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Unmanned Aerial Vehicles (UAVs) installed with a gripper is an effective and robust way to grab the wanted object from inaccessible locations. In this study, we develop a novel control mechanism to regulate the nonlinear dynamics of the aerial manipulator. In this research, hex-rotor UAV is chosen in order to fulfill the mission requirement in terms of size and weight of the object. It is equipped with a manipulator and the gimbal-based camera that will help to see the desired object and then transport it. The aerial vehicle has six-degrees-of-freedom (6-DOF) and the installed manipulator has 4-DOF which in total makes the 10-DOF aerial manipulator vehicle. At the time of clutching the desired object to eliminate or reduce the external noise, and stabilize the dynamic behavior of the aerial manipulator, we need a robust and efficient controller. To solve the aforementioned problems, this study develops a hybrid control mechanism that tracks and controls the altitude and attitude of UAV after clutching the desired object. The main contribution of this study is to design a control mechanism that includes Model Reference Adaptive Control with an Integrator (MRACI) in conjunction with regulation, pole-placement and tracking (RST) control algorithm. On one hand, the simulation results using MATLAB demonstrate the efficiency of the proposed control mechanism. On the other hand, to cross verify the validity of the proposed control algorithm, we perform the experiment by clutching the desired object at hovering and normal flight operation. [ABSTRACT FROM AUTHOR]

Remotely operated underwater vehicle (ROUV) provides an interesting and extensible platform to search the wanted objects and for the inspection of deep sea. The basic goal of this research is to control the position and stabilize the dynamic behavior of ROUV. In this study, we design the dual controller approach design for controlling the overall responses of ROUV. The design dual controller consists of model reference adaptive control (MRAC) along with proportional integral derivative (PID) controller and an integral use for the feedback of the design scheme. The dynamic moments and disturbances in the system is dealt by MRAC controller and PID controller is responsible for tuning the adaptive gains of the system. However, Lyapunov stability criterion is responsible for the stability of the system. The inclusion of integrator as a feedback in the system increases the order of the system model, but it helps to eliminate the steady state error and also improves the convergence rate of the system. The designed control algorithm is tested and confirmed its validity using experiment and simulations by tracking the reference path of the ROUV. It is evidence that the designed control system shows quick convergence, improved steady state error and better robustness in the presence of disturbances. [ABSTRACT FROM AUTHOR]

In this article, adaptive hybrid control scheme is proposed for controlling the position of a coaxial tri-rotor unmanned aerial system (UAS) in the presence of input saturation and external wind disturbance. The adaptive hybrid controller consists of model reference adaptive control with integral feedback (MRACI) and proportional integral derivative (PID) controller. The adaptive controller deals with the flight dynamics uncertainties and PID controller is used for tuning the gains of MRACI whereas the stability of system is verified by Lyapunov stability criterion. The integrator improves the order of the system thereby improving the convergence rate by rejecting the noise and eliminating steady state errors. Moreover, anti-windup Compensator (AWC) is used to handle the saturation problem. The designed algorithm is applied to a six degree of freedom (6-DOF) nonlinear model of coaxial tri-rotor UAS. Simulations are carried out to validate the reference path of UAS and are compared with MRAC. In this article the wind disturbance test is also performed to check the robustness of the designed controller. It is observed that the proposed algorithm exhibits, quick error convergence, zero steady state error and robustness in the presence of input saturation and external wind disturbance. [ABSTRACT FROM AUTHOR]

Arm mounted unmanned aerial vehicles provide more feasible and attractive solution to manipulate objects in remote areas where access to arm mounted ground vehicles is not possible. In this research, an under-actuated quadrotor unmanned aerial vehicle model equipped with gripper is utilized to grab objects from inaccessible locations. A dual control structure is proposed for controlling and stabilization of the moving unmanned aerial vehicle along with the motions of the gripper. The control structure consists of model reference adaptive control augmented with an optimal baseline controller. Although model reference adaptive control deals with the uncertainties as well as attitude controlling of unmanned aerial vehicle, baseline controller is utilized to control the gripper, remove unwanted constant errors and disturbances during arm movement. The proposed control structure is applied in 6-degree-of-freedom nonlinear model of a quadrotor unmanned aerial vehicle equipped with gripper having (2 degrees of freedom) robotic limb; it is applicable for the simulations to desired path of unmanned aerial vehicle and to grasp object. Moreover, the efficiency of the presented control structure is compared with optimal baseline controller. It is observed that the proposed control algorithm has good transient behavior, better robustness in the presence of continuous uncertainties and gripper movement involved in the model of unmanned aerial vehicle. [ABSTRACT FROM AUTHOR]

This research proposes ASC (Adaptive Synergetic Controller) for the nonlinear model of MH (Mini Helicopter) to stabilize the desired altitude and angle. The model of MH is highly nonlinear, under-actuated and multivariable in nature due to its dynamic uncertainties and restrictions of velocities during the flight. ASC can force the tracking errors of the system states converges to zero in a finite interval of time. The MH system requires smooth controller and fast precise transition response from initial state till the desired state, therefore the parametric calculations and simulations can be done by the proposed ASC algorithm. It is validated that the above simulated results of the proposed controller have a better convergence rate and smoother stability response in order to track the desired altitude and angle when compared with SMC (Sliding Mode Controller). Moreover, it does not need any linearization, transformation and variations in the system model. [ABSTRACT FROM AUTHOR]

In this article, a novel hybrid control scheme is proposed for controlling the position of a three-phase brushless direct current (BLDC) motor. The hybrid controller consists of discrete time sliding mode control (SMC) with model free adaptive control (MFAC) to make a new data-driven control (DDC) strategy that is able to reduce the simulation time and complexity of a nonlinear system. The proposed hybrid algorithm is also suitable for controlling the speed variations of a BLDC motor, and is also applicable for the real time simulation of platforms such as a gimbal platform. The DDC method does not require any system model because it depends on data collected by the system about its Inputs/Outputs (IOS). However, the model-based control (MBC) method is difficult to apply from a practical point of view and is time-consuming because we need to linearize the system model. The above proposed method is verified by multiple simulations using MATLAB Simulink. It shows that the proposed controller has better performance, more precise tracking, and greater robustness compared with the classical proportional integral derivative (PID) controller, MFAC, and model free learning adaptive control (MFLAC). [ABSTRACT FROM AUTHOR]