Mechanical Engineering

The measurement of aerodynamic forces and moments acting on an aircraft model is important for the development of wind tunnel measurement technology. The primary purpose of wind tunnel measurements over the model for the forces and moments is to predict the performance of the full-scale vehicle. Force measurement techniques in wind tunnel testing are necessary for determining a variety of aerodynamic

Abstract: This paper is devoted to the application of Artificial Neural Networks (ANN) to the solution of the Inverse Kinematics (IK) problem for serial robot manipulators, in this study two networks were trained and compared to examine the effect of considering the Jacobian Matrix to the efficiency of the IK solution. Given the desired trajectory of the end effector of the manipulator in a free-of-obstacles workspace, Offline smooth geometric paths in the joint space of the manipulator are obtained.

This paper devoted for the implementation of a learning algorithm, utilized Artificial Neural Network (ANN), to predict the passive joint angular positioning for 3R under-actuated serial robot. Under-actuated system has less number of actuators than the degrees of freedom; therefore, the estimating or modelling of its behaviour is difficult with many uncertainties. Thus, to overcome the disadvantages of several methods reported in literatures. A specific ANN model has been designed and trained to learn a desired set of joint angular positions for the passive joint from a given set of input torques and angular positions for the active joints over a certain period of time. ANN proposes from being free model technique. Consequently, data from sensors fixed on each joints were collected experimentally and provided for the developed ANN model. The learning algorithm can directly determine the position of its passive joint, and can, therefore, completely eliminate the need for any system modelling. Hence, this method could be generalized for the prediction of under-actuated systems behaviour. Results show a successful implementation of the learning algorithm in predicting the behaviour for 3R underactuated robot.

An adoptive learning strategy using an artificial neural network ANN has been proposed here to control the motion of a 6 D.O.F manipulator robot and to overcome the inverse kinematics problem, which are mainly singularities and uncertainties in arm configurations. In this approach a network have been trained to learn a desired set of joint angles positions from a given set of end effector positions, experimental results has shown an excellent mapping over the working area of the robot, to validate the ability of the designed network to make prediction and well generalization for any set of data, a new training using different data set has been performed using the same network, experimental results has shown a good generalization for the new data sets. The proposed control technique does not require any prior knowledge of the kinematics model of the system being controlled, the basic idea of this concept is the use of the ANN to learn the characteristics of the robot system rather than to specify explicit robot system model. Any modification in the physical setup of the robot such as the addition of a new tool would only require training for a new path without the need for any major system software modification, which is a significant advantage of using neural network technology.

Singularities and uncertainties in arm configurations are the main problems in kinematics robot control resulting from applying robot model, a solution based on using Artificial Neural Network (ANN) is proposed here. The main idea of this approach is the use of an ANN to learn the robot system characteristics rather than having to specify an explicit robot system model. Despite the fact that this is very difficult in practice, training data were recorded experimentally from sensors fixed on each joint for a six Degrees of Freedom (DOF) industrial robot. The network was designed to have one hidden layer, where the input were the Cartesian positions along the X, Y and Z coordinates, the orientation according to the RPY representation and the linear velocity of the end-effector while the output were the angular position and velocities for each joint, In a free-of-obstacles workspace, off-line smooth geometric paths in the joint space of the manipulator are obtained. The resulting network was tested for a new set of data that has never been introduced to the network before these data were recorded in the singular configurations, in order to show the generality and efficiency of the proposed approach, and then testing results were verified experimentally.

This paper is devoted to the application of Artificial Neural Networks (ANN) to the solution of the Inverse Kinematics (IK) problem for serial robot manipulators, in this study two networks were trained and compared to examine the effect of considering the Jacobian Matrix to the efficiency of the IK solution. Given the desired trajectory of the end effector of the manipulator in a free-of-obstacles workspace, Offline smooth geometric paths in the joint space of the manipulator are obtained. Even though it is very difficult in practice, data used in this study were recorded experimentally from sensors fixed on robot's joints to overcome the effect of kinematics uncertainties presence in the real world such as ill-defined linkage parameters, links flexibility and backlashes in gear train The generality and efficiency of the proposed algorithm are demonstrated through simulations of a general six DOF serial robot manipulator, finally the obtained results have been verified experime...

