Speed Control of Travelling Wave Type Ultrasonic Motors Using Artificial Neural Network

Ultrasonic motor (USM) is a newly developed motor, and it has some excellent performances and useful features, therefore, it has been expected to be of practical use. However, the driving principle of USM is different from that of other electromagnetic type motors, and the mathematical model is complex to apply to motor control. Furthermore, the speed characteristics of the motor have heavy nonlinearity and vary with driving conditions. Hence, the precise speed control of USM is generally difficult. This paper proposes a new speed control scheme for USM using an artificial neural network. An accurate tracking response can be obtained by random initialization of the weights of the network owing to the powerful on line learning capability. Two prototype ultrasonic motors of travelling wave type were fabricated, both having 100 mm outer diameters of stator and piezoelectric ceramic. The usefulness and validity of the proposed control scheme are examined in experiments.

ARTIFICIAL NEURAL NETWORK AND FUZZY LOGIC CONTROLLER FOR GTAW MODELING AND CONTROL

An artificial neural network(ANN) and a self-adjusting fuzzy logiccontroller(FLC) for modeling and control of gas tungsten arc welding(GTAW) process are presented.The discussion is mainly focused on the modeling and control of the weld pool depth with ANN and theintelligent control for weld seam tracking with FLC. The proposed neural network can produce highlycomplex nonlinear multi-variable model of the GTAW process that offers the accurate prediction ofwelding penetration depth. A self-adjusting fuzzy controller used for seam tracking adjusts thecontrol parameters on-line automatically according to the tracking errors so that the torch positioncan be controlled accurately.

Study on optimization control method based on artificial neural network

In the goal optimization and control optimization process the problems with common artificial neural network algorithm are unsure convergence, insufficient post-training network precision, and slow training speed, in which partial minimum value question tends to occur. This paper conducted an in-depth study on the causes of the limi-tations of the algorithm, presented a rapid artificial neural network algorithm, which is characterized by integrating multiple algorithms and by using their complementary advan-tages. The salient feature of the method is self-organization, which can effectively prevent the optimized results from tending to be partial minimum values. Overall optimization can be achieved with this method, goal function can be searched for in overall scope. With op-timization control of coal mine ventilator as a practical application, the paper proves that by integrating multiple artificial neural network algorithms, best control optimization and goal optimized can be achieved.

A Review: Artificial Neural Networks as Tool for Control Food Industry Process

In the last year, interest in using Artificial Neural networks as a modeling tool in food technology is increasing because they have found extensive utilization in solving many complex real world problems. Due to this and as previous step at development of some project, this paper intends to introduce the reader inside neural networks: general characteristics of the ANN, their architectures, their rules of learning, types of networks and ANN’s create process. Also this paper presents a comprehensive review of food industrial applications of artificial neural networks in the last year. ANN industrial applications are grouped and tabulated by their main functions and what they actually performed on the referenced papers with except the applications in the olive oil industry that are described with special emphasis.

Implementation of Radial Basis Function Artificial Neural Network into an Adaptive Equivalent Consumption Minimization Strategy for Optimized Control of a Hybrid Electric Vehicle

Continued increases in the emission of greenhouse gases by passenger vehicles have accelerated the production of hybrid electric vehicles. With this increase in production, there has been a parallel demand for continuously improving strategies of hybrid electric vehicle control. The goal of an ideal control strategy is to maximize fuel economy while minimizing emissions. Methods exist by which the globally optimal control strategy may be found. However, these methods are not applicable in real-world driving applications since these methods require a priori knowledge of the upcoming drive cycle. Real-time control strategies use the global optimal as a benchmark against which performance can be evaluated. The goal of this work is to use a previously defined strategy that has been shown to closely approximate the global optimal and implement a radial basis function (RBF) artificial neural network (ANN) that dynamically adapts the strategy based on past driving conditions. The strategy used is the Equivalent Consumption Minimization Strategy (ECMS), which uses an equivalence factor to define the control strategy and the power train component torque split. An equivalence factor that is optimal for a single drive cycle can be found offline with a priori knowledge of the drive cycle. The RBF-ANN is used to dynamically update the equivalence factor by examining a past time window of driving characteristics. A total of 30 sets of training data (drive cycles) are used to train the RBF-ANN. For the majority of drive cycles examined, the RBF-ANN implementation is shown to produce fuel economy values that are within ±2.5% of the fuel economy obtained with the optimal equivalence factor. The advantage of the RBF-ANN is that it does not require a priori drive cycle knowledge and is able to be implemented in real-time while meeting or exceeding the performance of the optimal ECMS. Recommendations are made on how the RBF-ANN could be improved to produce better results across a greater array of driving conditions.

