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This paper aims to describe the characteristics and modeling of the variable pitch quadrotor. In a variable pitch quadrotor, unlike ordinary quadrotors that the force is generated by the rotors, the speed of the rotors is constant, and the force is generated by varying the pitch angle of blades.

In this paper, a sliding mode controller and an adaptive sliding mode controller are used to control the variable pitch quadrotor to have better performance.

The variable pitch mechanism has a wider control bandwidth, and it is able to produce a negative thrust that facilitates trajectory tracking and aggressive maneuvers. The simulation results indicate high performance of the proposed control scheme in presence of disturbance and changes in mass of the quadrotor.

The performance of the controllers for the variable pitch quadrotor is investigated through computer simulation with MATLAB software.

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 study aims to compare the performance of the conventional and fractional order proportional-integral-derivative (PID and FOPID) controllers tuned with a particle swarm optimization (PSO) and genetic algorithm (GA) for quadrotor control.

In this study, the gains of the controllers were tuned using PSO and GA, which are included in the heuristic optimization methods. The tuning processes of the controller’s gains were formulated as optimization problems. While generating the objective functions (cost functions), four different decision criteria were considered separately: integrated summation error (ISE), integrated absolute error, integrated time absolute error and integrated time summation error (ITSE).

According to the simulation results and comparison tables that were created, FOPID controllers tuned with PSO performed better performances than PID controllers. In addition, the ITSE criterion returned better results in control of all axes except for altitude control when compared to the other cost functions. In the control of altitude with the PID controller, the ISE criterion showed better performance.

While a conventional PID controller has three parameters (Kp, Ki, Kd) that need to be tuned, FOPID controllers have two additional parameters (µ). The inclusion of these two extra parameters means more flexibility in the controller design but much more complexity for parameter tuning. This study reveals the potential and effectiveness of PSO and GA in tuning the controller despite the increased number of parameters and complexity.

The growing use of small unmanned rotorcraft in civilian applications means that safe operation is increasingly important. The purpose of this paper is to investigate the fault tolerant properties to faults in the actuators of an C ₁ adaptive controller for a quadrotor vehicle.

C ₁ adaptive control provides fast adaptation along with decoupling between adaptation and robustness. This makes the approach a suitable candidate for fault tolerant control of quadrotor and other multirotor vehicles. In the paper, the design of an C ₁ adaptive controller is presented. The controller is compared to a fixed-gain LQR controller.

The C ₁ adaptive controller is shown to have improved performance when subject to actuator faults, and a higher range of actuator fault tolerance.

The control scheme is tested in simulation of a simple model that ignores aerodynamic and gyroscopic effects. Hence for further work, testing with a more complete model is recommended followed by implementation on an actual platform and flight test. The effect of sensor noise should also be considered along with investigation into the influence of wind disturbances and tolerance to sensor failures. Furthermore, quadrotors cannot tolerate total failure of a rotor without loss of control of one of the degrees of freedom, this aspect requires further investigation.

Applying the C ₁ adaptive controller to a hexrotor or octorotor would increase the reliability of such vehicles without recourse to methods that require fault detection schemes and control reallocation as well as providing tolerance to a total loss of a rotor.

In order for quadrotors and other similar unmanned air vehicles to undertake many proposed roles, a high level of safety is required. Hence the controllers should be fault tolerant.

Fault tolerance to partial actuator/effector faults is demonstrated using an C ₁ adaptive controller.

This study aims to deal with the problem of trajectory tracking control for the quadrotor under external environmental disturbance and variable payloads.

In the field of unmanned aerial vehicle (UAV) control, external environmental disturbance and internal variable payloads as two major interference factors lead to control performance degradation or even instability, thus a trajectory tracking controller which innovatively combines sliding mode control technology and model-free control technique is proposed. The proposed controller is constructed with a learning rate-based sliding mode controller and an ultra-local model. Based on the proposed controller, the nonlinear system model of variable load quadrotor is locally estimated and the system’s uncertainties and disturbances can be compensated.

The simulation and actual test results demonstrate the satisfactory control performance and the robustness of the proposed controller compared with the PID and Backstepping controller under external environmental disturbance and variable payloads. Moreover, the proposed controller solves the trajectory tracking control problem not only when payloads change at the center of gravity but also when the position of load variation deviates from the center of gravity.

In both military and civilian domains, the quadrotor may encounter such situations that the payloads change, such as transporting goods, aerial refueling and so on. As a large internal interference factor, variable load tends to lead to unstable control. The research results provide theoretical guidance and technical support for trajectory tracking control of quadrotor under variable payloads.

The proposed controller combines learning rate-based sliding mode controller and model-free control technique to achieve a more efficient and accurate trajectory control of the quadrotor when considering system uncertainties and the load variation that happens in the unknown location.

The purpose of this paper is to design an output-feedback algorithm based on low-power observer (LPO), robust chattering-free controller and nonlinear disturbance observer (DO) to achieve trajectory tracking of quadrotor in the Cartesian plane.

To achieve trajectory tracking control, firstly the decoupled rotational and translational model of quadrotor are modified by introducing backstepped state-space variables. In the second step, robust integral sliding mode control is designed based on the proportional-integral-derivative (PID) technique. In the third step, a DO is constructed. In next step, the measurable outputs, i.e. rotational and translational state variables, are used to design the LPO. Finally, in the control algorithm all state variables and its rates are replaced with its estimates obtained using the state-observer.

