Showing papers by "Mahdi Aliyari Shoorehdeli published in 2011"

Variable structure control and anti-control of flexible joint manipulator with experimental validation

Abstract: This paper presents a variable structure control and anti control for trajectory tracking and vibration control of a flexible joint manipulator. To study the effectiveness of the controllers, designed controller is developed for tip angular position control of a flexible joint manipulator. Based on Lyapunov stability theory for variable structure control, the nonlinear controller and some generic sufficient conditions for global asymptotic control are attained. Also in this study, the anti-control is applied to reduce the deflection angle of flexible joint system. To achieve this goal, the chaos dynamic must be created in the flexible joint system. So, the flexible joint system has been synchronized to chaotic gyroscope system. In this study, control and anti-control concepts are applied to achieve the high quality performance of flexible joint system. It is tried to design a controller which is capable to satisfy the control and anti- control aims. The performances of the proposed control are examined in terms of input tracking capability, level of vibration reduction, and time response specifications. Finally, the efficacy of the proposed method is validated through experimentation on QUANSER's flexible-joint manipulator.

Generalized projective synchronization of uncertain chaotic systems with external disturbance

Abstract: Research highlights? A systematic procedure for GPS of uncertain chaotic systems with disturbance. ? Capable to achieve a full range of GPS. ? Without calculating Lyapunov exponents and Eigen values of the Jacobian matrix. ? Useful for a variety of chaotic systems with or without external excitation. ? Design one controller to realize GPS without considering the dimensions of system. This paper proposes the generalized projective synchronization (GPS) of uncertain chaotic systems with external disturbance via Gaussian radial basis adaptive sliding mode control (GRBASMC). A sliding surface is adopted to ensure the stability of the error dynamics in sliding mode control. In the neural sliding mode controller, a Gaussian radial basis function is utilized to online estimate the system dynamic function. The adaptation law of the control system is derived in the sense of Lyapunov function, thus the system can be guaranteed to be asymptotically stable.The proposed method allows us to arbitrarily adjust the desired scaling by controlling the slave system. It is not necessary to calculate the Lyapunov exponents and the eigen values of the Jacobian matrix, which makes it simple and convenient. Also, it is a systematic procedure for GPS of chaotic systems and it can be applied to a variety of chaotic systems no matter whether it contains external excitation or not. Note that it needs only one controller to realize GPS no matter how much dimensions the chaotic system contains and the controller is easy to be implemented.The proposed method is applied to three chaotic systems: Genesio system, Lur'e like system and Duffing system.

Anti-swing control for a double-pendulum-type overhead crane via parallel distributed fuzzy LQR controller combined with genetic fuzzy rule set selection

Abstract: Overhead crane is an industrial structure that used widely in many harbors and factories. It is usually operated manually or by some conventional control methods. In this paper, we propose a hybrid controller includes both position regulation and anti-swing control. According to Takagi-Sugeno fuzzy model of an overhead crane and genetic algorithm, a fuzzy controller is designed with parallel distributed compensation and Linear Quadratic Regulation. Using genetic algorithm, important fuzzy rules are selected and so the number of rules decreased and design procedure need less computation and its computation needs less time. Further, the stability of the overhead crane with the parallel distributed fuzzy LQR controller is discussed. The stability analysis and control design problems is reduced to linear matrix inequality (LMI) problems. Simulation results illustrated the validity of the proposed parallel distributed fuzzy LQR control method and it was compared with a similar method parallel distributed fuzzy controller with same fuzzy rule set.

Feature selection and classification of breast MRI lesions based on multi classifier

Abstract: This paper aims to increase the classification specificity by using multi classifier system. First, a novel pixel search approach is applied to find significant region in images. Fuzzy C-means is utilized to determine the clear boundary of tumor. Then, shape and texture features are extracted from region of interest. Genetic algorithm is applied to select the best feature used for classifiers. Several neural networks and support vector machine are considered as classifiers that classify the data into benign and malignant group. To improve the performance of classification, three classifiers that have the best results among all applied methods are combined together that they have been named as multi classifier system. For each lesion, final detection as malignant or benign has been evaluated, when the same results are achieved from two classifiers of multi classifier system. Notice that the Jack-Knife technique is applied in this study, because it is useful for small data base as ours gotten from Milad Hospital in Tehran, Iran.