NONLINEAR MODELING AND CONTROLLING OF ARTIFICIAL MUSCLE SYSTEM USING NEURAL NETWORKS

The pneumatic artificial muscles are widely used in the fields of medicalrobots, etc. Neural networks are applied to modeling and controlling of artificial muscle system. Asingle-joint artificial muscle test system is designed. The recursive prediction error (RPE)algorithm which yields faster convergence than back propagation (BP) algorithm is applied to trainthe neural networks. The realization of RPE algorithm is given. The difference of modeling ofartificial muscles using neural networks with different input nodes and different hidden layer nodesis discussed. On this basis the nonlinear control scheme using neural networks for artificialmuscle system has been introduced. The experimental results show that the nonlinear control schemeyields faster response and higher control accuracy than the traditional linear control scheme.

Artificial neural network algorithm for pulse shape discrimination in 2πα and 2πβ particle surface emission rate measurements

To enhance the accuracy of 2πα and 2πβ particle surface emission rate measurements and address the identification issues of nuclides in conventional methods, this study introduces two artificial neural network(ANN) algorithms: back-propagation(BP) and genetic algorithm-based back-propagation(GA-BP). These algorithms classify pulse signals from distinct α and β particles. Their discrimination efficacy is assessed by simulating standard pulse signals and those produced by contaminated sources, mixing α and β particles within the detector. This study initially showcases energy spectrum measurement outcomes, subsequently tests the ANNs on the measurement and validation datasets, and contrasts the pulse shape discrimination efficacy of both algorithms. Experimental findings reveal that the proportional counter's energy resolution is not ideal, thus rendering energy analysis insufficient for distinguishing between 2πα and 2πβ particles. The BP neural network realizes approximately 99% accuracy for 2πα particles and approximately 95% for 2πβ particles, thus surpassing the GA-BP's performance. Additionally, the results suggest enhancing β particle discrimination accuracy by increasing the digital acquisition card's threshold lower limit. This study offers an advanced solution for the 2πα and 2πβ surface emission rate measurement method, presenting superior adaptability and scalability over conventional techniques.

Application of Artificial Neural Networks Based Monte Carlo Simulation in the Expert System Design and Control of Crude Oil Distillation Column of a Nigerian Refinery

This research work investigated comparative studies of expert system design and control of crude oil distillation column (CODC) using artificial neural networks based Monte Carlo (ANNBMC) simulation of random processes and artificial neural networks (ANN) model which were validated using experimental data obtained from functioning crude oil distillation column of Port-Harcourt Refinery, Nigeria by MATLAB computer program. Ninety percent (90%) of the experimental data sets were used for training while ten percent (10%) were used for testing the networks. The maximum relative errors between the experimental and calculated data obtained from the output variables of the neural network for CODC design were 1.98 error % and 0.57 error % when ANN only and ANNBMC were used respectively while their respective values for the maximum relative error were 0.346 error % and 0.124 error % when they were used for the controller prediction. Larger number of iteration steps of below 2500 and 5000 were required to achieve convergence of less than 10-7?for the training error using ANNBMC for both the design of the CODC and controller respectively while less than 400 and 700 iteration steps were needed to achieve convergence of 10-4?using ANN only. The linear regression analysis performed revealed the minimum and maximum prediction accuracies to be 80.65% and 98.79%;and 98.38% and 99.98% when ANN and ANNBMC were used for the CODC design respectively. Also, the minimum and maximum prediction accuracies were 92.83% and 99.34%;and 98.89% and 99.71% when ANN and ANNBMC were used for the CODC controller respectively as both methodologies have excellent predictions. Hence, artificial neural networks based Monte Carlo simulation is an effective and better tool for the design and control of crude oil distillation column.

