Showing papers by "Mahdi Aliyari Shoorehdeli published in 2013"

Type-2 Fuzzy Control for a Flexible- joint Robot Using Voltage Control Strategy

Abstract: Type-1 fuzzy sets cannot fully handle the uncertainties. To overcome the problem, type-2 fuzzy sets have been proposed. The novelty of this paper is using interval type-2 fuzzy logic controller (IT2FLC) to control a flexible-joint robot with voltage control strategy. In order to take into account the whole robotic system including the dynamics of actuators and the robot manipulator, the voltages of motors are used as inputs of the system. To highlight the capabilities of the control system, a flexible joint robot which is highly nonlinear, heavily coupled and uncertain is used. In addition, to improve the control performance, the parameters of the primary membership functions of IT2FLC are optimized using particle swarm optimization (PSO). A comparative study between the proposed IT2FLC and type-1 fuzzy logic controller (T1FLC) is presented to better assess their respective performance in presence of external disturbance and unmodelled dynamics. Stability analysis is presented and the effectiveness of the proposed control approach is demonstrated by simulations using a two-link flexible-joint robot driven by permanent magnet direct current motors. Simulation results show the superiority of the IT2FLC over the T1FLC in terms of accuracy, robustness and interpretability.

Modeling, identification and control of a heavy duty industrial gas turbine

Abstract: In this paper, modeling, identification and control of a real 162MW heavy duty industrial gas turbine is taken into account. This work is aimed to introduce a simple and comprehensive model to test various controllers. Rowen's model is used to present the mechanical behavior of the gas turbine, while the identification of it is done using a feedforward neural network. The control rules of the turbine are applied on both models and a comparison of the results is also presented.

Synchronization of Underactuated Unknown Heavy Symmetric Chaotic Gyroscopes via Optimal Gaussian Radial Basis Adaptive Variable Structure Control

Abstract: This brief proposes modified projective synchronization (MPS) methods for underactuated unknown heavy symmetric chaotic gyroscope systems via optimal Gaussian radial basis adaptive variable structure control. Chaotic gyroscope systems are considered as underactuated systems where a control input is designed to synchronize the two degree of freedoms interactions. Until now, no investigation of this subject with one control input has been presented. The importance of obtaining synchronization objectives is specified when the dynamics of gyroscope system are unknown. In this brief, using the neural variable structure control technique, a control law is established that guarantees the MPS of underactuated unknown chaotic gyros. In the neural variable structure control, Gaussian radial basis functions are utilized to estimate online the system dynamic functions. Adaptation laws of the online estimator are derived in the sense of the Lyapunov function. Moreover, online and offline optimizers are applied to optimize the energy of the control signal. The proposed solution is generalized to chaos control of the mentioned gyroscopes. Numerical simulations are presented to verify the proposed synchronization methods.

Interval Type-2 Adaptive Network-based Fuzzy Inference System (ANFIS) with Type-2 non-singleton fuzzification

Abstract: In this study it is attempted to describe the structure and procedure of training for the Interval Type-2 Fuzzy Logic inference System completely. To achieve this goal Adaptive Network-based Fuzzy Inference System (ANFIS) structure has been generalized to interval type-2 fuzzy, also all of the relations to describe inference structure and all of the necessary differentiation to adjust parameters with Gradient descent and Levenberg-Marquardt method has been brought. Described structure has been used to forecast Mackey-Glass chaotic time-series that polluted with additive uncertain domain noise. Using mentioned procedure for parameters adjustment achieved acceptable results.

