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This paper presents the inverted PID control of a quadrotor based on the experimentally measured sensors and actuators’ specifications. The main goal is the control and closed loop simulation of a quadrotor using inverted PID algorithm. First, a nonlinear model of quadrotor is derived using Newton-Euler equations. To have a more realistic simulation a setup were designed and developed to measure the sensors noise performance as well as the actuators’ dynamics. The setup involves a platform that two brushless motors mounted at the ends and rotates on a shaft. The platform attitude is measured using the MEMS sensors attached to it. A Kalman filter was used to reduce the sensors noises effect. Results demonstrate good performance for Kalman filter and the controller.

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Traffic issue is an international challenge in the sophisticated countries in which over population is considered as an important factor in creating this problem. Studies show that the accidents’ report during the minimum time is the best way to control the traffics. For this purpose, this paper has been done in such a way that after modeling the flying robot using Newton-Euler equations, a three-dimensional constrained optimal trajectory has been generated through Direct Collocation Approach. In other words, the proposed problem in this paper is first formulated as an optimal control problem. Afterwards, the optimal control problem is discretized through Direct Collocation Technique, which is one of the numerical solving methods of the optimal control problems, and it is transformed into a Nonlinear Programing Problem (NLP). Eventually, the aforementioned nonlinear programming problem is solved via SNOPT which works based on the gradient algorithm like SQP. It should be noted that since the main objective of motion planning in this paper is controlling the urban traffic, the urban constrains are utilized during the trajectory optimization. In other words, all of the high-rise buildings located during the course are modeled by the various cylinders. The efficacy of the aforementioned method is demonstrated by extensive simulations, and in particular it is verified that this method is capable of producing a suitable solution for three-dimensional constrained optimal motion planning for a six-degree-of-freedom quadrotor helicopter for urban traffic purposes.

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In this paper, modeling and design of a trajectory tracking control system for a novel multi-rotor UAV (Unmanned Aerial Vehicle) is developed. The UAV is similar to a quadrotor with an extra no feedback propeller which is added to center of vehicle. The additional rotor improves the ability of lifting heavier payloads, and anti-crosswind capability for quadrotor. For validation, the dynamic model is obtained via both Newton Euler and Lagrange approaches. The dynamical model is under actuated, nonlinear, and has strongly coupled terms. Therefore, an appropriate control system is necessary to achieve desired performance. The proposed nonlinear controller of this paper is an input-output feedback linearization companioned with an optimal LQR controller for the linearized system. The controller involves high-order derivative terms and turns out to be quite sensitive to un-modeled dynamics. Therefore, precise model of UAV is derived by considering actuator’s dynamics. To compensate the actuator’s dynamic and moreover, to avoid complexity in the controller, a second control loop is utilized. The obtained simulation results confirm that the proposed control system has a promising performance in terms of stabilization and position tracking even in presence of external disturbances.

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In this paper, modeling and tow type of nonlinear controller for trajectory tracking of a novel five-rotor UAV (Unmanned Aerial Vehicle) is developed. Because of the very simple structure and high maneuverability, quadrotors are one of the most preferred types of UAVs but the main problem of them is their small payload. In the proposed novel model, one propeller is added to the center of vehicle to improve the ability of lifting heavier payloads, and to excel anti-crosswind capability of quadrotor. The dynamic model is obtained via Newton Euler approach. The model is under actuated, nonlinear, and has strongly coupled terms. Also, two types of nonlinear controllers are presented. First one is a conventional input-output feedback linearization controller which involves high-order derivative terms and turns out to be quite sensitive to sensor noise as well as modeling uncertainty. Second controller is a BackStepping controller based on the hierarchical control strategy that yields easier controller. The obtained simulation results confirm that the performance of BackStepping controller is convenient in terms of stability, position tracking and it is robust in presence of disturbance.

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In this paper, modeling and feedback linearization controller for trajectory tracking of a novel six-rotor UAV (Unmanned Aerial Vehicle) is developed. Because of the very simple structure and high maneuverability, quadrotors are one of the most preferred types of UAVs but the main problem of them is their small payload. In the proposed novel model, two coaxial propellers are added to the center of vehicle to improve the ability of lifting heavier payloads, and to excel anti-crosswind capability of quadrotor, while the dynamic and steering principle is preserved. The dynamic model is obtained via Newton Euler approach. Model is under actuated, nonlinear, and has strongly coupled terms. Also, two types of nonlinear controllers are presented. First one is a conventional input-output feedback linearization controller which involves high-order derivative terms and turns out to be quite sensitive to sensor noise as well as modeling uncertainty. Second controller is a feedback linearization based on the hierarchical control strategy that yields easier controller. To compensate actuator’s dynamic and moreover, to avoid complexity of controller, a two-stage algorithm is utilized. The obtained simulation results confirm that the performance of hierarchical controller is more convenient in terms of position tracking and disturbance rejection than conventional controller.

