İkbal Eski

Vibration control of vehicle active suspension system using a new robust neural network control system

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. Consequently, random road roughnesses are used as disturbance of control system. The simulation results are indicated that the proposed control system has superior performance at adapting random road disturbance for vehicle’s suspension.

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.

Sound quality analysis of cars using hybrid neural networks

Abstract 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.

Vibration analysis of an experimental suspension system using artificial neural networks

This study analyzes effects of vibrations on comfort and road holding capability of vehicles as observed in variations of suspension springs, road roughness etc. Also, design of non-linear experimental car suspension system for ride qualities using neural networks is presented. Proposed active suspension system has been found more effective in vibration isolation of car body than linear active suspension system. Proposed neural network predictor could be used in vehicle’ s suspension vibration analysis .

Design of adaptive neural predictor for failure analysis on hip and knee joints of humans

Due to recent heart attacks on humans, it is necessary to predict heart graphs of humans during running positions. On the other hand, hip and knee joints should be analyzed to predict walking and running conditions. Therefore, in this experimental works, hip, knee, and heart attacks are analyzed in experimentally. After experimental measurement, a proposed neural network is employed to predict hip, knee, and heart attack behavior of humans with walking and running stages.

Neural network-based fuzzy inference system for speed control of heavy duty vehicles with electronic throttle control system

Abstract The objective of this study is to apply various control approaches to control the speed of a heavy duty vehicle using an electronic throttle control system. However, the DC servo motor is used for controlling the angular position of electronic throttle valve. Moreover, four control techniques are used to control prescribed two different random inputs of the heavy duty vehicle speed. These control structures are named as standard PID controller, model-based neural network controller, adaptive neural network-based fuzzy inference controller and proposed robust adaptive neural-based fuzzy inference control systems. On the other hand, the time performance specifications such as rise time, settling time, peak time, peak value and steady-state error are also examined for these control approaches. The results of the simulation for four approaches showed that the proposed robust adaptive neural network-based fuzzy inference control system has better performance rather than other standard control systems under varying speed conditions. Finally, the proposed control system structure will be implemented for speed control of DC servo motor.

Vibration analysis of food industries mixing systems for long life using neural networks

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.

Design of artificial neural network model for analysing of a self‐acting parallel surface thrust bearing

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 ...

Controller design for upper limb motion using measurements of shoulder, elbow and wrist joints

Passive exercises are implemented by physiotherapists to people who have movement functionality loss due to any reason at their limbs for recovering all or a part of movement functionality. Physiotherapists are constantly repeating the passive therapy movements with patients. Nowadays, rehabilitation devices which are capable of repeating therapy exercises and control techniques for that device are developing. For this reason, controller design for the angular trajectory of human shoulder, elbow and wrist joints is presented in this paper. This paper consists of two parts. At first part, patients who had loss of function at their upper limbs were identified and shoulder, elbow and wrist angular displacement parameters were obtained by means of sensors. At second part, three control structures were selected for tracking shoulder, elbow and wrist angle trajectories. According to the experimental and simulation results, neural network-based PID control structure is the best controller on tracking given trajectories.

Correction to: Controller design for upper limb motion using measurements of shoulder, elbow and wrist joints

In the original publication, the second author affiliation was incorrectly published. The first and second author affiliation remains the same.