A neural network based robust control system design for the trajectory of Autonomous Underwater V... more A neural network based robust control system design for the trajectory of Autonomous Underwater Vehicles (AUVs) is presented in this paper. Two types of control structure were used to control prescribed trajectories of an AUV. The vehicle was tested with random disturbances while taxiing under water. The results of the simulation showed that the proposed neural network based robust control system has superior performance in adapting to large random disturbances such as underwater flow. It is proved that this kind of neural predictor could be used in real-time AUV applications.
Purpose – To analyse a self-acting parallel surface thrust bearing using a proposed feedforward n... more Purpose – To analyse a self-acting parallel surface thrust bearing using a proposed feedforward neural network. Design/methodology/approach – Firstly, a one-piece hydrodynamic thrust bearing with an initially flat surface is analysed, designed and tested. Analysis of the configuration used is particularly simple and gives good agreement with experimental results. Secondly, some artificial neural network types are designed to analyse minimum film thickness for specified load of thrust bearing system. Findings – A more efficient film shape might result if the length of the cantilever did not increase with radius, since with the configuration used, the deflection of the outer corner was almost three times greater than the deflection of the inner corner, although this effect only becomes acute with regard to film thickness at fairly high loads. The design analysis of an asymmetric cantilever would be more lengthy and less easy to apply. Extrapolation of results for the plain bearing shows that high loads could be carried, but under severe conditions of temperature and clearance. Research limitations/implications – Owing to finance problems, it was not easy to setup system in real time applications. This approach would be given usefulness elsewhere. Practical implications – In future, this technique will be implemented for designing experimental neural network predictor on thrust bearing system. Also, this kind of neural predictor will be suitable for complex bearing systems. Originality/value – A new type of neural network is used to investigate film thickness of thrust bearing system. Quick propagation neural network has given superior performance for designing of model of thrust bearing system. As described and shown in figures and tables, this kind of neural predictor could be employed for analysing such systems in practical analyses.
Journal of Mechanical Science and Technology, 2006
In recent decades, Artificial Neural Networks (ANNs) have become the focus of considerable attent... more In recent decades, Artificial Neural Networks (ANNs) have become the focus of considerable attention in many disciplines, including robot control, where they can be used to solve nonlinear control problems. One of these ANNs applications is that of the inverse kinematic problem, which is important in robot path planning. In this paper, a neural network is employed to analyse of inverse kinematics of PUMA 560 type robot. The neural network is designed to find exact kinematics of the robot. The neural network is a feedforward neural network (FNN). The FNN is trained with different types of learning algorithm for designing exact inverse model of the robot. The Unimation PUMA 560 is a robot with six degrees of freedom and rotational joints. Inverse neural network model of the robot is trained with different learning algorithms for finding exact model of the robot. From the simulation results, the proposed neural network has superior performance for modelling complex robot’s kinematics.
Robotics and Computer-integrated Manufacturing, 2010
Due to a lot of robot manipulators application in industry, low noise degree is very important cr... more Due to a lot of robot manipulators application in industry, low noise degree is very important criteria for robot manipulator's joints. In this paper, joint noise problem of a robot manipulator with five joints is investigated both theoretically and experimentally. The investigation is consisted of two steps. First step is to analyze the noise of joints using a hardware and software. The hardware is a part of noise sensors. The second step; according to experimental results, some neural networks are employed for finding robust neural noise analyzer. Five types of neural networks are used to compare each other. From the results, it is noted that the proposed RBFNN gives the best results for analyzing joint noise of the robot manipulator.
Purpose -To analyse a self-acting parallel surface thrust bearing using a proposed feedforward ne... more Purpose -To analyse a self-acting parallel surface thrust bearing using a proposed feedforward neural network. Design/methodology/approach -Firstly, a one-piece hydrodynamic thrust bearing with an initially flat surface is analysed, designed and tested. Analysis of the configuration used is particularly simple and gives good agreement with experimental results. Secondly, some artificial neural network types are designed to analyse minimum film thickness for specified load of thrust bearing system. Findings -A more efficient film shape might result if the length of the cantilever did not increase with radius, since with the configuration used, the deflection of the outer corner was almost three times greater than the deflection of the inner corner, although this effect only becomes acute with regard to film thickness at fairly high loads. The design analysis of an asymmetric cantilever would be more lengthy and less easy to apply. Extrapolation of results for the plain bearing shows that high loads could be carried, but under severe conditions of temperature and clearance. Research limitations/implications -Owing to finance problems, it was not easy to setup system in real time applications. This approach would be given usefulness elsewhere. Practical implications -In future, this technique will be implemented for designing experimental neural network predictor on thrust bearing system. Also, this kind of neural predictor will be suitable for complex bearing systems. Originality/value -A new type of neural network is used to investigate film thickness of thrust bearing system. Quick propagation neural network has given superior performance for designing of model of thrust bearing system. As described and shown in figures and tables, this kind of neural predictor could be employed for analysing such systems in practical analyses.
