This paper deals with the trajectory tracking problem for a quadrotor unmanned aerial vehicle (UAV). For this purpose, two control strategies are proposed. First, a flight controller with a hierarchical structure is designed, whereby the complete closed-loop system is divided into two blocks. The system has an inner block for attitude control and an outer block for position stabilization, for a total of six proportional-derivative/proportional-integral-derivative (PD/PID) controllers. The second new trajectory tracking strategy is based on attitude stabilization. In addition to a direct stabilization of yaw and altitude, the x and y positions are stabilized by choosing an appropriate control of roll and pitch angles. The relations between positions (x, y) and rotations (roll, pitch) are derived from the natural flight of the quadcopter. In this second approach, with only four controllers, the quadrotor UAV is able to follow any trajectory. In both approaches, the PD/PID controllers are synthesized using the genetic algorithm method, and compared with those obtained by the reference model method. Furthermore, a comparison between PD and PID controller performance is performed. Thereafter, the robustness of the proposed controllers is tested for trajectory tracking in a disturbed environment. Simulation results demonstrate that for the two approaches, PD controllers show a better behavior with respect to quadcopter stabilization than in trajectory tracking under different conditions.

This paper explores the full control of a quadrotor Unmanned Aerial Vehicles (UAVs) by exploiting the nature-inspired algorithms of Particle Swarm Optimization (PSO), Cuckoo Search (CS), and the cooperative Particle Swarm Optimization-Cuckoo Search (PSO-CS). The proposed PSO-CS algorithm combines the ability of social thinking in PSO with the local search capability in CS, which helps to overcome the problem of low convergence speed of CS. First, the quadrotor dynamic modeling is defined using Newton-Euler formalism. Second, PID (Proportional, Integral, and Derivative) controllers are optimized by using the intelligent proposed approaches and the classical method of Reference Model (RM) for quadrotor full control. Finally, simulation results prove that PSO and PSO-CS are more efficient in tuning of optimal parameters for the quadrotor control. Indeed, the ability of PSO and PSO-CS to track the imposed trajectories is well seen from 3D path tracking simulations and even in presence of wind disturbances.

This paper introduces a nonlinear adaptive controller of unknown nonlinear dynamical systems based on the approximate models using a multi-layer perceptron neural network. The proposal of this study is to employ the structure of the Multi-Layer Perceptron (MLP) model into the NARMA-L2 structure in order to construct a hybrid neural structure that can be used as an identifier model and a nonlinear controller for the MIMO nonlinear systems. The big advantage of the proposed control system is that it doesn’t require previous knowledge of the model. Our ultimate goal is to determine the control input using only the values of the input and output. The developed NARMA-L2 neural network model is tuned for its weights employing the backpropagation optimizer algorithm. Nonlinear autoregressive-moving average-L2 (NARMA-L2) neural network controller, based on the inputs and outputs from the nonlinear model, is designed to perform control action on the nonlinear for the attitude control of unmanned aerial vehicles (UAVs) model. Once the system has been modeled efficiently and accurately, the proposed controller is designed by rearranging the generalized submodels. The controller performance is evaluated by simulation conducted on a quadcopter MIMO system, which is characterized by a nonlinear and dynamic behavior. The obtained results show that the NARMA-L2-based neural network achieved good performances in modeling and control.

Typical 3D integrated circuit structures based on through-silicon vias (TSVs) are complicated to study and analyze. Therefore, it seems important to find some methods to investigate them. In this paper, a method is proposed to model and compute the time-domain coupling noise in 3D Integrated Circuit (3D-IC) based on TSVs. It is based on the numerical inversion Laplace transform (NILT) method and the chain matrices. The method is validated using some experimental results and the Pspice and Matlab tools. The results confirm the effectiveness of the proposed technique and the noise is analyzed in several cases. It is found that TSV noise coupling is affected by different factors such as source characteristics, horizontal interconnections, and the type of Inputs and Outputs (I/O) drivers.