This article presents the development of an intelligent technique of Adaptive-Neuro-Fuzzy Inference System (ANFIS) based on Maximum Power Point Tracking (ANFIS-MPPT) algorithm with PI controller in order to increase the performances of the photovoltaic panel system below change atmospheric circumstances. In this work, the mathematical principles of the ANFIS method were presented and developed using the software Matlab/Simulink. Moreover, the effectiveness of this ANFIS-MPPT technique is demonstrated by a comparison of the obtained results with others obtained from a classical (Perturb & Observe) P & O-MPPT method.From the analysis of the obtained results, the ANFIS-MPPT command provide better performances, respectable dynamic operations, quicker convergence and fewer fluctuations of working point nearby MPP compared to the classical P & O-MPPT under every irradiance conditions. We have confirmed, here, the capacity of the ANFIS-MPPT technique to increase not only the performance but also the tracking accuracy, speed and system stability under varying climatic conditions. Such improvement would be very beneficial when looking at the significance of the network coupled PV system.

In this work, the differential mobile robot is controlled utilizing fuzzy PID speed control, which combines fuzzy control with conventional PID control in real time. The path may be convoluted, and the surrounding environment may contain a range of arbitrary shape and size obstacles. A monocular camera is used to detect obstacles during the navigation process. To enable a robot to travel within an indoor space while avoiding obstacles, a basic image processing approach based on area of interest was used. The goal of this research is to develop a fuzzy PID speed controller on a real robot, as well as a simple and efficient visual obstacle avoidance system. MATLAB is used to implement the control system. GUIDE (graphical user interface development environment) has enabled the creation of graphical user interfaces. These interfaces make it easy to manipulate the system in real time and capture live video. The proposed methodologies are tested on a non-holonomic dr robot i90 mobile robot, and the results are satisfactory.

The purpose of this article is to present the application of the sliding mode control and investigate its effectiveness when applied to a three-dimensional robotic manipulator model. The analysis is based on the application of the sliding mode control law for the PUMA 560 model, three degrees of freedom, through the development of a dynamic simulation model. The simulation results show the effectiveness of this proposed method for the automation of industrial applications, such as assembly, machining (deburring, trimming), and surface tracking (polishing). This technique provides a useful insight into the advantages of using sliding mode control laws in robotics applications.

The maximum power point tracking (MPPT) plays a very important role to extract the maximum power of the photovoltaic (PV) system by ensuring its optimal production under sunshine and temperature variations. This study presents an algorithm based MPPT named an adaptive neuro-fuzzy inference system (ANFIS) which is built with the combination of the artificial neural network (ANN) and the fuzzy logic controller (FLC). The efficiency of the ANFIS algorithm is tested under Matlab/Simulink and compared with the fixed step conventional perturb and observe (P&O) and the gradient descent techniques under temperature and irradiance change. The obtained results showed a significant improvement in performances of the PV system using the ANFIS-MPPT technique which provides also faster convergence, stability in steady state, less oscillations around the MPP and higher efficiency to track the maximum power from the PV system compared to other techniques under different operating conditions.

The Single-Stage Grid-Connected Solar Photovoltaic (SSGC-SPV) topology has recently gained significant attention, as it offers promising advantages in terms of reducing overall losses and installation costs. We provide a comprehensive overview of the system components, which include the photovoltaic generator, the inverter, the Incremental Conductance Maximum Power Point Tracking (IC-MPPT) algorithm, and the PI regulator for DC bus voltage control. Moreover, this study presents detailed system configurations and control schemes for two types of inverters: 2L$-$3PVSI and 3L$-$3PNPC. In order to perform a comparative study between the two structures, we subjected them to the same irradiation profile using the same grid configuration. The Photovoltaic Array (PVA) irradiance is increased instantaneously, in 0.2 s, from 400 W/m² to 800 W/m², is kept at 800 W/m² for 0.2 s, is then gradually decreased from 800 W/m² to 200 W/m² in 0.2 s, is then kept at 200 W/m² for 0.2 s, and is then finally increased to 1000 W/m² for 0.2 s. We explain the operational principles of these inverters and describe the various switching states involved in generating output voltages. To achieve effective control, we adopt the Finite Set–Model Predictive Control (FS-MPC) algorithm, due to the benefits of excellent dynamic responsiveness and precise current tracking abilities. This algorithm aims to minimise the cost function, while taking into account the dynamic behaviour of both the PV system and the inverter, including any associated delays. To evaluate the performance of the FS-MPC controller, we compare its application in the three-level inverter configuration with the two-level inverter setup. The DC bus voltage is maintained at 615 V using the PI controller. The objective is to achieve a Total Harmonic Distortion (THD) below 5%, with reference to the IEEE standards. The 2L$-$3PVSI inverter is above the threshold at an irradiance of 200 W/m². The 3L$-$3PNPC inverter offers a great THD percentage, meaning improved quality of the power returned to the grid.

In this paper, a study on the Field Programmable Gate Array (FPGA)-based Proportional-Integral-Derivative (PID) control of an Unmanned Aerial Vehicle (UAV) is presented. The goal is to implement four PID controllers in FIL (FPGA In-the-Loop) execution mode using Simulink. These controllers are designed to manage rotation angles and altitude, aiming to stabilize the quadrotor. The controllers were developed within the MATLAB Simulink environment, utilizing blocks supported by HDL Coder. PID parameters were fine-tuned through the PID Tuner feature. The control strategy was subsequently implemented on a Zybo FPGA board, employing the HDL Workflow Advisor to assess the controllers' behavior in FIL mode. The obtained results underscore the effectiveness and performance of the adopted control design.