Robotics and Computer-integrated Manufacturing, 2011
Nowadays, gas welding applications on vehicle's parts with robot manipulators have increased in a... more Nowadays, gas welding applications on vehicle's parts with robot manipulators have increased in automobile industry. Therefore, the speed of end-effectors of robot manipulator is affected on each joint during the welding process with complex trajectory. For that reason, it is necessary to analyze the noise and vibration of robot's joints for predicting faults. This paper presents an experimental investigation on a robot manipulator, using neural network for analyzing the vibration condition on joints. Firstly, robot manipulator's joints are tested with prescribed of trajectory end-effectors for the different joints speeds. Furthermore, noise and vibration of each joint are measured. And then, the related parameters are tested with neural network predictor to predict servicing period. In order to find robust and adaptive neural network structure, two types of neural predictors are employed in this investigation. The results of two approaches improved that an RBNN type can be employed to predict the vibrations on industrial robots. Crown
Due to health problems on food industry, it is necessary to control exact mixing rate of some fru... more Due to health problems on food industry, it is necessary to control exact mixing rate of some fruit juices. In this study; whole mixing systems with automation is investigated for different flow rates in the pipes. On the other hand, a robust analyzer is designed to predict real time vibrations on the system. Furthermore, from other investigations; neural networks have superior performance to predict such problems. For that reason, three types of neural networks are used to predict vibrations on different points of three tank mixing system. The results are improved that the proposed Radial Basis Neural Network (RBNN) has good performance at adapting vibration problems on mixing system. Finally, this type of neural network will be employed to analyze food industries automation systems.
In this paper, a procedure of testing and evaluation on the sound quality of cars are proposed an... more In this paper, a procedure of testing and evaluation on the sound quality of cars are proposed and sound quality is analysed through the cars' road running test on the providing ground, which was carried out with varying running speed. In addition to this experimental analysis, a neural network predictor is also designed to model the system for possible experimental applications. The proposed neural network is a recurrent type network, which consists of two types of neuron function in the hidden layer. As basic factors for sound quality, only objective factors are considered such as loudness, sharpness, speech intelligibility, and sound pressure level. The correlation between sound pressure level and another factor are discussed from a point of view of running speed dependency. Results of both computer simulations and experiments show that the neural predictor algorithm gives good results at accommodating different cases and provides superior prediction on two cars' sound analysis.
... In system identification applications of neural networks, the main aim is usually to obtain a... more ... In system identification applications of neural networks, the main aim is usually to obtain a dynamically valid model of the system which can be used for system analysis and for controller design ... Tokhi and Wood (1997) have presented a neuro-adaptive active noise control system ...
The main problem of vehicle vibration comes from road roughness. For that reason, it is necessary... more The main problem of vehicle vibration comes from road roughness. For that reason, it is necessary to control vibration of vehicle's suspension by using a robust artificial neural network control system scheme. Neural network based robust control system is designed to control vibration of vehicle's suspensions for full suspension system. Moreover, the full vehicle system has seven degrees of freedom on the vertical direction of vehicle's chassis, on the angular variation around X-axis and on the angular variation around Y-axis. The proposed control system is consisted of a robust controller, a neural controller, a model neural network of vehicle's suspension system. On the other hand, standard PID controller is also used to control whole vehicle's suspension system for comparison.
Journal of Mechanical Science and Technology, 2008
Due to different load conditions on four-bar mechanisms, it is necessary to analyze force distrib... more Due to different load conditions on four-bar mechanisms, it is necessary to analyze force distribution on the bearing systems of mechanisms. A proposed neural network was developed and designed to analyze force distribution on the bearings of a four bar mechanism. The proposed neural network has three layers: input layer, output layer and hidden layer. The hidden layer consists of a recurrent structure to keep dynamic memory for later use. The mechanism is an extended version of a four-bar mechanism. Two elements, spring and viscous, are employed to overcome big force problem on the bearings of the mechanism. The results of the proposed neural network give superior performance for analyzing the forces on the bearings of the four-bar mechanism undergoing big forces and high repetitive motion tracking. This continuation of simulation analysis of bearings should be a benefit to bearing designers and researchers of such mechanisms.