The finding includes output-feedback control (OFC) algorithm designed by using a LPO. A modified backstepping model for rotational and rotational systems is developed prior to the design of integral sliding mode control based on PID technique. Unlike traditional high-gain observers (HGO), this paper used the LPO for state estimation of quadrotor systems to solve the problem of peaking phenomenon in HGO. Furthermore, a nonlinear DO is designed such that it attenuates disturbance with unknown magnitude and frequency. Moreover, a chattering reduction criterion has been introduced to solve the inherited chattering issue of controllers based on sliding mode technique.

This paper presents input and output data-driven model-free control algorithm. That is, only input and output of the quadrotor model are required to achieve the trajectory tracking control. Therefore, for practical implementation, the number of on-board sensor is reduced.

Although extensive research has been done for designing OFC algorithms for quadrotor, LPO has never been implemented for the rotational and translational state estimations of quadrotor. Furthermore, the mathematical model of rotational and translational systems is modified by using backstepped variables followed by the controller designed using PID and integral sliding mode control technique. Moreover, a DO is developed for attenuation of disturbance with unknown bound, magnitude and frequency.

In this research paper, an adaptive output-feedback robust active disturbance rejection control (RADRC) is designed for the multiple input multiple output (MIMO) quadrotor attitude model subject to unwanted uncertainties and disturbances (UUDs).

In order to achieve the desired control objectives in the presence of UUDs, the low pass filter (LPF) and extended high gain observer (EHGO) methods are used for the estimation of matched and mismatched UUDs, respectively. Furthermore, for solving the chattering incurred in the standard sliding mode control (SMC), a multilayer sliding mode surface is constructed. For formulating the adaptive output-feedback RADRC algorithm, the EHGO, LPF and SMC schemes are combined using the separation principle.

The findings of this research work include the design of an adaptive output-feedback RADRC with the ability to negate the UUDs as well as estimate the unknown states of the quadrotor attitude model. In addition, the chattering problem is addressed by designing a modified SMC scheme based on the multilayer sliding mode surface obtained by utilizing the estimated state variables. This sliding mode surface is also used to obtain the adaptive criteria for the switching design gain parameters involved in the SMC. Moreover, the requirement of high design gain parameters in the EHGO is solved by combining it with the LPF.

Designing the flight control techniques while assuming that the state variables are available is a common practice. In addition, to obtain robustness, the SMC technique is widely used. However, in practice, the state variables might not be available due to unknown parameters and uncertainties, as well as the chattering due to SMC reduces the performances of the actuators. Hence, in this paper, an adaptive output-feedback RADRC technique is designed to solve the problems of UUDs and chattering.

This study aims to optimize autonomous performance (i.e. both longitudinal and lateral) and endurance of the quadrotor type aerial vehicle simultaneously depending on the autopilot gain coefficients and battery weight.

Quadrotor design processes are critical to performance. Unmanned aerial vehicle durability is an important performance parameter. One of the factors affecting durability is the battery. Battery weight, energy capacity and discharge rate are important design parameters of the battery. In this study, proper autopilot gain coefficients and battery weight are obtained by using a stochastic optimization method named as simultaneous perturbation stochastic approximation (SPSA). Because there is no direct correlation between battery weight and battery energy density, artificial neural network (ANN) is benefited to obtain battery energy density corresponding to resulted battery weight found from SPSA algorithm. By using the SPSA algorithm optimum performance index is obtained, then obtained data is used for longitudinal and lateral autonomous flight simulations.

With SPSA, the best proportional integrator and derivative (PID) coefficients and battery weight, energy efficiency and endurance were obtained in case of morphing.

It takes a long time to find the most suitable battery values depending on quadrotor endurance. However, this situation can be overcome with the proposed SPSA.

It is very useful to determine quadrotor endurance, PID coefficients and morphing rate using the optimization method.

Determining quadrotor endurance, PID coefficients and morphing rate using the optimization method provides advantages in terms of time, cost and practicality.

The proposed method improves quadrotor endurance. In addition, with the SPSA optimization method and ANN, the parameters required for endurance will be obtained faster and more securely. In addition, the energy density according to the battery weight also contributes to the clean environment and energy efficiency.

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 enhance the quadrotor’s capability of short-distance delivery to satisfy the large demand for quadrotor, which is used for goods distribution in huge warehouses, under time-varying payload and external wind disturbance.

A trajectory tracking controller design based on the combination of an adaptive sliding mode control (ASMC) method and the active disturbance rejection control (ADRC) technique is proposed. Besides, an inner–outer loop control system structure is adopted.

Simulation results of different trajectory tracking verify the effectiveness and robustness of the proposed tracking control method under various conditions, including parameter uncertainty and external wind disturbance. The proposed control strategy ensures that quadrotor UAV is capable of tracking linear and spiral trajectory well whether it loads or unloads goods in the presence of the external wind disturbance.

The proposed method of designing a trajectory tracking controller is based on an integral ADRC and ASMC scheme so as to deal with the trajectory tracking problem for a quadrotor with payload variation.