İbrahim Uzmay

Noise and Vibration Analysis of Car Engines using Proposed Neural Network

An experimental design method for noise and vibration analysis of two car engines by feedforward and radial basis neural networks is presented. Two types of car engines are experimentally analyzed by using intelligent data acquisition card with software. Measured vibration and noise parameters of two car engines are used as desired values of the neural networks. The effectiveness of using Radial Basis Neural Network (RBNN) with backpropagation algorithm is demonstrated for predicting the vibrations and noises of two car engines. The robustness of the proposed RBNN predictor to parameters of vibration and noise as well measurement disturbances is investigated. The result of experiments and simulation show that the proposed RBNN is able to adapt effectively under disturbances.

Neural network applications to vehicle's vibration analysis

Abstract This paper investigates the variation of vertical vibrations of vehicles using a neural network (NN). The NN is a radial basis NN, which is employed to predict the amplitude of acceleration for different road conditions such as concrete, waved stone block paved and country roads. Proposed neural system is also tested for different natural frequencies of the vehicle’s body and the damping ratios of shock absorber. This method is conceptually straightforward, and it is also applicable to other type vehicles for practical purposes.

Application of robust and adaptive control techniques to cooperative manipulation

This paper presents a study on application of adaptive and robust control methods to a cooperative manipulation system, which is developed for handling an object by two-link planar robot manipulators. Adaptive control algorithm ensures a parameter adaptation law satisfying the stability condition of uncertain systems. In designing robust control structure, contact and friction constraints for grasp and bearing conditions, structural flexibility or such similar factors as various unmodeled dynamics are considered as the uncertainties that determine the available values of control parameters. The novelty of results in the present paper is to define new control inputs using the parametric uncertainties and the Lyapunov-based theory of guaranteed stability of uncertain systems.

Upper bounding estimation for robustness to the parameter uncertainty in trajectory control of robot arm

Abstract In this paper, a new robust control law is presented for robot manipulators subjected to uncertainties. Stability of the system is established by the Lyapunov function, and a control law that guaranteed the system stability is derived as a result of analytical solution. Apart from previous studies, uncertainty bound is determined with the estimation law to control the system properly and the estimation law is written as an exponential function of robot kinematics, inertia parameters and tracking error. Due to asymptotic stability and increasing convergence rate, tracking performance for the case of transient and steady-state are increased.

An artificial neural network application to fault detection of a rotor bearing system

Purpose – To improve the application neural networks predictors on bearing systems and to investigate the exact neural model of the ball‐bearing system.Design/methodology/approach – A feed forward neural network is designed to model‐bearing system. Two results are compared for finding the exact model of the system.Findings – The results of the proposed neural network predictor gives superior performance for analysing the behaviour of ball bearing undergoing loading deformation.Research limitations/implications – The results of the proposed neural network exactly follows desired results of the system. Neural network predictor can be employed in practical applications.Practical implications – As theoretical and practical study is evaluated together, it is hoped that ball‐bearing designers and researchers will obtain significant results in this area.Originality/value – This paper fulfils an identified research results need and offers practical investigation for an academic career and research. Also, It shoul...

Parameter estimation and upper bounding adaptation in adaptive-robust control approaches for trajectory control of robots

In this paper a new robust adaptive control law for n-link robot manipulators with parametic uncertainties is derived using the Lyapunov theory thus guaranteed the stability of an uncertain system. The novelty of the adaptive robust control algorithm is that manipulator parameters and adaptive upper bounding functions are estimated to control the system properly, and the adaptive robust control law is also updated as an exponential function of manipulator kinematics, inertia parameters and tracking errors. The proposed adaptive control input includes a parameter estimation law as an adaptive controller and an additional control input vector as a robust controller. The developed approach has the advantages of both adaptive and robust control laws, without their discolour tags.

Variable Upper Bounding Approach for Adaptive-Robust Control in Robot Control

This paper presents a new adaptive-robust control law for robot manipulators with parametric uncertainty. Stability of the uncertain system has been guaranteed using the Lyapunov theory and the control law is derived by means of analytical approach. In this scheme, the manipulator parameters are determined with an estimation law, and both adaptive gain and additional control input are also updated as a function of the estimated value. The proposed adaptive control input includes a parameter estimation law as an adaptive controller and an additional control input vector as a robust controller. The developed approach has the advantages of both adaptive and robust control laws, and besides it eliminates the disadvantages of them.

Statistical analysis of vehicles' vibration due to road roughness using radial basis artificial neural network

This article investigates the variation of vertical vibrations of vehicles using a Radial Basis Neural Network (RBNN). The RBNN is employed to predict desired values of amplitude of acceleration for different road conditions such as concrete, waved stone block paved and country roads. The proposed neural system is also tested for different natural frequencies and the ratios of damping. This method is conceptually straightforward, and it is also applicable to other type vehicles such as trucks.

Kinematic Analysis of Cranes Using Neural Networks

Due to load uncertainties of cranes, it is necessary to find exact kinematic parameters of crane mechanisms. prograrruning techniques [41. The objective function for minimization was taken as the weight of the girder. The limitations on the stresses and the deflections induced in the girder in different load conditions were stated in the form of inequality constraints. This research is concerned with application of neural network to the kinematic analysis of a crane mechanism. The type of network investigated is a Radial Basis Neural Network (RBNN). The crane mechanism is considered as a double-rocker four-bar mechanism. Desired kinematic parameteres of the crane is found by a a software deal ing with simulation and analysis of nrecharmisms . The RBNN is employed in four parameters prediction schemes; displacement, velocity, acceleration and force. The results obtained have supported the theory that the proposed RBNN is able to predict different types of crane system.

Balancing of Planar Mechanisms Having Imperfect Joints Using Neural Network-Genetic Algorithm (NN-GA) Approach

As a result of design, manufacturing and assembly processes or a wear effect, clearances are inevitable at the joints of mechanisms. In this study, dynamic response of mechanism having revolute joints with clearance is investigated. A four-bar mechanism having two revolute joints with clearance is considered as a model mechanism. A neural network was used to model several characteristics of joint clearance. Kinematic and dynamic analyses were achieved using continuous contact mode between journal and bearing. A genetic algorithm was also used to determine the appropriate values of design variables for reducing the additional vibration effects due primarily to the joint clearance. The results show that the optimum adjusting of suitable design variables gives a certain decrease in shaking forces and their moments on the mechanism frame.