Comparative Analysis between Conventional PI, Fuzzy Logic and Artificial Neural Network Based Speed Controllers of Induction Motor with Considering Core Loss and Stray Load Loss

Most of the controllers of IM （induction motor） for industrial applications have been designed based on PI controller without consideration of CL （core loss） and SLL （stray load loss）. To get the precise performances of torque as well as rotor speed and flux, the above mentioned losses should be considered. Conventional PI controller has overshoot effect at the transient period of the speed response curve. On the other hand, fuzzy logic and ANN （artificial neural network） based controllers can minimize the overshoot effect at the transient period because they have the abilities to deal with the nonlinear systems. In this paper, a comparative analysis is done between PI, fuzzy logic and ANN based speed controllers to find the suitable control strategy for IM with consideration of CL and SLL. The simulation analysis is done by using Matlab/Simulink software. The simulation results show that the fuzzy logic based speed controller gives better responses than ANN and conventional PI based speed controllers in terms of rotor speed, electromagnetic torque and rotor flux of IM.

Artificial Neural Networks for Controlling the Temperature of Internally Cooled Turning Tools

By eliminating the need for externally applied coolant, internally cooled turning tools offer potential health, safety and cost benefits in many types of machining operation. As coolant flow is completely controlled, tool temperature measurement becomes a practical proposition and can be used to find and maintain the optimum machining conditions. This also requires an intelligent control system in the sense that it must be adaptable to different tool designs, work piece materials and machining conditions. In this paper, artificial neural networks (ANN) are assessed for their suitability to perform such a control function. Experimental data for both conventional tools used for dry machining and internally cooled tools is obtained and used to optimise the design of an ANN. A key finding is that both experimental scatter characteristic of turning and the range of machining conditions for which ANN control is required have a large effect on the optimum ANN design and the amount of data needed for its training. In this investigation, predictions of tool temperature with an optimised ANN were found to be within 5°C of measured values for operating temperatures of up to 258°C. It is therefore concluded that ANN’s are a viable option for in-process control of turning processes using internally controlled tools.

Predictive active control of building structures using LQR and artificial intelligence

This study presents a neural network-based model for predicting linear quadratic regulator(LQR)weighting matrices for achieving a target response reduction.Based on the expected weighting matrices,the LQR algorithm is used to determine the various responses of the structure.The responses are determined by numerically analyzing the governing equation of motion using the state-space approach.For training a neural network,four input parameters are considered:the time history of the ground motion,the percentage reduction in lateral displacement,lateral velocity,and lateral acceleration,Output parameters are LQR weighting matrices.To study the effectiveness of an LQR-based neural network(LQRNN),the actual percentage reduction in the responses obtained from using LQRNN is compared with the target percentage reductions.Furthermore,to investigate the efficacy of an active control system using LQRNN,the controlled responses of a system are compared to the corresponding uncontrolled responses.The trained neural network effectively predicts weighting parameters that can provide a percentage reduction in displacement,velocity,and acceleration close to the target percentage reduction.Based on the simulation study,it can be concluded that significant response reductions are observed in the active-controlled system using LQRNN.Moreover,the LQRNN algorithm can replace conventional LQR control with the use of an active control system.

Application of Smith Predictor Based on Single Neural Network in Cold Rolling Shape Control