Specificity enhancement in classification of breast MRI lesion based on multi-classifier

Abstract: MR-based methods have acceded an important role for the clinical detection and diagnosis of breast cancer. Dynamic contrast-enhanced MRI of the breast has become a robust and successful method, especially for the diagnosis of high-risk cases due to its higher sensitivity compared to X-ray mammography. In the clinical setting, the ANN has been widely applied in breast cancer diagnosis using a subjective impression of different features based on defined criteria. In this study, several neural networks classifiers like MLP, PNN, GRNN, and RBF has been presented on a total of 112 histopathologically verified breast lesions to classify into benign and malignant groups. Also, support vector machine has been considered as classifier. Before applying classification methods, feature selection has been utilized to choose the significant features for classification. Finally, to improve the performance of classification, three classifiers that have the best results among all applied methods have been combined together that they been named as multi-classifier system. For each lesion, final detection as malignant or benign has been evaluated, when the same results have been achieved from two classifiers of multi-classifier system. Tables of results show that the proposed methods are correctly capable to feature selection and improve classification of breast cancer.

V94.2 gas turbine identification using neural network

Abstract: This paper presents the identification of V94.2 gas turbine. This turbine is built by Siemens. It has 162.1 MW nominal power and 50 Hz nominal frequency and is located at Kermanshah power plant, Kermanshah city of Iran. The stored data from turbine include fuel pressure valve angle and IGV1 angle as inputs and compressor output pressure, compressor output temperature, fuel pressure, turbine output power and turbine output temperature as outputs. To simplify identification process, the system turns into MISO2 systems to the number of outputs, and then correlation analysis is used to examine the dependence of the outputs to each input and other outputs. For turbine identification, dynamic linear models are estimated and then Feedforward neural network with one hidden layer is trained. The result shows dynamic linear models have poor performance in comparison with Feedforward neural network with one hidden layer. The neural network is able to identify a predictor model with fitness over 96% for outputs of V94.2 gas turbine.

Modified projective synchronization of unknown chaotic dissipative gyroscope systems via Gaussian radial basis adaptive variable structure control

Abstract: This paper proposes the modified projective synchronization method for unknown chaotic dissipative gyroscope systems via Gaussian radial basis adaptive variable structure control. Because of the nonlinear terms of the dissipative gyroscope system, the system exhibits chaotic motions. As chaotic signals are usually broadband and noise-like, synchronized chaotic systems can be used as cipher generators for secure communication. Obviously the importance of obtaining these objectives is specified when the dynamics of the gyroscope system are unknown. In this paper, using the neural variable structure control technique, control laws are established, which guarantees the modified projective synchronization of an unknown chaotic dissipative gyroscope system. Switching surfaces are adopted to ensure the stability of the error dynamics in variable structure control. In the neural variable structure control, Gaussian radial basis functions are utilized online to estimate the system dynamic functions. Also, the adap...

Fault Detection and Isolation of a cement rotary kiln using fuzzy clustering algorithm

Abstract: In this paper, Fault Detection and Isolation (FDI) is studied for the rotary kiln of Saveh White Cement Company. To do so, K-means algorithm as a crisp clustering, Fuzzy C-Means (FCM), and Gustafson-Kessel (GK) algorithms as fuzzy clustering are used. In those, for finding number of clusters, Cluster Validity Indices (CVI) are applied. Principal Component Analysis (PCA) mapped the clusters into two dimensional spaces. Fault detection and isolation performance are evaluated by three criteria namely sensitivity, specificity, and confusion matrix. The results reveal that GK fuzzy algorithm provides better performance on detection and isolation of fault in this industrial plant.

The Scrutiny of Variation in the Number of Fuzzy Rules and Membership Functions in a New Genetic-Fuzzy System in Approximation and Prediction Problems

Abstract: This paper shows a new fuzzy system was improved using genetic algorithm to handle fuzzy inference system as a function approximator and time series predictor. The system was developed generality that trained with genetic algorithms (GAs) corresponding to special problem and would be evaluated with different number of rules and membership functions. Then, compare the efficacy of variation of these two parameters in behavior of the system and show the method that achieves an efficient structure in both of them. Also, the proposed GA-Fuzzy inference system successfully predicts a benchmark problem and approximates an introduced function and results have been shown.

