Intelligent swarm algorithms for optimizing nonlinear sliding mode controller for robot manipulator

This work introduces an accurate and fast approach for optimizing the parameters of robot manipulator controller. The approach of sliding mode control (SMC) was proposed as it documented an effective tool for designing robust controllers for complex high-order linear and nonlinear dynamic systems operating under uncertain conditions. In this work Intelligent particle swarm optimization (PSO) and social spider optimization (SSO) were used for obtaining the best values for the parameters of sliding mode control (SMC) to achieve consistency, stability and robustness. Additional design of integral sliding mode control (ISMC) was implemented to the dynamic system to achieve the high control theory of sliding mode controller. For designing particle swarm optimizer (PSO) and social spider optimization (SSO) processes, mean square error performances index was considered. The effectiveness of the proposed system was tested with six degrees of freedom robot manipulator by using (PUMA) robot. The iteration of SSO and PSO algorithms with mean square error and objective function were obtained, with best fitness for (SSO) =4.4876 -6 and (PSO)=3.4948 -4

Artificial Intelligent Techniques for Modeling Solar Cell Based on FPGA

Abstract-- The proposed need to construction of a solar tracking system is to extract the majority of solar energy solar panel. Work includes sports simulation and control of solar tracking system for dual- axis solar panel the program has been implemented using MATLAB. The tracking system can be mounted in areas that were considered rich in solar energy. In this work the design of the solar panel Biaxial characterized by the ability to move in the horizontal and vertical directions. Has been used a fuzzy controller which is dominated by the main portion of the solar tracker positioning of the engines that drives the solar panel to face the sun. Mechanical design consists of rotary joints and two engines. The tracking system makes the solar system more efficient by keeping the face of the solar panel perpendicular to the sun and thus extract the majority of solar energy has led to increased overall efficiency. In this work propos a method to track the sun's rays using sensors solar tracker by the sun and by changing the direction of the solar panel in the vertical and horizontal directions by two engines. Require a sun tracker controller effective. The user is controlled and fuzzy logic controller (FLC). The main idea of this work is to build a digital FLC using fuzzy equations and can be implemented on the FPGA in a practical way, and the advantage of this design is the possibility of FLC used for any other application without changing any part in the main design of the FLC only change the external standard inputs and outputs. Field Programmable Gate Arrays (FPGAs) have been used to implement digital fuzzy logic controller, because of their benefits, as well as the reprogrammability of the FPGAs which can support the necessary reconfiguration to program fuzzy logic controller. A VHDL design of digital fuzzy logic controller is proposed to evolve the architecture FLC circuits using FPGA-Spartan-6. The VHDL design platform creates digital fuzzy IJSER logic controller design files using WebPACKTMISE 13.3 program. Index Terms — Sun Tracker, Fuzzy Logic Controller (FLC), Field programmable gate array (FPGA), DC motor

Classification Accuracy Enhancement Based Machine Learning Models and Transform Analysis

The problem of leak detection in water pipeline network can be solved by utilizing a wireless sensor network based an intelligent algorithm. A new novel denoising process is proposed in this work. A comparison study is established to evaluate the novel denoising method using many performance indices. Hardyrectified thresholding with universal threshold selection rule shows the best obtained results among the utilized thresholding methods in the work with Enhanced signal to noise ratio (SNR) = 10.38 and normalized mean squared error (NMSE) = 0.1344. Machine learning methods are used to create models that simulate a pipeline leak detection system. A combined feature vector is utilized using wavelet and statistical factors to improve the proposed system performance