This paper compares the performance of Differential Evolution (DE) with Self-Organizing Migrating Algorithm (SOMA) in the task of optimization of the control of chaos. The main aim of this paper is to show that evolutionary algorithms like DE are capable of optimizing chaos control, leading to satisfactory results, and to show that extreme sensitivity of the chaotic environment influences the quality of results on the selected EA, construction of cost function (CF) and any small change in the CF design. As a model of deterministic chaotic system, the two-dimensional Henon map is used and two complex targeting cost functions are tested. The evolutionary algorithms, DE and SOMA were applied with different strategies. For each strategy, repeated simulations demonstrate the robustness of the used method and constructed CF. Finally, the obtained results are compared with previous research.

This paper is devoted to the development and implementation of the neural network technique to solve the forward kinematics problems of robot control, which are mainly singularities and non-linearities. In this paper, a network has been trained to learn the set of end effecter positions X, Y and Z from a given set of joint angle positions for a 6 D.O.F industrial robot. Training data sets were uniformly distributed over a particular region of the robot's working area so that the network can make good generalisation for the intermediate points. Experimental results have shown a good mapping over the working area for the robot. The proposed control technique does not require any prior knowledge of the kinematics model of the system to be controlled; the basic idea of this concept is to use a neural network to learn the characteristics of the robot system rather than having to specify explicit robot system model, which is a significant advantage of using neural network technology. Any modifications in the physical setup of the system would involve only training the robot in a new path without the need for any major software modification.

—A hydraulic servosystem is commonly used in heavy industries in which high power is required. The most widely utilized valve is the solenoid ON/OFF valve because of its simplicity and low cost. However, it has poor controlling performance, which increases the nonlinear behavior occurring in hydraulic systems. This paper devotes on the development and implementation of an adaptive learning algorithm as a real-time controlling algorithm for a hydraulic servosystem utilizing a solenoid valve. The proposed adaptive learning algorithm is a special Artificial Neural Network (ANN) model. ANN is a computer program that provides human brain learning capability to computers for a specific task. In this approach, ANN predicts the controlling action for certain operating parameters learned through a training process. The operating parameters and the result controlling action datasets are collected from experimental operations and then provided to the ANN to learn during the training process. The real-time operating results of the hydraulic servosystem using the adaptive learning algorithm show a compliance of the actuator, which is a linear hydraulic cylinder, to the desired displacements. The controlling through learning concept has the potential to overcome the drawbacks of traditional controllers through the ability to adapt to any changes in the dynamic behavior of a hydraulic servosystem.

The singularity of parallel manipulator has been extensively studied based on the forward and inverse kinematic analysis. In fact, different singularities are different in nature. Some singularities caused by particular configuration, some singularities caused by failure in kinematic model and others caused by architecture of the manipulator. This paper focuses on the architecture singularities analysis. The detection of architecture singularity is very important issue in design process because it causes dangerous situation and lead to ill-condition configuration of parallel manipulator. The quality index used as good index to avoid architecture singularities and obtain on optimal architectural parameters that leading to a good properties for design the manipulator. The effects location of the joints, translation and rotation of the moving platform are analyzed. The technique used for architecture singularity analysis of a 6 DOF parallel manipulator based on neural network algorithm. It is concluded this method decreases the processing time to analyze architecture singularity and get on optimal structural parameters.

This paper presents kinematic analysis of a new 2RPU+RPS parallel manipulator. Because the parallel manipulator has three degree of freedom (DOF), one translation degree of freedom and two rotational degrees of freedom, it has received considerable attention from both researchers and manufacturers over the past years. The geometrical equations of this parallel manipulator are derived, the physical constraints are introduced and the inverse kinematic equations are analyzed. Parasitic motions are occurring in the constrained DOFs of the parallel manipulator is a key issue affecting its application. The effect structural parameters upon kinematics performance of the parallel manipulator are proposed. The simulation results, using MATLAB, testify the validity of the analytic model and illustrate the structural parameters have direct effect upon kinematic performance of the 3-DOF parallel manipulator.

A new three Degree Of Freedom (3-DOF) parallel manipulator has been proposed in this study. Because the parallel manipulator has three Degree Of Freedom (DOF), one translation degree of freedom and two rotational degrees of freedom, it has received considerable attention from both researchers and manufacturers over the past years. The inverse kinematic and Jacobain matrix were derived. The dexterity of the parallel manipulator is presented. The key issue of how the kinematic performance in term of dexterity varies with differences in the structural parameters of the parallel manipulator is investigated. The simulation results, using MATLAB, testify the validity of the analytic model and illustrate the structural parameters have direct effect upon dexterity characteristic of the 3-DOF parallel manipulator.