Journal of Majlesi Journal of Mechatronic Systems
Volume:10 Issue: 3, Sep 2021

Transportation is one of the important cases in flexible manufacturing systems. One of the means of transportation in flexible manufacturing systems is AGVs. Given that path planning of AGVs is of NP-Hard type problems, metaheuristic algorithms or simulation method should be used for the analysis of such problems. In this study, AGVs motion in a complicated network of a manufacturing system was explored and the purpose was to determine the best distribution rule of AGVs in machines and the number of AGVS to decrease the waiting time of parts in warehouses. Considering that the time of manufacturing of the parts by machines as well as AGVs' motion and their Load and Unload time are probable, discrete event simulation is an efficient tool in analysis of the problem. The results of this study revealed efficiency of the proposed method when analytical solution is not possible. 

Noise generation is both simple and very effective in disrupting radar performance. This noise signal, similar to thermal noise, raises the level of the background signal against the target signal and reduces the signal-to-noise ratio, so that the target signal, even if it has a good and strong reflection, will not be detectable. But it has the weakness that its location and direction can be easily detected by radar. In this article, we try to study the methods of interfering with the target radars using electronic attack techniques using electronic warfare and radar systems and compare these methods with each other and examine the advantages and disadvantages of each.

One of the important methods of signal analysis is Fourier series and Fourier transform. use the Fourier series to analyze alternating and periodic signals, and to process non-periodic, use Fourier transform. In many applications, they sample the analog signal from the converter and process the required numerical data. Discrete Fourier transform is used to analyze discrete signals and extract its frequency harmonics. Mostly, this algorithm is implemented on software packages using software such as MATLAB, but hardware implementation has the undeniable benefits such as a much higher speed that makes it suitable for real-time processing. FPGA chips are well-suited platforms for implementing signal processing algorithms such as fast Fourier Transform, due to their advantages such as higher performance and flexibility and parallel processing compared to other hardware packages such as microcontrollers or DSP. In this paper, we implement optimized Fast Fourier Transform algorithm by implementing Verilog hardware on FPGA chip.

The suspension systems are responsible for neutralizing the vibrations caused by the roughness of the road surface imposed on the car. In this paper, a quarter car model (with two degrees of freedom) is employed to investigate the exerted vibrations on the suspension system. After developing the equation of motion, a combination of two fuzzy and robust PID (FRPID) controllers is applied to the system to suppress the vibrations. The coefficients of these controllers are parameters that are optimized by the Whale Optimization Algorithm (WOA). It is observed that the proposed approach is successful to control the car suspension system properly with a very low error. Finally, the proposed controller is compared with a recently published method in the literature. As the results show, the proposed control method in this paper provides better outcomes.

With the growing usage of renewable energy sources in power systems, it is more important than ever to make appropriate use of these resources. As a result, in this paper, we look at the electrical challenges of integrating renewable energy sources into electricity networks from four different perspectives: short circuit level and network strength, operation in island mode, contingency analysis, and operation of renewable energy sources in the presence of energy storage systems. The influence of renewable resources on various metrics has been discussed. In the presence of renewable energy sources and energy storage systems, the studies done in the paper show an overall improvement in the system. However, it emphasizes the significance of assessing grid strength and short circuit levels, as well as the serious concerns that may develop for system operation in terms of power quality and issues with unacceptable voltage levels for the system operator. A basic guideline is provided at the end of the paper to decrease the problems of network functioning in the presence of renewable resources.

This paper presents an optimum network structure based on a BBO tuned adaptive neuro-fuzzy inference system (ANFIS) to control an active suspension system (ASS). The unsupervised learning via Biogeography-Based Optimization (BBO) algorithm is used to train the ANFIS network. The optimal proportional-integral-derivative controller tuned based on the LQR method is used to generate the training data set. ANFIS base on Fuzzy c-means (FCM) clustering algorithm is applied to approximate the relationships between the vehicle body (sprung mass) vertical input velocity and the actuator output force. BBO algorithm is used to optimize fuzzy c means clustering parameters. The numerical simulation results showed that the proposed optimized BBO-FCMANFIS based vehicle suspension system has better performance as compared with the optimal LQR-PID controller under uncertainties in both of reducing actuator energy consumption and the suppression of the vibration of the sprung mass acceleration, with a 43% and 9.5% reduction, respectively.