Journal of Modeling and Simulation
Volume:45 Issue: 1, Spring 2013

This article presents a fuzzy robust Mixed - Sensitivity Gain - Scheduled H controller based on the Loop - Shaping methodology for a class of MIMO uncertain nonlinear Time - Varying systems. In order to design this controller, the nonlinear parameter - dependent plant is first modeled as a set of linear subsystems by Takagi and Sugeno’s (T - S) fuzzy approach. Both Loop - Shaping methodology and Mixed - Sensitivity problem are then introduced to formulate the frequency - domain specifications. Furthermore, a Regular Weights Selection Method (RWSM) is used to devise a systematic design for choosing properly the weighting matrices. Afterwards, for each linear subsystem, an H∞∞ controller is designed via linear matrix inequality (LMI) approach. Such controllers are said to be scheduled by the Time - Varying parameter measurements in real time. The Parallel Distributed Compensation (PDC) is then used to design the controller for the overall system and the total linear system is also obtained through using the weighted sum of the local linear subsystems. Several results show that the proposed method can effectively meet the performance requirements like robustness, good load disturbance rejection and tracking responses, and fast transient responses for the 3 - phase interior permanent magnet synchronous motor (IPMSM). Finally, the superiority of the proposed control scheme is approved in comparison with the feedback linearization controller, the H2/H∞ Controller and the H∞ Mixed - Sensitivity controller methods.

In this research, static stresses analysis of boron nitride nano - tube reinforced composite (BNNTRC) cylinder made of poly - vinylidene fluoride (PVDF) subjected to non - axisymmetric thermo - mechanical loads and applied voltage is developed. The surrounded elastic medium is modelled by Pasternak foundation. Composite structure is modeled based on piezoelectric fiber reinforced composite (PFRC) theory and a representative volume element has been considered for predicting the elastic, piezoelectric and dielectric properties of the cylinder. Higher order governing equations were solved analytically by Fourier series. The results demonstrated that the fatigue life of BNNTRC cylinder will be significantly dependent on the angle orientation and volume fraction of BNNTs. Results of this investigation can be used for the optimum design of thick - walled cylinders under the multi - physical fields.

In this paper, we present a comparative study between the modified variational iteration method (MVIM) and a hybrid of Fourier transform and variational iteration method (FTVIM). The study outlines the efficiency and convergence of the two methods. The analysis is illustrated by investigating four singular partial differential equations with variable coefficients. The solution of singular partial differential equations usually needs a coordinate transformation in order to discard the singularity of the partial differential equation. Most often this transformation is not applicable and even does not exist. Therefore in this case the solution for the singular partial differential equation does not exist. In the present study the results of simulation for the singular partial differential equations with variable coefficients using the Fourier transform variational iteration method are compared with the results of simulation using the modified variational iteration method. The comparison shows that the effectiveness and accuracy of Fourier transform variational iteration method is more than that of the modified variational iteration method for the simulation of singular partial differential equations.

In this paper we investigate a biological framework to generate and adapt a motion pattern so that can be energy efficient. In fact, the motion pattern in legged animals and human emerges among interaction between a central pattern generator neural network called CPG and the musculoskeletal system. Here, we model this neuro - musculoskeletal system by means of a leg - like mechanical system called stretchable pendulum, and an adaptive frequency nonlinear oscillator as a CPG unit. The stretchable pendulum is a simple oscillating mass - spring mechanism that interacts with the ground during its oscillations, and this interaction begins with a collision. Interaction with the ground causes the model to involve in two dynamic phases that are switched to each other through transition events. This hybrid model is very similar to models have been proposed for the legged locomotion mechanisms. Then, it will be simulated in coupling with an adaptive frequency Hopf oscillator as a controller placed in feedback loop. The simulation results reveal that this scheme is able to excite the mechanical system in an energy efficient pattern by way of exploiting resonance phenomenon. Also, adaptation of the system against the environmental changes is examined and it is seen that the controller is able to find the resonant mode after the changes were made.

In this paper, in order to increase the efficiency, to reduce the cost and to prevent the failures of wind turbines, which lead to an extensive break down, a robust fault diagnosis system is proposed for V47/660kW wind turbine operated in Manjil wind farm, Gilan province, Iran. According to the acquired data from Iran wind turbine industry, common faults of the wind turbine such as sensor faults, actuator faults and component faults are identified and considered in Fault Detection and Isolation (FDI) system design. Various Faults in abrupt and incipient natures can be detected and isolated using the indicators of faults, namely residuals, that are derived based on Unknown Input Observer (UIO) approach. Moreover, some thresholds are exploited to evaluate the produced residuals. The robustness of the proposed method against parameter uncertainties is shown as well. Simulations are performed in Matlab/Simulink environment to demonstrate the effectiveness of the proposed method using the actual parameters derived from the turbine model.

There have been several efforts in the literature to extract as much information as possible from the financial networks. Most of the research has been concerned about the hierarchical structures, clustering, topology and also the behavior of the market network; but not a notable work on the network filtration exists. This paper proposes a stock market filtering model using the correlation - based financial networks in which network nodes represent the potential stocks and network edges indicate the correlation coefficients of corresponding stock pairs. The model is capable of reducing the basic market size while keeping the diversification and risk - return expectations fairly constant. The novelty of this research is to develop a new market network filtering method which exploits Minimum Spanning Tree (MST) to reduce the number of network nodes (graph order) rather than the links (graph size). The proposed method chooses the nodes (stocks) based on dangling ends of the constructed MST. In order to verify our proposed model, we applied the model on data of three stock markets: New York Stock Exchange (NYSE), Germany Stock Exchange (DAX) and Toronto Stock Exchange (TSE). In conclusion, the numerical results showed that our proposed model can make a subset of the stock market in which its performance can imitate the whole market with a rather considerable reduction in size; as a result, we can have a diversified subset of the market compatible with that of the whole market. The performance of the model is confirmed by comparing the portfolio of the filtered market network with the whole market portfolio using the complement of Herfindahl Index as a measure of diversification.