A neural network based robust control system design for the trajectory of Autonomous Underwater V... more A neural network based robust control system design for the trajectory of Autonomous Underwater Vehicles (AUVs) is presented in this paper. Two types of control structure were used to control prescribed trajectories of an AUV. The vehicle was tested with random disturbances while taxiing under water. The results of the simulation showed that the proposed neural network based robust control system has superior performance in adapting to large random disturbances such as underwater flow. It is proved that this kind of neural predictor could be used in real-time AUV applications.
Purpose – To analyse a self-acting parallel surface thrust bearing using a proposed feedforward n... more Purpose – To analyse a self-acting parallel surface thrust bearing using a proposed feedforward neural network. Design/methodology/approach – Firstly, a one-piece hydrodynamic thrust bearing with an initially flat surface is analysed, designed and tested. Analysis of the configuration used is particularly simple and gives good agreement with experimental results. Secondly, some artificial neural network types are designed to analyse minimum film thickness for specified load of thrust bearing system. Findings – A more efficient film shape might result if the length of the cantilever did not increase with radius, since with the configuration used, the deflection of the outer corner was almost three times greater than the deflection of the inner corner, although this effect only becomes acute with regard to film thickness at fairly high loads. The design analysis of an asymmetric cantilever would be more lengthy and less easy to apply. Extrapolation of results for the plain bearing shows that high loads could be carried, but under severe conditions of temperature and clearance. Research limitations/implications – Owing to finance problems, it was not easy to setup system in real time applications. This approach would be given usefulness elsewhere. Practical implications – In future, this technique will be implemented for designing experimental neural network predictor on thrust bearing system. Also, this kind of neural predictor will be suitable for complex bearing systems. Originality/value – A new type of neural network is used to investigate film thickness of thrust bearing system. Quick propagation neural network has given superior performance for designing of model of thrust bearing system. As described and shown in figures and tables, this kind of neural predictor could be employed for analysing such systems in practical analyses.
Journal of Mechanical Science and Technology, 2006
In recent decades, Artificial Neural Networks (ANNs) have become the focus of considerable attent... more In recent decades, Artificial Neural Networks (ANNs) have become the focus of considerable attention in many disciplines, including robot control, where they can be used to solve nonlinear control problems. One of these ANNs applications is that of the inverse kinematic problem, which is important in robot path planning. In this paper, a neural network is employed to analyse of inverse kinematics of PUMA 560 type robot. The neural network is designed to find exact kinematics of the robot. The neural network is a feedforward neural network (FNN). The FNN is trained with different types of learning algorithm for designing exact inverse model of the robot. The Unimation PUMA 560 is a robot with six degrees of freedom and rotational joints. Inverse neural network model of the robot is trained with different learning algorithms for finding exact model of the robot. From the simulation results, the proposed neural network has superior performance for modelling complex robot’s kinematics.
Robotics and Computer-integrated Manufacturing, 2010
Due to a lot of robot manipulators application in industry, low noise degree is very important cr... more Due to a lot of robot manipulators application in industry, low noise degree is very important criteria for robot manipulator's joints. In this paper, joint noise problem of a robot manipulator with five joints is investigated both theoretically and experimentally. The investigation is consisted of two steps. First step is to analyze the noise of joints using a hardware and software. The hardware is a part of noise sensors. The second step; according to experimental results, some neural networks are employed for finding robust neural noise analyzer. Five types of neural networks are used to compare each other. From the results, it is noted that the proposed RBFNN gives the best results for analyzing joint noise of the robot manipulator.
Purpose -To analyse a self-acting parallel surface thrust bearing using a proposed feedforward ne... more Purpose -To analyse a self-acting parallel surface thrust bearing using a proposed feedforward neural network. Design/methodology/approach -Firstly, a one-piece hydrodynamic thrust bearing with an initially flat surface is analysed, designed and tested. Analysis of the configuration used is particularly simple and gives good agreement with experimental results. Secondly, some artificial neural network types are designed to analyse minimum film thickness for specified load of thrust bearing system. Findings -A more efficient film shape might result if the length of the cantilever did not increase with radius, since with the configuration used, the deflection of the outer corner was almost three times greater than the deflection of the inner corner, although this effect only becomes acute with regard to film thickness at fairly high loads. The design analysis of an asymmetric cantilever would be more lengthy and less easy to apply. Extrapolation of results for the plain bearing shows that high loads could be carried, but under severe conditions of temperature and clearance. Research limitations/implications -Owing to finance problems, it was not easy to setup system in real time applications. This approach would be given usefulness elsewhere. Practical implications -In future, this technique will be implemented for designing experimental neural network predictor on thrust bearing system. Also, this kind of neural predictor will be suitable for complex bearing systems. Originality/value -A new type of neural network is used to investigate film thickness of thrust bearing system. Quick propagation neural network has given superior performance for designing of model of thrust bearing system. As described and shown in figures and tables, this kind of neural predictor could be employed for analysing such systems in practical analyses.