Anti-swing control of a double-pendulum-type overhead crane using parallel distributed fuzzy LQR controller

Abstract: One of the common industrial structures that are used widely in many harbors and factories, and buildings is overhead crane. Overhead cranes are usually operated manually or by some conventional control methods. In this paper, we propose a hybrid controller, that includes both position regulation and anti-swing control. According to Takagi-Sugeno fuzzy model of an overhead crane, a fuzzy controller designed with parallel distributed compensation and linear quadratic regulation. With the Takagi-Sugeno fuzzy modeling, the nonlinear system is approximated by the combination of several linear subsystems in the corresponding fuzzy state space region. Then by constructing a linear quadratic regulation sub-controller according to each linear subsystem, a parallel distributed fuzzy LQR controller is designed. Further, the stability of the overhead crane with the parallel distributed fuzzy LQR controller is discussed. Simulation results illustrated the validity of the proposed control algorithm and it is compared with a similar method parallel distributed fuzzy controller. Key words: Pattern parallel distributed compensation, Takagi_Sugeno fuzzy modelling, overhead crane, linear matrix inequality, linear quadratic regulation.

Designing Flexible Neuro-Fuzzy System Based on Sliding Mode Controller for Magnetic Levitation Systems

Abstract: This study presents a novel controller of magnetic levitation system by using new neuro-fuzzy structures which called flexible neuro-fuzzy systems. In this type of controller we use sliding mode control with neuro-fuzzy to eliminate the Jacobian of plant. At first, we control magnetic levitation system with Mamdanitype neuro-fuzzy systems and logical-type neuro-fuzzy systems separately and then we use two types of flexible neuro-fuzzy systems as controllers. Basic flexible OR-type neuro-fuzzy inference system and basic compromise AND-type neuro-fuzzy inference system are two new flexible neuro-fuzzy controllers which structure of fuzzy inference system (Mamdani or logical) is determined in the learning process. We can investigate with these two types of controllers which of the Mamdani or logical type systems has better performance for control of this plant. Finally we compare performance of these controllers with sliding mode controller and RBF sliding mode controller.

Design of a parallel distributed fuzzy LQR controller for double-pendulum-type overhead cranes

Abstract: One of the common industrial structures that are used widely in many harbors and factories and buildings is overhead crane. Overhead cranes are usually operated manually or by some conventional control methods. In this paper, we propose a hybrid controller includes both position regulation and anti-swing control. According to Takagi-Sugeno fuzzy model of an overhead crane, a fuzzy controller designed with parallel distributed compensation and Linear Quadratic Regulation. With the Takagi-Sugeno fuzzy modeling, the nonlinear system is approximated by the combination of several linear subsystems in the corresponding fuzzy state space region. Then by constructing a linear quadratic regulation subcontroller according to each linear subsystem, a parallel distributed fuzzy LQR controller is designed. Further, the stability of the overhead crane with the parallel distributed fuzzy LQR controller is discussed. Simulation results illustrated the validity of the proposed control algorithm and it is compared with a similar method parallel distributed fuzzy controller.

Fault tolerant synchronization of chaotic heavy symmetric gyroscope systems via Gaussian RBF Neural Network Based on Sliding Mode Control

Abstract: In this paper, fault tolerant synchronization of chaotic gyroscope systems via Gaussian RBF neural network based on sliding mode control is investigated. Taking a general nature of fault in the slave system into account, a new synchronization scheme, namely, fault-tolerant synchronization, is proposed, by which the synchronization can be achieved no matter if the fault and disturbance occur or not. By making use of a slave-observer and Gaussian RBF Neural Network Based on Sliding Mode Control, the fault tolerant synchronization can be achieved. The adaptation law of designed controller is obtained based on sliding mode control methodology without calculating the Jacobian of the system. The proposed method can compensate the actuator faults and disturbances occurred in the slave system. Numerical simulation results demonstrate the validity and feasibility of the proposed method to fault tolerant synchronization.

Fault detection of nonlinear systems by parity relations

Abstract: Recently a lot of works have been done to detect faults in nonlinear systems. In this paper a new method, based on parity relations for linear systems, is proposed to detect faults in nonlinear systems that can be modeled by Takagi-Sugeno (T.S) fuzzy system. This method is an intuitive generalization of parity relations, because T.S fuzzy system uses local linear models. Results of simulation and implementation on a rotary inverted pendulum show that faults can be detected very well.

