The inverse kinematics of redundant manipulators has infinite solutions by using conventional methods, so that, this work presents applicability of intelligent tool (artificial neural network ANN) for finding one desired solution from these solutions. The inverse analysis and trajectory planning of a three link redundant planar robot have been studied in this work using a proposed dual neural networks model (DNNM), which shows a predictable time decreasing in the training session. The effect of the number of the training sets on the DNNM output and the number of NN layers have been studied. Several trajectories have been implemented using point to point trajectory planning algorithm with DNNM and the result shows good accuracy of the end

In this research, a modified artificial hand with direct control has been designed using electrical artificial muscle wires that receive direct sensory impulses through human hand instead of using the mechanical action to open and close this artificial hand. Each finger is designed as a chain and its movements achieved through the conventional arrangement control of the electrical muscles wires. The results indicate that it is possible to design an artificial hand using electrical muscle wire for control it with high accuracy.

The process of controlling a Flexible Joint Robot Manipulator (FJRM) requires additional sensors for measuring the state variables of flexible joints. Therefore, taking the elasticity into account adds a lot of complexity as all the additional sensors must be taken into account during the control process. This paper proposes a nonlinear observer that controls FJRM, without requiring equipment sensors for measuring the states. The nonlinear state equations are derived in detail for the FJRM where nonlinearity, of order three, is considered. The Takagi–Sugeno Fuzzy Model (T-SFM) technique is applied to linearize the FJRM system. The Luenberger observer is designed to estimate the unmeasured states using error correction. The develop

The robot arm is the most popular robotic form used in industry. Thus, it is crucial to make a system programming which could controlled the movement of each part in the industrial robot to make it works properly. One of the simplest models of the robot arm is EDARM ED-7100 which has a controller to control the movement of the robot arm manually. In this study, the robot controller has been redesigned in order to improve this robot's function. The new controller system used AT89S52 microcontroller which has wire connected to the robot hand. A function has been added with this controller to improve the system of controlling and becomes better than the previous system (only manually). The functions of the new system include three mo

Ball and Plate (B&P) system is a benchmark system in the control engineering field that has been used to verify many control methods. In this paper the design of a sliding mode . controller has been investigated and verified in real-time via implementation on a real ball and plate system hardware. The mathematical model has been derived and the necessary parameters have been measured. The sliding mode controller has been designed based on the obtained mathematical model. The resulting controller has been implemented using the Arduino Mega 2560 and a ball and plate system built completely from scratch. The Arduino has been programmed by the Arduino support target for Simulink. Three test signals has been used for verification purposes

Olmesartan medoxomil (OM) has low bioavailability and limited solubility. To enhance bioavailability, fast dissolving films (FDF) with mixed micelles of soluplus (SPL) and solutol HS15 (STL H15) were developed using solvent casting. The optimised formula, FM2, used polyvinyl alcohol (PVA) and showed high entrapment efficiency, rapid disintegration, and significant improvement in OM bioavailability compared to the market tablet (Olmetec®). FM2 also demonstrated stability and potential for enhanced drug delivery.

Software-defined networks (SDN) have a centralized control architecture that makes them a tempting target for cyber attackers. One of the major threats is distributed denial of service (DDoS) attacks. It aims to exhaust network resources to make its services unavailable to legitimate users. DDoS attack detection based on machine learning algorithms is considered one of the most used techniques in SDN security. In this paper, four machine learning techniques (Random Forest, K-nearest neighbors, Naive Bayes, and Logistic Regression) have been tested to detect DDoS attacks. Also, a mitigation technique has been used to eliminate the attack effect on SDN. RF and KNN were selected because of their high accuracy results. Three types of ne

The objective of this study was tointroduce a recursive least squares (RLS) parameter estimatorenhanced by using a neural network (NN) to facilitate the computing of a bit error rate (BER) (error reduction) during channels estimation of a multiple input-multiple output orthogonal frequency division multiplexing (MIMO-OFDM) system over a Rayleigh multipath fading channel.Recursive least square is an efficient approach to neural network training:first, the neural network estimator learns to adapt to the channel variations then it estimates the channel frequency response. Simulation results show that the proposed method has better performance compared to the conventional methods least square (LS) and the original RLS and it is more robust a