Flatness is one of the most important criterion factors to evaluate the quality of the steel strip. To improve the strip＇ s flatness quality, the most frequently used methodology is to employ the closed-loop automatic shape control system. However, in the shape control system, the shape-meter is always installed at the down way of the exit of the cold rolling mill and can not sense the changes of the strip flatness in the rolling gap directly. This kind of installation results in the delay of the feedback in the control system. Therefore, the stability and response performance of the system are strongly affected by the delay. At present, there is still no mature way to design controllers for systems with time delay. Although the conventional PID controller used in most practical applications has the capability to compensate the delay, the effect of the compensation is limited, especially for the systems with long time delay. Smith predictor, as a compensator for solving this problem, is now widely used in industry systems. However, the request of highly precise model of the system and the poor adaptive performance to the changes of related parameters limit the application of the Smith predictor in practice. In order to overcome the drawbacks of the Smith predictor, a new Smith predictor based on single neural network PID （SNN-PID） is proposed. Because the single neural network is employed into the Smith predictor to improve the controller＇s self-adaptability, the adaptive capability to the varying parameters of the system is improved. Meanwhile, for the purpose of solving the problems such as time-consuming and complicated calculation of the neural networks in real time, the learning coefficient of neural network is divided into several stages as usually done in expert control system. Therefore, the control system can obtain fast response due to the improved calculation speed of the neural networks. In order to validate the performance of the proposed controller, the experiment is conducted on the shape control system in a 300 mm four-high reversing cold rolling mill. The experimental results show that the SNN-PID with Smith predictor controller can effectively compensate the delay effects and achieve better control performance than the conventional PID controller.

Flame image recognition of alumina rotary kiln by artificial neural network and support vector machine methods

Based on the Fourier transform, a new shape descriptor was proposed to represent the flame image. By employing the shape descriptor as the input, the flame image recognition was studied by the methods of the artificial neural network(ANN) and the support vector machine(SVM) respectively. And the recognition experiments were carried out by using flame image data sampled from an alumina rotary kiln to evaluate their effectiveness. The results show that the two recognition methods can achieve good results, which verify the effectiveness of the shape descriptor. The highest recognition rate is 88.83% for SVM and 87.38% for ANN, which means that the performance of the SVM is better than that of the ANN.

Resource Exhaustion Attack Detection Scheme for WLAN Using Artificial Neural Network

IEEE 802.11 Wi-Fi networks are prone to many denial of service(DoS)attacks due to vulnerabilities at the media access control(MAC)layer of the 802.11 protocol.Due to the data transmission nature of the wireless local area network(WLAN)through radio waves,its communication is exposed to the possibility of being attacked by illegitimate users.Moreover,the security design of the wireless structure is vulnerable to versatile attacks.For example,the attacker can imitate genuine features,rendering classificationbased methods inaccurate in differentiating between real and false messages.Althoughmany security standards have been proposed over the last decades to overcome many wireless network attacks,effectively detecting such attacks is crucial in today’s real-world applications.This paper presents a novel resource exhaustion attack detection scheme(READS)to detect resource exhaustion attacks effectively.The proposed scheme can differentiate between the genuine and fake management frames in the early stages of the attack such that access points can effectively mitigate the consequences of the attack.The scheme is built through learning from clustered samples using artificial neural networks to identify the genuine and rogue resource exhaustion management frames effectively and efficiently in theWLAN.The proposed scheme consists of four modules whichmake it capable to alleviates the attack impact more effectively than the related work.The experimental results show the effectiveness of the proposed technique by gaining an 89.11%improvement compared to the existing works in terms of detection.

Fuzzy Control System of Hydraulic Roll Bending Based on Genetic Neural Network

For nonlinear hydraulic roll bending control, a new fuzzy intelligent control method was proposed based on the genetic neural network. The method taking account of dynamic and static characteristics of control system has settled the problems of recognizing and controlling the unknown, uncertain and nonlinear system successfully, and has been applied to hydraulic roll bending control. The simulation results indicate that the system has good performance and strong robustness, and is better than traditional PID and neural-fuzzy control. The method is an effective tool to control roll bending force with increased dynamic response speed of control system and enhanced tracking accuracy.

Automated Identification of Basic Control Charts Patterns Using Neural Networks

The identification of control chart patterns is very important in statistical process control. Control chart patterns are categorized as natural and unnatural. The presence of unnatural patterns means that a process is out of statistical control and there are assignable causes for process variation that should be investigated. This paper proposes an artificial neural network algorithm to identify the three basic control chart patterns;natural, shift, and trend. This identification is in addition to the traditional statistical detection of runs in data, since runs are one of the out of control situations. It is assumed that a process starts as a natural pattern and then may undergo only one out of control pattern at a time. The performance of the proposed algorithm was evaluated by measuring the probability of success in identifying the three basic patterns accurately, and comparing these results with previous research work. The comparison showed that the proposed algorithm realized better identification than others.