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In this paper, a trajectory tracking control strategy for a quadrotor flying robot is developed. At first, dynamic model is obtained by lagrange-euler approach. Then, control structure, consisting of a model-based predictive controller, has been used based on state space error to track transitional movements for reference trajectory and also robust nonlinear H∞ control is applied for stabilizing the rotational movements and reject the external disturbance. In both controllers the integral of the position error is considered, allowing the achievement of a null steady-state error when sustained disturbances are acting on the system. The external disturbances is considered as aerodynamic torques. If uncertainties increase, the designed control system will be unable to track and stabilizing perform properly and completely. So finally, in order to eliminate the effects of parameter uncertainties the recursive least squares is used for estimating mass and moment inertia parameters which are linear and it is applied to the control system. Simulation results show that by using estimation of system parameters, the proposed control system has a promising performance in terms of stabilization and position tracking even in the presence of external disturbance and parametric uncertainties.

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This paper presents a completely practical control approach for quadrotor drone. Quadrotor is modelled using Euler-Newton equations. For stabilization and control of quadrotor a classic PID controller has been designed and implemented on the plant and a fuzzy controller is used to adjust the controller parameters. Considering that quadrotor is a nonlinear system, using classic controllers for the plant is not effective enough. Therefor using fuzzy system which is a nonlinear controller is effective for the nonlinear plant. According to the desire set point, fuzzy system adjusts the controller gain values to improve the performance of quadrotor and it leads to better results than classical PID controller. To study the performance of fuzzy PID controller on attitude control of the system, a quadrotor is installed to the designed stand. The system consists of accelerometer and gyroscope sensors and a microcontroller which is used to design fuzzy PID attitude controller for the quadrotor. Considering that the experimental data has lots of errors and noises, Kalman filter is used to reduce the noises. Finally using the Kalman filter leads to better estimation of the quadrotor angle position and the fuzzy PID controller performs the desired motions successfully.

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This paper presents the control of a quadrotor using nonlinear approaches based on the experimentally measured sensors data. The main goal is the control and closed loop simulation of a quadrotor using feedback linearization and sliding mode algorithms. First, a nonlinear model of quadrotor is derived using Newton-Euler equations. To have a more realistic simulation the sensors noise performance were measured using a setup. sensors data was measured under on engines. Since the experimental data for sensor had error and noise, a Kalman filter was used to reduce sensors noise effect. Results demonstrate good performance for Kalman filter and controllers. Results showed that feedback linearization and sliding mode controllers performance was good but angles changes were smoother on feedback linearization controller. With increasing uncertainty, feedback linearization performance was away desired mode from this aspect The time to reach the goal situation while increasing uncertainty was no significant impact on the performance of sliding mode controller.Thus feedback linearization controller added PID is Appropriate to Maintain the quadrotor attitude while sliding mode controller has better performance to angles change and transient situations.

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In this paper, using both linear and nonlinear identification methods based on iterative and recursive least-square, the performance of a backstepping control system of a quadrotor in the presence of uncertainties is improved. At first, the dynamic model of a quadrotor is introduced and descriptive equations are presented in an appropriate state-space in order to design a controller based on backstepping method. Then the backstepping controller is designed using virtual controller for trajectory tracking. In this control system, the control performance is not satisfying because of the physical uncertainties existed in quadrotor. Consequently, an online identification method is introduced and used to improve the performance of the controller. In this regard, some parameters, which are linear in the model structure, are identified by least square error technique and iterative least square method is used for identifying other parameters.The results indicate that the steady-state error is decreased and the ability of tracking of a desired trajectory in the presence of uncertainties is increased. Furthermore, the result demonstrate the stabilization of roll and pitch angles, while, the method prevents the vibration of control forces.

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One of the most important factors in surveillance systems using robots, is the complexity and unpredictability of the robot trajectories. This becomes more vital in hostile conditions where the robot trajectory is being followed by another agent. Therefore, random or chaotic sequences can be used in motion planning of surveillant robots. However chaotic sequences would be more effective due to their deterministic nature. Moreover the intrinsic robustness and ergodicity of chaotic systems, compared to random functions, would be another advantage to be considered in surveillance systems which require comprehensive coverage. In this paper, a method is proposed for chaotic motion planning for boundary surveillance and implemented to a quadrotor robot. Quadrotor robot is introduced as an appropriate choice for boundary surveillance application due to high maneuverability and aerial functions. The chaotic trajectory is produced using Henon map. Then the dynamics of the system is derived and a sliding mode controller is designed for such chaotic motion. Finally the dynamics of the robot and the proposed controller are simulated to generate the chaotic trajectories for two cases. The performance of the proposed algorithm is discussed according to unpredictability and staying in the allowable region. A circular path and a non-smooth path are considered for simulation examples.