Lyapunov rule-based fuzzy control and chaotic anti-control for flexible joint system and analysis of chaotic signal existence effectiveness with experimental validation

Abstract: This study proposes a novel chaotic anti-control for flexible joint system. The proposed controller is composed of a Lyapunov rule-based fuzzy control and chaotic anti-control for target tracking of the flexible joint manipulator. Chaotic signal is used to study the effect of anti-control to reduce the deflection of flexible joint system and control signal energy. For this purposes the flexible joint has been synchronized with chaotic Lorenz system. In this study on of the Lorenz parameters is changed to analysis the effect of chaotic signals. The results of the proposed approach shows in terms of level of vibration reduction and energy consumption of control signal, we could find an optimum point based on value of Lorenz system parameter. Finally, the efficacy of the proposed method and results of existence of different nonlinearity behavior is validated through experiments on QUANSER's flexible-joint manipulator.

Model-based fault detection of a nonlinear system using interval type-2 fuzzy systems with non-singleton type-2 fuzzification

Abstract: In this study interval type-2 fuzzy systems with non-singleton type-2 fuzzifire are used for identification and modeling nonlinear systems having noise with changing domain for fault detection purpose. The main idea in this fault detection method is to serve an upper bound and a lower bound as a confidence bound for system output that obtained from the interval type-2 fuzzy system. If we haven't precise information about mean and variance of noise, then non-singleton type-2 fuzzifire is usable. This fuzzifire improves performance of fault detection confidence bound. In the end of this paper a well-known benchmark two-tank system has been used for representing the advantages of proposed fault detection method.

Identification and control of water supply reservoirs by using neural networks

Abstract: In this study, first by using the collected real data from a 10000 cubic - meter Qazvin - kowsar water supply reservoir is modeled by nonlinear output error (NOE) structure, then a neural nonlinear controller based on the MLP neural network according to created model is designed in order to control the tank water level. The operation of the proposed controller is compared by a PID controller which its coefficients is optimized by genetic algorithm. Results of the simulation indicates that the neural nonlinear controller has a better function than the PID controller, and also this controller is able to control the level water of the tank appropriately regardless the consumer profile in all conditions even in consumer picks.

Multidisciplinary modeling and position control of an electro-hydrostatic actuation system using an adaptive PID controller based on neurofuzzy network

Abstract: This paper deals with the issue of position control of an Electro-Hydrostatic Actuator (EHA) using an adaptive PID controller based on neurofuzzy network. In this relation, the design and simulation of an electro-hydrostatic actuation system referred to as EHA using multidisciplinary modeling method is presented. In recent years, fuzzy-PID controller is one of the main controllers that apply to the EHA systems. To improve the response of this controller, another control technique is needed to combine with the fuzzy-PID, and also, training some parameters of fuzzy-PID technique is a solution. The whole of new controller is composed of pair of interconnected subsystems, that is, an RBF network and conventional fuzzy-PID controller to enhance the tracking performance. Results show a significant improvement in transient response is achieved in comparison with a conventional fuzzy-PID control.

Patterns with different phases but same statistics

Abstract: Many successful methods in various vision tasks rely on statistical analysis of visual patterns. However, we are interested in covering the gap between the underlying mathematical representation of the visual patterns and their statistics. With this general trend, in this paper a relationship between phase structure of a class of patterns and their moments after and before filtering have been considered. First, a general formula between the phase structure and moments of the images is obtained. Second, a theorem is developed that states under which conditions two visual patterns with the same frequencies but different phases have the same moments up to a certain moment. Finally, a theorem is developed that explains, given a set of filters, under which conditions two visual patterns with both different frequencies and different phases have the same subband statistics.

The presentation of an anti-noise defuzzifier to improve the performance of the Interval Type-2 Fuzzy Controller

Abstract: The output of the Interval Type-2 Fuzzy Logic Systems (IT2FLS) before defuzzification is an interval that the average of this interval usually is considered as the output. IT2FLS describes the uncertainties of input signals with an interval in the output. Considering the average of this interval as the crisp output wastes the description of uncertainties. For better performance the crisp output of the interval should be selected purposive. In this paper an anti-noise defuzzifier is presented. This improved defuzzifier generates soft outputs for noisy input signals. The PH neutralization process has measurement noise so it is used as the case study.