Intelligent vehicle lateral control based on radial basis function neural network sliding mode controller

Based on the predigestion of the dynamic model of the intelligent firefighting vehicle,a linear 2-DOF lateral dynamic model and a preview error model are established.To solve the problems of a highly non-linear vehicle model,time-varying parameters,output chattering,and poor robustness,the Radial Basis Function neural network sliding mode controller is designed.Then,different driving speeds are used to conduct simulation tests under standard double-shifting and smooth curve road conditions,and the simulation results are used to analyse the tracking effect of the lateral motion controller on the desired path.The simulation results reveal that the controller designed has high accuracy in tracking the desired path and has good robustness to the disturbance of intelligent firefighting vehicle speed changes.

Hybrid Power Systems Energy Controller Based on Neural Network and Fuzzy Logic

This paper presents a novel adaptive scheme for energy management in stand-alone hybrid power systems. The proposed management system is designed to manage the power flow between the hybrid power system and energy storage elements in order to satisfy the load requirements based on artificial neural network (ANN) and fuzzy logic controllers. The neural network controller is employed to achieve the maximum power point (MPP) for different types of photovoltaic (PV) panels. The advance fuzzy logic controller is developed to distribute the power among the hybrid system and to manage the charge and discharge current flow for performance optimization. The developed management system performance was assessed using a hybrid system comprised PV panels, wind turbine (WT), battery storage, and proton exchange membrane fuel cell (PEMFC). To improve the generating performance of the PEMFC and prolong its life, stack temperature is controlled by a fuzzy logic controller. The dynamic behavior of the proposed model is examined under different operating conditions. Real-time measured parameters are used as inputs for the developed system. The proposed model and its control strategy offer a proper tool for optimizing hybrid power system performance, such as that used in smart-house applications.

Modeling and Control Maximum Power Point Tracking of an Autonomous Photovoltaic System Using Artificial Intelligence

Despite investigative efforts seen in the literature, the maximum power point tracking remains again a crucial problem in photovoltaic system (PV) connected to the power grid. In this paper, a new maximum power point tracking technique which is our contribution to the resolution of this problem is treated. We proposed a hybrid controller of maximum power point tracking based on artificial neural networks. This hybrid controller is composed of two neural networks. The first network has two inputs and two outputs: the inputs are solar irradiation and ambient temperature and the outputs are the reference output voltage and current corresponding at the maximum power point. The second network has two inputs and one output: the inputs use the outputs of the first network and the output will be the periodic cycle which controls the DC/DC converter. The training step of neural networks requires two modes: the offline mode and the online mode. The data necessary for the training are collected from a very large number of real-time measurements of the PV module. The performance of the proposed method is analyzed under different operating conditions using the Matlab/Simulink simulation tool. A comparative study between the proposed method and the perturbation and observation approach was presented.

Joint position control of bionic jumping leg driven by pneumatic artificial muscle

The bionic legs are generally driven by motors which have the disadvantages of large size and heavy weight.In contrast,the bionic legs driven by pneumatic artificial muscles(PAMs)have the advantages of light weight,good bionics and flexibility.A kind of bionic leg driven by PAMs is designed.The proportional-integral-derivative(PID)algorithm and radial basis function neural network(RBFNN)algorithm are combined to design RBFNN-PID controller,and a low-pass filter is added to the control system,which can effectively improve the jitter phenomenon of the joint during the experiment.It is verified by simulation that the RBFNN-PID algorithm is better than traditional PID algorithm,the response time of joint is improved from 0.15 s to 0.07 s,and the precision of joint position control is improved from 0.75°to 0.001°.The experimental results show that the amplitude of the change in error is reduced from 0.5°to 0.2°.It is verified by jumping experiment that the mechanism can realize jumping action under control,and can achieve the horizontal displacement of 500 mm and the vertical displacement of 250 mm.