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The purpose of this article is dynamic modeling of a quadrotor and control of its Roll and Pitch angles based on the experimentally measured sensors data. So, after driving nonlinear model of quadrotor equations, the control of the quadrotor’s angular situation was simulated using PID and feedback linearization algorithms. Due to the widespread application of MEMS sensors in measuring the status of various systems and to have a more realistic simulation, sensors data was measured and used in simulation of controllers. Due to errors of MEMS sensors, vibration of motors and airframe, being noise on outputs, Kalman filter was used for estimation of angular situation. As one of the purposes of this paper was the use of its results in actual control of a quadrotor, motor model was used to determine PWM control signals. The results obtained from simulation in Simulink showed good performance of both controllers in controlling roll and pitch angles.

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In this paper, an adaptive robust tracking control system for an unmanned quadrotor is designed .Quadrotor placed in category of rotary wing aerial vehicle, and it is an under actuated and inherently unstable system. Also the dynamic model of system is nonlinear and along with the Uncertainty, therefore it is required to design a robust control system for stabilization and tracking the desired path. This system must be capable to retain the quadrotor balance in the presence of the disturbance, undesired aerodynamical forces and Measurement error of constant parameters. The suggested controller in this paper consists of two inner and outer control loops. Inner loop controls the Euler angles and outer loop is for control the quadrotor position and translational motion, and calculating the desired angles for trajectory tracking. In this paper by utilizing the adaptive sliding mode, the controller has been designed which is no need to be given the uncertainty range and the upper bound of it will be estimated as a scalar number. In order to prevent from diverging adaptive parameters, the sigma-modification is used in adaption laws and also to achieve suitable performance in various load, the total mass is estimated adaptively. The control design is based on the Lyapunov theory and the robust stability of system in the presence of the disturbance have been shown.

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Quadrotors are types of Unmanned Aerial Vehicles (UAVs) which have unique features compared to conventional aircrafts because of its vertical take-off and landing capability, flying in small areas and its high maneuverability. Also the relatively simple, economical and easy flight system of quadrotors, makes it to widely used as a good platform for development, implementation and testing a variety of control methods. One of the robust control methods is sliding mode control. In spite of the high capabilities of this approach, it has a main problem which is high frequency switching of the control signal witch is known the chattering phenomenon. In the past several decades, fractional order differential equations have been implemented in engineering application field, including controllers design and provided the possibility of using controllers for improving the performance of system. In this paper, a fractional order sliding surface has been employed for designing sliding mode control rule for quadrotors. The main objective of this study is to improve the performance and reduce the chattering phenomenon in sliding mode method. In this regard, by introducing sliding 〖PD〗^α surface, the control rule is designed in two different modes of 0

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In this paper, according to flight devices categories, advantages and features of quadrotors and its performances are investigated. Then, the main challenges in quadrotors control and stability in the presence of obstacles have been considered in such a way that the system crosses the maximum number of obstacles in the best distance and time. For this purpose, the equations of motion of the system are derived and a controller for command tracking is designed without obstacles based on sliding mode method. The simulation results of the controller performances are given in the paper. In continuous, trajectory planning for crossing the system from the obstacles in alternative positions is presented and the quadrotor with the designed control system are simulated using the designed trajectory. The preference of the proposed trajectory planning is that the system can cross the number of obstacles in alternative positions in minimum time and using fewer sensors. Because of free shape of designing method and alternative initial velocity, the proposed method are applicable for piecewise trajectories. As a result of considering the drag force, the proposed approach is more successful in the various problems.

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Sliding mode control is one of the most common types robust control that can compensate the model structure and parametric uncertainties, but the main disadvantage of this method is chattering phenomenon. Although a boundary layer around the sliding surface can eliminate chattering effect, it reduces tracking performance and robustness in control. The second generation of sliding mode control called Second Order Sliding Modes (SOSM) is a solution to this problem. Super-Twisting Sliding Mode (STSM) is a modified SOSM control that reduces chattering effect naturally and without a defined boundary layer, while maintaining the robustness of the Conventional Sliding Mode (CSM) control. In this paper, the problem trajectory planning is solved in an environment with fixed obstacles by using firefly optimization algorithm and polynomial trajectories, then STSM control is designed for quadrotor in the presence of uncertainties to tracking path trajectory and the performance of this controller is compared against Feedback Linearization (FL) and CSM control. Also, derivative of some of the states calculates by using super-twisting observer in the closed loop control and stabilization while there is no direct access to them through the sensors.

