In this paper, the conceptual design, modeling, and simulation are proposed for an octorotor UAV with a manipulator arm. The conceptual design of the octorotor UAV with a manipulator arm is developed, and for the study and analysis, the design is implemented and validated in Matlab (Simulink-SimMechanics) software. The kinematics and dynamics models of the octorotor UAV with a manipulator arm are obtained using the classical Denavit–Hartenberg convention and the recursive Newton–Euler method, respectively. In this sense, a cascade PID controller for the attitude and navigation of the UAV and a simple PID controller for the manipulator arm are proposed and simulated in a closed-loop system in order to highlight the performance of the proposed design. Finally, simulations show the feasibility and behavior of the mathematical model and the flight controller in a closed-loop system.

In this work, a decentralized robust direct model reference adaptive controller (MRAC) via e-modification is suggested for the pose control of a quadrotor to prevent parameter drift. The governing equations of motion referred to the rotational system of the quadrotor are parameterized involving matched uncertainties through the control input channel in a decentralized way from the angles of motion, where bounded perturbations are also considered. An error-dependent damping term in the update law is added to enforce robustness. Uniform ultimate boundedness of the tracking error signal is ensured. The translational dynamics are governed through a linear proportional–integral–derivative (PID) control. The performance of the decentralized robust MRAC scheme proposed here is assessed via simulation and compared with that from decentralized robust MRACs using smooth dead-zone modification and $\sigma$-modification.

In this research, a proportional plus integral plus velocity (PIV) fuzzy gain scheduling flight controller for an octorotor mini-unmanned aerial vehicle is developed. The designed flight controller scheme, with a PIV term, is combined with a fuzzy gain scheduling approach. The tracking controller PIV fuzzy gain scheduling is based on two controllers connected in cascade with a saturation approach. The Newton–Euler equations of motion are applied to obtain a mathematical model for the octorotor mini-unmanned aerial vehicle (mini-UAV). The flight controller approach is applied to obtain coupling moments and forces with interconnected attitude and navigation tracking trajectory. In the design of a flight navigation controller with two layers, the inner layer consists of a PIV fuzzy gain scheduling controller that is applied to the attitude dynamics, obtaining the references for the coupling outer layer PIV fuzzy gain scheduling controller, which manipulates the translational dynamics. The navigation PIV fuzzy gain scheduling controller is saturated for bounding in translational forces to avoid large deviations of commands to Euler angles pitch and roll, and another saturated controller is implemented for the bounded thrust rotor to avoid the excessive angular speed of these rotors. The octorotor mini-UAV flight navigation simulation is performed to validate the tracking control of a sequence of motions in each axis, which is presented as a validation for the proposed control scheme.

The translational and rotational dynamics of quadrotor UAVs are commonly described by mathematical modeling where aerodynamic and inertial parameters are involved. Therefore, the importance of having accurate parameters in the model is critical for the correct performance of the UAV. In this paper, Artificial Neural Networks (ANNs) are used to estimate the aerodynamic and inertial parameters corresponding to the mathematical model of a quadrotor. Thrust and torque coefficients from the rotor models and the quadrotor inertia matrix are estimated by proposing and training two different ANN models implementing the back-propagation algorithm, using both experimental and simulation data. The estimated parameters are then compared with the reference parameters by means of quadrotor attitude simulations, showing high accuracy in their behavior. The results have shown that the proposed ANN models can accurately estimate both the aerodynamic and inertial parameters of a quadrotor UAV model using both experimental and simulation data, thus contributing to increasing the tools available for parameter estimation.

This research work addresses the problem of path planning for unmanned aerial vehicles (UAVs) in complex environments such as a maze. A genetic algorithm (GA) with variable chromosomes and gene change conditions is designed when the decision point criteria and a collision with a wall are presented; without considering large chromosomes. Our proposed GA obtains the sequence of minimum movements required to solve the maze. Then, the trajectories are generated by high-order polynomials. Finally, numerical simulations and real-time tests are carried out to validate the proposed algorithm.

In this work, a velocity tracking control strategy is presented using passivity methodology for a permanent magnet synchronous machine with uniformly distributed windings, which is known as a brushless direct current (BLDC) motor. The trapezoidal components of back electromotive force (EMF) are here modeled by using a novel differentiable approximation. The proposed control scheme is constructed by employing only measurements of the shaft position and stator currents. The use of a second-order filter makes it possible to solve the velocity tracking control problem without measuring the actual rotor velocity. A formal stability analysis of the closed-loop system consisting of the motor, the controller, and the second-order filter is presented using partial stability theory. By applying partial stability, the relevant variables are shown to converge, while the behavior of the rest of the variables that are not relevant to the control objective can be disregarded, which makes the theoretical analysis simpler. A numerical simulation is constructed to verify the applicability of the control law. The results show that all the variables of the BLDC motor are below the nominal values of the machine. Therefore, the physical implementation of the controller is feasible.