Fault Detection in Tennessee Eastman Process Using Fisher’s Discriminant Analysis and Principal Component Analysis Modified by Genetic Algorithm

Abstract: This paper describes hybrid multivariate methods: Fisher’s Discriminant Analysis and Principal Component Analysis improved by Genetic Algorithm. These methods are good techniques that have been used to detect faults during the operation of industrial processes. In this study, score and residual space of modified PCA and modified FDA are applied to the Tennessee Eastman Process simulator and show that modified PCA and modified FDA are more proficient than PCA and FDA for detecting faults.

Fuzzy-LQR hybrid control of an electro hydraulic velocity servo system

Abstract: This paper presents a new hybrid control strategy for velocity control of an electro hydraulic servo system (EHSS) in presence of flow nonlinearities and internal friction. We employed a combination of LQR controller and fuzzy-neural network in a feedback error learning framework. In the proposed control approach, LQR controller as a classical controller is designed such that the stability is guaranteed and the control purposes are satisfied. Then an intelligent controller (FNN) which is working with the classical controller (LQR) takes the control task completely. It is shown that this technique (fuzzy-LQR) has good performance and also it has a very fast and proper response. All derived results are validated by computer simulation of a nonlinear mathematical model of the system.

Hybrid Concepts of the Control and Anti-Control of Flexible Joint Manipulator

Abstract: This paper presents a Gaussian radial basis function neural network based on sliding mode control for trajectory tracking and vibration control of a flexible joint manipulator. To study the effectiveness of the controllers, designed controller is developed for tip angular position control of a flexible joint manipulator. The adaptation laws of designed controller are obtained based on sliding mode control methodology without calculating the Jacobian of the flexible joint system. Also in this study, the anti-control is applied to reduce the deflection angle of flexible joint system. To achieve this goal, the chaos dynamic must be created in the flexible joint system. So, the flexible joint system has been synchronized to chaotic gyroscope system. In this study, control and anti-control concepts are applied to achieve the high quality performance of flexible joint system. It is tried to design a controller which is capable to satisfy the control and anti- control aims. The performances of the proposed control are examined in terms of input tracking capability, level of vibration reduction and time response specifications. Finally, the efficacy of the proposed method is validated through experimentation on QUANSER’s flexible-joint manipulator.

Estimating CO Conversion Values in the Fischer-Tropsch Synthesis Using LoLiMoT Algorithm

Abstract: In this paper, a new method for estimation of CO conversion in a range of temperatures, pressures and H2/CO molar ratios in the Fischer-Tropsch (FT) synthesis based on Locally Liner Model Tree (LoLiMoT) has been introduced. LoLiMoT is an incremental tree-construction algorithm that partitions the input space by axis-orthogonal splits. In each iteration two new local models as the result of splitting the worst local model has been inserted into the previous structure and result decreasing the total error. The system has been evaluated through two methods and results show estimated CO conversion values by LoLiMoT are in good agreement with experimental data.

Feedback linearization and chaotic anti-control of flexible joint manipulator with Experimental validation

Abstract: This paper presents a feedback linearization and chaotic anti-control for trajectory tracking and vibration control of a flexible joint manipulator. To study the effectiveness of the controllers, designed controller is developed for tip angular position control of a flexible joint manipulator. Based on Lyapunov stability theory for variable structure control, the nonlinear controller and some generic sufficient conditions for global asymptotic control are attained. Also in this study, the anti-control is applied to reduce the deflection angle of flexible joint system. To achieve this goal, the chaos dynamic must be created in the flexible joint system. So, the flexible joint system has been synchronized to chaotic gyroscope system. In this study, control and anti-control concepts are applied to achieve the high quality performance of flexible joint system. It is tried to design a controller which is capable to satisfy the control and anti-control aims. The performances of the proposed control are examined in terms of input tracking capability, level of vibration reduction and time response specifications. Finally, the efficacy of the proposed method is validated through experimentation on QUANSER's flexible-joint manipulator.

Chaos synchronization for a class of chaotic systems with model uncertainty and external disturbances

Abstract: This paper proposes the generalized projective synchronization (GPS) for a class of nonlinear chaotic systems with model uncertainty and external disturbances via variable structure control. As chaotic signals are usually broadband and noise like, synchronized chaotic systems can be used as cipher generators for secure communication. This paper presents chaos synchronization of two identical chaotic systems named as the master and the slave systems. The slave system is considered with model uncertainty and external disturbances. A sliding surface is adopted to ensure the stability of the error dynamics in variable structure control. The control law applied to chaos synchronization has been established in the sense of Lyapunov function, thus the system can be guaranteed to be asymptotically stable. The proposed method allows us to arbitrarily adjust the desired scaling by controlling the slave system. It is not necessary to calculate the Lyapunov exponents and the eigen values of the Jacobian matrix, which makes it simple and convenient. Also, it is a systematic procedure for GPS of chaotic systems and it can be applied to a variety of chaotic systems no matter whether it contains external excitation or not. Notice that it needs only one controller to realize GPS no matter how much dimensions the chaotic system contains and the controller is easy to be implemented. The designed controller is robust versus model uncertainty and external disturbances. The proposed method is applied to two chaotic systems; chaotic Gyroscope system and Genesio system. Numerical simulations are presented to verify the synchronization approach.