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Quadrotor is a Flying robot which can fly vertically and has a simple structure. Because of nonlinear dynamics of the system, Stability of the control process has an important role in this robot. In this paper, a neural controller is designed to stabilize the quadrotor. The neural controller is used to stabilize the attitude of the quadrotor. We first designed a PD controller using Ziegler Nichols method, then an online learner neural controller is trained for tuning the parameters of this PD controller. To verify these controllers first a simulation performed in the Simulink environment of the Matlab. In addition to simulation we have practically implemented these control methods on a Quadrotor test bench. Practical implementation results demonstrate the effectiveness of the presented method.

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Attitude control of the UAV’s is basis of the of many control systems such as position control, trajectory traking, traking moving targets and obstacle avoidance. Hence, one of the most important parts of the UAV's control is designing an appropriate and efficient controller, so that system being able to eliminates or reduces external disturbances, mechanical underactuation, changes in the model or physical parameter and interactions between its subsystems. In this paper, the attitude control problem is studed. For this purpose, the dynamics model of a quadrotor is derived by using Newton-Euler mtethod and the required parameters of the model such as moment of inertia, thrust and drag torque coefficient identified by experimental methods and an actual physical sample. Then, modidied PID and sliding mode controllers are designed to provide attitude traking for quadrotor and performance of these controllers is investigated in the presence of disturbance and sensors noise. Finally, the desgned cotrollers are implemented on a real 3DOF system and the experimental results are compared with the simulation results.

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In unmanned aerial vehicle (UAV) classes, the control of quadrotor has attracted many researchers from around the world in recent years. In this type of rotary wing, it is attempted to achieve stability in hover and motion flight modes using the forces, produced by propellers. Quadrotor has nonlinear and time-varying behavior and the aerodynamic forces almost always disturb it. In near the ground, the wake of quadrotor interacting with the ground surface causes perturbation to the flow near the blades and frame. These perturbations have significant effect on quality and stability of flight. Most of the related researches were only studied hover and landing operation and the ground effect was considered as constant coefficient in dynamic equations. In this paper, a comprehensive nonlinear model is developed for variety modes of quadrotor flight in near the ground in space state, and the ground effect is as function of state variables in equation. Then, according to the proposed model, the PID controller is designed and the effect of the ground effect on controller performance is investigated. The results of simulation indicate that, the flight stability and trajectory tracking have improved significantly by using of the model and designed controller.

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In this paper, we used a non-singular backstepping terminal sliding mode control approach to the unmanned aerial vehicle (quadrotor). In the first step, the governing dynamical equations were obtained based on the quadrotor considering all the effective parameters. The controller objective is limited to obtaining proper tracking of the desired positions (x, y, z) and the yaw angle (ψ), as well as maintaining the stability of the roll and pitch angles despite the presence of external disturbances. Controlling methods require complete information about system states that may be limited in practice. Even if all system conditions are available, it is interfered by noise, and also large number of applier sensors to measure states, makes the entire system more complex and costly. For this purpose, the Extended Kalman Filter (EKF) has been used as an observer. The extended Kalman filter is used as a speed observer and estimator of external disturbances such as wind force. Therefore, the use of a controller-observer is suggested to estimate the effects of external disturbances in order to compensate for them. The design method is based on the stability of Lyapunov. Simulation results show the promising performance and suitability of the observer-controller.

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Controller design for non-linear multi-input, multi-output systems, such as unmanned quadrotor vehicles, has always been a challenging issue due to the strong interconnection between state variables and highly nonlinear dynamic equations. In addition, quadrotor is an under-actuated non-linear dynamic device. Due to being under-actuated for moving in the horizontal direction, the combination of changes in the speed of the existing quadruple operators should be used. So that, by creating the angle between the quadrotor hypothetical plane and the horizon surface, the device can be forced to move in the longitudinal or transverse direction. Therefore, in the quadrotor control system, two nested control loops are required. An outer loop to determine the appropriate angle of the device relative to the horizon for horizontal movements and an inner loop that is required to angle of the device panel is equal to this angle. In this paper, a fuzzy hybrid super-twisting sliding mode non-linear controller for controlling a sample quadrotor is designed. For this purpose, a fuzzy controller in the outer loop and a super twisting sliding mode controller in inner loop are used. An important advantage of this strategy is that it optimizes the horizontal speed of the device. If the distance from the target is too high, the angle of the device panel also increases, and if the distance is reduced, the angle also decreases. As a result, the device reaches the target with the desired speed. The performed simulation results confirmed this fact.