentized real‐time processing of sensors and controllers, the TMO model that has periodic and sporadic threads as members, has been used in designing the OFP. It has been found that using the TMO scheme is a way to construct a very flexible, well‐componentized and timeliness‐guaranteed OFP.

As the RTOS, RT‐eCos has been used. It was developed a few years ago based on the eCos3.0 to support the real‐time thread model of the TMO scheme. To verify this navigation system, a hardware‐in‐the‐loop simulation (HILS) system also has been developed.

Designing an OFP by using the real‐time object model TMO and the proposed 3D safe path finding algorithm is a whole new effective deadline‐based approach. And the developed OFP can be used intensively in the phase of disaster response and recovery.

The purpose of this paper is to investigate the feasibility of controlling a small‐scale helicopter by using the model predictive control (MPC) method.

The MPC control synthesis is employed by considering five linear models representing the flight of a small‐scale helicopter from hover to high‐speed cruise. The internal model principle is employed for the trajectory tracking design.

It is found that the MPC handles well the transition problems between the models, yields satisfactory tracking control performance and produces a suitable control signal. The performance of the tracking control of the helicopter is considerably influenced by the parameter selection in the states and inputs weighting matrices of the MPC. Simulation results also showed that faster dynamics, coupling problems, input and output constraints and changing linearized multi‐inputs multi‐outputs dynamics models in the small‐scale helicopter can be handled simultaneously by the MPC controller.

The present study is limited for the application of MPC for the control of small‐scale helicopters with non‐aggressive maneuvers.

The result can be extended to design a full envelope controller for an autonomous small‐scale helicopter without the need to resort to a conventional gain scheduling technique.

Helicopter control system designs using MPC with a single either linear or non‐linear model have been studied and reported in numerous literatures. The main contribution of the paper is in the application of MPC to handle the control problems of a small‐scale helicopter defined as a mathematical model with several different modes during a flight mission.

This paper aims to study the issue of the three-dimensional formation coordinated control for the unmanned autonomous helicopter (UAH) by using the sliding mode disturbance observer. Under the designed formation coordinated controller, the desired formation can be maintained and the closed-loop system stability is analyzed by using the Lyapunov theory.

Considering the unknown time-varying external 10; disturbance in formation flight of UAHs, a sliding mode disturbance observer has been employed to estimate them.

This work is supported in part by the National Natural Science Foundation of China under Grant 61803207, and in part by the Fundamental Research Funds for the Central Universities under Grant LGZD201806.

A sliding mode disturbance observer has been designed to estimate the unknown time-varying external disturbance in formation flight of UAHs. Aiming at the leading UAH maneuver in three-dimensional space during the formation flight progress, the formation coordinated controller has been proposed based on the output of the disturbance observer to maintain the formation.

Despite of the numerous characteristics of the multirotor unmanned aircraft systems (UASs), they have been termed as less energy-efficient compared to fixed-wing and helicopter counterparts. The purpose of this paper is to explore a more efficient multirotor configuration and to provide the robust and stable control system for it.

A heterogeneous multirotor configuration is explored in this paper, which employs a large rotor at the centre to provide majority of lift and three small tilted booms rotors to provide the control. Design provides the combined characteristics of both quadcopters and helicopters in a single UAS configuration, providing endurance of helicopters keeping the manoeuvrability, simplicity and control of quadcopters. In this paper, rotational as well as translational dynamics of the multirotor are explored. Cascade control system is designed to provide an effective solution to control the attitude, altitude and position of the rotorcraft.

One of the challenging tasks towards successful flight of such a configuration is to design a stable and robust control system as it is an underactuated system possessing complex non-linearities and coupled dynamics. Cascaded proportional integral (PI) control approach has provided an efficient solution with stable control performance. A novel motor control loop is implemented to ensure enhanced disturbance rejection, which is also validated through Dryden turbulence model and 1-cosine gust model.

Robustness and stability of the proposed control structure for such a dynamically complex UAS configuration is demonstrated with stable attitude and position performance, reference tracking and enhanced disturbance rejection.

The purpose of the paper is to examine a fixed wing unmanned aerial vehicle (UAV) as it undergoes five flight scenarios under straight and level, level climb, level turn, climbing turn and level steady heading sideslip conditions in a desired and controlled manner using constrained multi input multi output (MIMO) model predictive controllers (MPCs).

An MPC strategy based on the lateral and longitudinal linear models is proposed for the flight control design. Simulations are carried out for the nonlinear closed-loop aircraft Simulink model available from the University of Minnesota UAV research group with the implemented MIMO MPCs designed in this paper.

The results of the simulations show that the MIMO MPCs can achieve satisfactory performance and flying qualities under three different test conditions in terms of existing unmeasured outputs and unmeasured output disturbances.

The MPCs designed in this paper can be implemented to UAVs. Therefore, the implementation is considered as an advanced research.

The proposed MPC design in this paper provides more flexibility in terms of tracking complex trajectories comparing with the classical controllers in the literature. Besides they provide to change more than one reference of the states at any time.

The purpose of this paper is to design a controller for the unmanned aerial vehicle (UAV).

In this study, the constrained multivariable multiple-input and multiple-output (MIMO) model predictive controller (MPC) has been designed to control all outputs by manipulating inputs. The aim of the autopilot of UAV is to keep the UAV around trim condition and to track airspeed commands.

The purpose of using this control method is to decrease the control effort under the certain constraints and deal with interactions between each output and input while tracking airspeed commands.

By using constraint, multivariable (four inputs and seven outputs) MPC unlike the relevant literature in this field, the UAV tracked airspeed commands with minimum control effort dealing with interactions between each input and output under disturbances such as wind.