Generalized projective synchronization of time-delayed chaotic systems via sliding adaptive radial basis function neural network control

Abstract: Summary from only given. In this study, generalized projective synchronization (GPS) of two identical and nonidentical time-delayed chaotic systems is presented. Sliding adaptive radial basis function neural network control (SARBFNNC) is applied to synchronize two delayed chaotic systems. The advantages of the adaptive control, neural network and sliding mode control theory are combined in the proposed method. The stability of error dynamics is guaranteed with Lyapunov stability theory. Moreover, supposing that the parameters of the chaotic system are unknown, recursive least square (RLS) method is applied to estimate these unknown parameters. The proposed method has not been used for synchronization of time-delayed chaotic systems yet. Simulation results show that the proposed method is suitable and effective for synchronization of time-delayed chaotic systems.

Modeling and identification of catalytic reformer unit using locally linear model trees

Abstract: This paper presents a Neuro-fuzzy based method using local linear model trees (LOLIMOT) train algorithm for nonlinear identification of a catalytic reformer unit in oil refinery plant. This unit include highly nonlinear behaviour and it is complicated to obtain an accurate physical model. There for, it is necessary to use such appropriate method providing suitable while preventing computational complexities. LOLIMOT algorithm as an incremental learning algorithm has been used several time as a well-known method for nonlinear system identification and estimation. For comparison, Multi Layer Perceptron (MLP) and Radial Bases Function (RBF) neural networks as well-known methods for nonlinear system identification and estimation are used to evaluate the performance of LOLIMOT. The results presented in this paper clearly demonstrate that the LOLIMOT is superior to other methods in identification of nonlinear system such as catalytic reformer unit (CRU).

Sliding adaptive fuzzy control for a class of time-delayed chaotic systems

Abstract: In this study, sliding adaptive fuzzy control (SAFC) is proposed to chaos control for a class of time-delayed chaotic systems. An adaptation law based on Gradient Descent (GD) method is utilized to determine the parameters of fuzzy controller. The first derivation of Lyapunov function is considered as the cost function which must be minimized, so the stability of proposed method is guaranteed with Lyapunov stability theory. Moreover, the changes of parameters and delay time are investigated. Simulation results show that the proposed method is appropriate to chaos control and it is robust with respect to changes of parameters. Also it is illustrated that the performance of proposed method is better than the performance of sliding mode controller.

Generalized projective synchroization of time-delayed chaotic systems via sliding adaptive fuzzy control

Abstract: In this paper, generalized projective synchronization (GPS) of two time-delayed chaotic systems using sliding adaptive fuzzy control (SAFC) is investigated. The proposed method combines the advantages of the adaptive control, fuzzy systems and sliding mode control theory. Lyapunov stability theory is employed to guarantee the stability of error dynamics. Assuming that the parameters of the chaotic drive system are unknown, recursive least square (RLS) method is applied to estimate these unknown parameters. Simulation results show the validity and effectiveness of the proposed method for synchronization of two identical and nonidentical time-delayed chaotic systems.

Synchronization of Chaotic Heavy Symmetric Gyroscope Systems via Gaussian RBF Neural Network Based on Sliding Mode Control

Abstract: .ac.ir Abstract-In this paper, fault tolerant synchronization of chaotic gyroscope systems via Gaussian RBF neural network based on sliding mode control is investigated. Taking a general nature of fault in the slave system into account, a new synchronization scheme, namely, fault-tolerant synchronization, is proposed, by which the synchronization can be achieved no matter if the fault and disturbance occur or not. By making use of a slave-observer and Gaussian RBF Neural Network Based on Sliding Mode Control, the fault tolerant synchronization can be achieved. The adaptation law of designed controller is obtained based on sliding mode control methodology without calculating the Jacobian of the system. The proposed method can compensate the actuator faults and disturbances occurred in the slave system. Numerical simulation results demonstrate the validity and feasibility of the proposed method to fault tolerant synchronization.