A New Method for Improving the Fairness of Multi-Robot Task Allocation by Balancing the Distribution of Tasks

This paper presents an innovative task allocation method for multi-robot systems that aims to optimize task distribution while taking into account various performance metrics such as efficiency, speed, and cost. Contrary to conventional approaches, the proposed method takes a comprehensive approach to initialization by integrating the K-means clustering algorithm, the Hungarian method for solving the assignment problem, and a genetic algorithm specifically adapted for Open Loop Travel Sales Man Problem (OLTSP). This synergistic combination allows for a more robust initialization, effectively grouping similar tasks and robots, and laying a strong foundation for the subsequent optimization process. The suggested method is flexible enough to handle a variety of situations, including Multi-Robot System (MRS) with robots that have unique capabilities and tasks of varying difficulty. The method provides a more adaptable and flexible solution than traditional algorithms, which might not be able to adequately address these variations because of the heterogeneity of the robots and the complexity of the tasks. Additionally, ensuring optimal task allocation is a key component of the suggested method. The method efficiently determines the best task assignments for robots through the use of a systematic optimization approach, thereby reducing the overall cost and time needed to complete all tasks. This contrasts with some existing methods that might not ensure optimality or might have limitations in their ability to handle a variety of scenarios. Extensive simulation experiments and numerical evaluations are carried out to validate the method's efficiency. The extensive validation process verifies the suggested approach's dependability and efficiency, giving confidence in its practical applicability

An efficient scanning algorithm for photovoltaic systems under partial shading

This paper proposes a new technique of maximum power point tracking (MPPT) for a photovoltaic (PV) system connected to three phase grids under partial shading condition (PSC), based on a new combined perturb and observe (P&O) with scanning algorithm. This new algorithm main advantages are the high-speed tracking compared to existing algorithms, high accuracy and simplicity which makes it ideal for hardware implementation. Simulation was carried on MATLAB/Simulink. Results showed the effectiveness in speed and accuracy of our algorithm over the existing ones either during standard condition (STC) or PSC. Furthermore, conventional direct power control (DPC) was applied to synchronize successfully the injected power with the grid, which makes our algorithm global and works efficiently under severe conditions

Modelling and Passivity-based Control of a Non-isolated DC-DC Converter in a Fuel Cell System

This paper presents the model of a fuel cell and the design and simulation of a cascade of two DC-DC converters. First, a detailed mathematical model of fuel cell is presented and simulated. Then, a nonlinear model of the whole controlled system is developed and a robust nonlinear controller of currents is synthesized using a passivity-based control. A formal analysis based on Lyapunov stability and average theory is developed to describe the control currents loops performances. A classical PI controller is used for the voltages loops. The simulation models have been developed and tested in the MATLAB/SIMULINK. Simulated results are displayed to validate the feasibility and the effectiveness of the proposed strategy

Integral Backstepping Control for Maximum Power Point Tracking and Unity Power Factor of a Three Phase Grid Connected Photovoltaic System

This paper presents a robust control strategy for a grid connected photovoltaic system with a boost converter by using an integral Backstepping method based on a nonlinear state model, which guarantees the Lyapunov stability of the global system. The system has tracked precisely the maximum power point, with a very fast response and the unit power factor has been observed under different atmospheric conditions. Moreover, the best advantage of the controller is that it’s a good corrector of the grid perturbation and system parameter disturbance. The simulation result has demonstrated the performance of this strategy

A Two-Stage Support Vector Machine and SqueezeNet System for Range-Angle and Range-Speed Estimation in a Cluttered Environment of Automotive MIMO Radar Systems

This paper proposes a two-stage deep-learning approach for frequency modulated continuous waveform multiple‐input multiple‐output (FMCW MIMO) radar embedded in cluttered and jammed environments. The first stage uses the support vector machine (SVM) as a feature extractor that discriminates targets from clutters and jammers. In the second stage, the angle, range, and Doppler estimations of the extracted targets are treated by the SqueezeNet deep convolutional neural network (DCNN) as a multilabel classification problem. The performance of the proposed hybrid SVM-SqueezeNet method is very close to the one achieved by the SqueezeNet only but with the advantage of identifying the type of targets and reducing the training time required by the SqueezeNet

Efficient Control of a Three Phase Grid Connected PV System

This paper presents a new control strategy of a photovoltaic system, which consists of a photovoltaic generator PVG coupled to a three phase load and three phase grid by a three phase voltage source inverter VSI without DC-DC converter. The controller is designed by using Backstepping method based on d-q transformation of a new model of the global system. The main goals of this control strategy are to achieve the maximum power point MPPT with very good precision and the unity power factor in level of the grid power flow. Mathematical analysis demonstrate the asymptotic stability of the controlled system and simulation results proved that the controller has achieved all the objectives with high dynamic performance in presence of atmospheric condition changes. Moreover, the proposed controller shows a very good robustness under system disturbance, which presents the most important advantage of this controller compared to the other control strategies. Furthermore, this controller can operate with a high efficiency with any kind of the load