رضا طریقی

Tracking the movement path is one of the most important factors of system control in intelligent subsurface robots. In this paper, the robust position control for a Solar Powered AUV is developed using nonlinear modeling. Because the used controller does not have the disadvantages of using a linear controller around a working point. Energy problems have been solved through the use of solar energy and will increase efficiency in a longer mission. That is, reducing the number of energy storage elements in these types of robots will increase other capabilities in these types of models. An attempt has been made to improve the movement path by relying on the combination of two Loops. In order to reduce the complexity of the controller, two models based on kinematics and dynamics of a Solar Powered AUV system with 6-DoF have been developed and generalized. In this modelling, the inner loop representing the system is used for virtual tracking with the back-stepping control technique and the outer loop representing the speed control, in convergence with the inner loop to oppose uncertainties and disturbances (internal-external) based on the sliding mode. A virtual reference input for robust nonlinear control is applied to the first part and then a real control input is applied to the second system. The closed-loop stability of the proposed subsystems is guaranteed so that it has a uniform final performance based on the Lyapunov stability criteria. Next, in order to guarantee the performance of the controllers, in addition to the different scenarios that have been introduced, in the simulation, external disturbances have also been applied to the system. The results of the outputs show the capability of the closed loop system in dealing with disturbances and the proper performance is in tracking the movement path and proves the feasibility and effectiveness.

The Kalman filter is a set of mathematical equations that is a computationally efficient tool for estimation. It provides us with the process status, in this way, it minimizes the mean square error. In some applications, this filter is very strong, it supports the past situation as well as the present and future situation. This mode is accurate for when the system model is also uncertain. The Kalman filter guides general problems trying to estimate the state x so that the discrete-time process is linearly controlled by a stochastic differential equation. The purpose of this article is to correctly track the target used in a radar with the help of the Kalman filter technique and MATLAB. The result of the implementation has been analyzed and planned for flight system maneuvers using a simulated target.

This paper discusses the effect of the horizontal movement of impinging jets on the convective heat transfer from a cylindrical concave surface. In this way, the averaged Navier-Stokes equations for turbulent incompressible flow in a steady state considering two prevalent turbulence models and a Low-Re model with the Yap correction in the 3D computational space were solved. Results indicate that the Yap correction significantly improves the over-prediction of Nusselt number in the impingement zone. The results show that the decrease in the distance of the center of the jets to the outlet edge of the concave surface leads to the transfer of the maximum amount of the Nusselt number to the upstream flow, and the approach of the main flow of the jet to the recirculation region formed in the upstream flow results in the increase in the turbulent kinetic energy of this area and the Nusselt number in the stagnation point.