Robotics and Computer-integrated Manufacturing, 2011
Nowadays, gas welding applications on vehicle's parts with robot manipulators have increased in a... more Nowadays, gas welding applications on vehicle's parts with robot manipulators have increased in automobile industry. Therefore, the speed of end-effectors of robot manipulator is affected on each joint during the welding process with complex trajectory. For that reason, it is necessary to analyze the noise and vibration of robot's joints for predicting faults. This paper presents an experimental investigation on a robot manipulator, using neural network for analyzing the vibration condition on joints. Firstly, robot manipulator's joints are tested with prescribed of trajectory end-effectors for the different joints speeds. Furthermore, noise and vibration of each joint are measured. And then, the related parameters are tested with neural network predictor to predict servicing period. In order to find robust and adaptive neural network structure, two types of neural predictors are employed in this investigation. The results of two approaches improved that an RBNN type can be employed to predict the vibrations on industrial robots. Crown
Due to health problems on food industry, it is necessary to control exact mixing rate of some fru... more Due to health problems on food industry, it is necessary to control exact mixing rate of some fruit juices. In this study; whole mixing systems with automation is investigated for different flow rates in the pipes. On the other hand, a robust analyzer is designed to predict real time vibrations on the system. Furthermore, from other investigations; neural networks have superior performance to predict such problems. For that reason, three types of neural networks are used to predict vibrations on different points of three tank mixing system. The results are improved that the proposed Radial Basis Neural Network (RBNN) has good performance at adapting vibration problems on mixing system. Finally, this type of neural network will be employed to analyze food industries automation systems.
In this paper, a procedure of testing and evaluation on the sound quality of cars are proposed an... more In this paper, a procedure of testing and evaluation on the sound quality of cars are proposed and sound quality is analysed through the cars' road running test on the providing ground, which was carried out with varying running speed. In addition to this experimental analysis, a neural network predictor is also designed to model the system for possible experimental applications. The proposed neural network is a recurrent type network, which consists of two types of neuron function in the hidden layer. As basic factors for sound quality, only objective factors are considered such as loudness, sharpness, speech intelligibility, and sound pressure level. The correlation between sound pressure level and another factor are discussed from a point of view of running speed dependency. Results of both computer simulations and experiments show that the neural predictor algorithm gives good results at accommodating different cases and provides superior prediction on two cars' sound analysis.
... In system identification applications of neural networks, the main aim is usually to obtain a... more ... In system identification applications of neural networks, the main aim is usually to obtain a dynamically valid model of the system which can be used for system analysis and for controller design ... Tokhi and Wood (1997) have presented a neuro-adaptive active noise control system ...
The main problem of vehicle vibration comes from road roughness. For that reason, it is necessary... more The main problem of vehicle vibration comes from road roughness. For that reason, it is necessary to control vibration of vehicle's suspension by using a robust artificial neural network control system scheme. Neural network based robust control system is designed to control vibration of vehicle's suspensions for full suspension system. Moreover, the full vehicle system has seven degrees of freedom on the vertical direction of vehicle's chassis, on the angular variation around X-axis and on the angular variation around Y-axis. The proposed control system is consisted of a robust controller, a neural controller, a model neural network of vehicle's suspension system. On the other hand, standard PID controller is also used to control whole vehicle's suspension system for comparison.
Journal of Mechanical Science and Technology, 2008
Due to different load conditions on four-bar mechanisms, it is necessary to analyze force distrib... more Due to different load conditions on four-bar mechanisms, it is necessary to analyze force distribution on the bearing systems of mechanisms. A proposed neural network was developed and designed to analyze force distribution on the bearings of a four bar mechanism. The proposed neural network has three layers: input layer, output layer and hidden layer. The hidden layer consists of a recurrent structure to keep dynamic memory for later use. The mechanism is an extended version of a four-bar mechanism. Two elements, spring and viscous, are employed to overcome big force problem on the bearings of the mechanism. The results of the proposed neural network give superior performance for analyzing the forces on the bearings of the four-bar mechanism undergoing big forces and high repetitive motion tracking. This continuation of simulation analysis of bearings should be a benefit to bearing designers and researchers of such mechanisms.
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