Quadrotor is one of the types of flying robots that has attracted the attention of researchers due to its simple structure and perpendicular flight capability. This paper presents a new method based on machine vision for correct window detection, in smoothly unknown environments. One of the challenges of controlling the Quadrotor path in unknown environments is actually accurate window identification for passing through it. In this study, quadrotor Parrot Bebop2 is used which is equipped with a camera. Also, an algorithm is proposed to perform image processing to identify the window in the environment and control the quadrotor's trajectory, which is implemented on the quadrotor. This method consists of three parts: preprocessing, diagnosis and identification. First, by applying image processing algorithms, we improve the image and delete the data unrelated to the target, and then we use a smart machine vision algorithm to extract information. Furthermore, to control the quadrotor route, a proportional-integral-derivative controller is designed and implemented using Ziegler and Nichols method, which will take place during a real indoor flight in an automated tracking. According to the obtained results, it can be concluded that the use of flying robots can have positive results in military processes and assistance to people in a short time. Manuscript profile

In this paper, an adaptive-neural free model scheme is proposed to control a widely-used nonlinear multivariable industrial system, a quadruple-tank process (QTP). The system consists of four tanks that are arranged in two upper and two lower formations. The main objective is defined as maintaining the level of the liquid in lower tanks via two pumps. Controlling this system is not an easy task since it has nonlinear dynamics, strong interaction between different channels, and highly interacted input and output variables. In the adaptive part of the proposed controller, the parameters and rules obtained from Lyapunov stability analysis, along with the estimation of nonlinear functions performed with the neural network, constitute the controller design steps. To highlight the controller's abilities, an additional object is defined, which is controlling the temperature of liquid of those two tanks by adding a heater to the QTP system as a modified system. Obviously, the interactions amongst the control loops are multiplied because the modified quadruple tank process (MQTP) system has four inputs and four outputs. One of the main contributions of this paper is the implementation of the closed-loop system. Regarding the importance of such a system in the industry and to test the controller practically, the closed-loop system is implemented in an industrial automation environment with the connection of Process Control System SIMATIC (PCS7) industrial software to MATLAB with Open Platform Communications (OPC) protocol. The effectiveness of the introduced scheme is verified by performing some experimental validation. Manuscript profile