Journal of Modeling and Simulation
Volume:46 Issue: 2, Autumn 2014

Coriolis mass flow meters are one of the most accurate tools to measure the mass flow in the industry. However, two-phase mode (gas-liquid) may cause severe operating difficulties as well as decreasing certitude in measurement. This paper presents a method based on fuzzy systems to correct the error and improve the reliability of these sensors in the presence of two-phase model fluid. Definite available flow meter parameters are given to designed fuzzy system as inputs, and error is estimated as its output. In the proposed method, to decrease the number of rules, data are clustered using K-means clustering algorithm. The ability of this method in error correction is shown by testing it on real experimental data and compared with the least square method.

This paper is concerned with the consensus tracking problem of high order MIMO nonlinear multi-agent systems. The agents must follow a leader node in presence of unknown dynamics and uncertain external disturbances. The communication network topology of agents is assumed to be a fixed undirected graph. A distributed adaptive control method is proposed to solve the consensus problem utilizing relative information of neighbors of each agent and characteristics of the communication topology. A radial basis function neural network is used to represent the controller’s structure. The proposed method includes a robust term with adaptive gain to counter the approximation error of the designed neural network as well as the effect of external disturbances. The stability of the overall system is guaranteed through Lyapunov stability analysis. Simulations are performed for two examples: a benchmark nonlinear systems and multiple of autonomous surface vehicles (ASVs). The simulation results verify the merits of the proposed method against uncertainty and disturbances.

In this paper, a recurrent fuzzy-neural network (RFNN) controller with neural network identifier in direct control model is designed to control the speed and exhaust temperature of the gas turbine in a combined cycle power plant. Since the turbine operation in combined cycle unit is considered, speed and exhaust temperature of the gas turbine should be simultaneously controlled by fuel command signal and inlet guide vane position. Also practical limitations are applied to system inputs. In addition, demand power and ambient temperature are considered as disturbance. Simulation results show the effectiveness of proposed controller in comparison with other conventional methods such as Model Predictive Control (MPC) and H∞ control in a same operating condition.

Based on the problems caused by today conventional vehicles, much attention has been put on the fuel cell vehicles researches. However, using a fuel cell system is not adequate alone in transportation applications, because the load power profile includes transient that is not compatible with the fuel cell dynamic. To resolve this problem, hybridization of the fuel cell and energy storage devices such as batteries and ultra-capacitors are usually applied. This article has studied a hybrid electric vehicle comprising a fuel cell system and battery pack. Energy management strategy is one of the essential issues in hybrid electric vehicles designing, for power optimal distribution as well as, improving both the fuel economy and the performance of vehicle's components. In this paper, an optimal hierarchical strategy has been proposed based on the load power prediction and intelligent controlling to achieve an optimal distribution of energy between the vehicle's power sources; and, to ensure reasonable performance of the vehicle's components. For load power prediction, a new method is presented that is based on Takagi – Sugeno fuzzy model trained by an improved differential evolutionary algorithm with an objective function formulated by support vector machine. A combination of empirical mode decomposition (EMD) algorithm capabilities, fuzzy logic controller, supervisory switching technique and improved differential evolution algorithm is used to design the proposed energy management strategy. The proposed strategy is assessed in the UDDS Standard drive cycle. Simulation results show that the proposed control strategy can fulfill all the requirements of an optimal energy management.

Modeling and simulation of communication circuits at high frequency are important challenges ahead in the design and construction of these circuits. Knowing the fact that the lumped element model is not valid at high frequency, distributed analysis is presented based on active and passive transmission lines theory. In this paper, a lossy transmission line model of traveling wave switch (TWSW) is presented and fully distributed analysis of this structure is also introduced. In the off state, the ordinary single pole single throw (SPST) switches operate as short or open circuit and return an observable part of the signal. To improve return loss in the off state, a non-uniform structure of SPST switches is proposed which is based on the artificial tapered transmission line produced by applying various controlling voltage at the gate. The analysis of ordinary and improved structure of SPST switches is performed and it is further compared with that of the semi-distributed and fully distributed methods. The results of simulation easily approve the improvement of matching in the off state.

Manufacturers need to evaluate the reliability of their products in order to increase the customer satisfaction. Proper analysis of reliability also requires an effective study of the failure process of a product, especially its failure time. So, the Failure Process Modeling (FPM) plays a key role in the reliability analysis of the system that has been less focused on. This paper introduces a framework defining an approach for the failure process modeling with censored data in Constant Stress Accelerated Life Tests (CSALTs). For the first time, various types of censoring schemes are considered in this study. Usually, in data analysis, it is impossible to get closed form of estimates of the unknown parameter due to complex and nonlinear likelihood equations. As a new approach, a mathematical programming problem is formed and the Maximum Likelihood Estimation (MLE) of parameters is obtained to maximize the likelihood function. A case study in red Light- Emitting Diode (LED) lamps is also presented. The MLE of parameters is obtained using genetic algorithm (GA). Furthermore, the Fisher information matrix is obtained for constructing the asymptotic variances and the approximate confidence intervals of estimates of the parameters.