The Modares Journal of Electrical Engineering
Volume:16 Issue: 4, 2017

In the recent years, wind energy improves to one of the most important sources of electricity production. For instance it is predicted that the installed wind capacity in China will be about 40 GW till 2020. The permanent magnet synchronous generators (PMSG) which contains a permanent magnet that causes DC excitation current in the stator windings, are widely used in variety of wind turbines. The advantages of this type of generators in comparison to the others, are higher efficiency, controllable terminal voltage and reactive power. It is also remarkable that the PMSG speed can be controlled by the converter leads to MPPT implementation. The MPPT is always implemented to control the generator speed and output power between the cut-in and nominal wind speed. It is necessary to control the generator speed by pitch or stall control for upper rated wind speeds. In this paper after explaining the structure and components of a typical wind turbine with permanent magnet synchronous generator, designing of an offline DMC (Dynamic Matrix Control) and a gain scheduled PI pitch controller are presented. Both of these controllers have been tested on the practical simulator of 100 KW wind turbine with PMSG generator and the results are presented and compared.

Nonlinear behavior is a common feature of all real-world systems. However, for the sake of simplicity, a linear model is often used in the controller design procedure. Nevertheless, the neglected nonlinear dynamics could degrade the performance of controller drastically. This study presents a new method of designing a model predictive controller (MPC) for a class of nonlinear systems. In the proposed method, an MPC is first designed in state space based on a linear model and then modified by using modal series to compensate for the effect of the neglected nonlinear dynamics in the linear model. Because the proposed controller adjusts a linear controller instead of designing a new one, it can be easily applied in industries to modify controllers that have been designed based on linear models. In addition, its computational burden is much less than that of nonlinear MPC methods. In this study, the proposed technique is used to control two real-world systems, and the results of its application are discussed.

This study is aimed at designing offline and online controllers for energy management of series hybrid electric vehicles (SHEV). where beside decreasing fuel consumption and keeping battery state of charge within acceptable range, reduces air pollution .In this paper two energy management strategies of SHEV are designed. In first strategy based on known driving cycle, a fuzzy logic controller (FLC) is designed to manage energy and power between Electric battery (EB) and internal combustion engine (ICE). In Second control strategy widths of Gaussian membership functions in designed FLC of first strategy are optimized. Preliminarily optimization of Gaussian membership functions widths is done by ant colony optimization (ACO) algorithm, then according to four representative driving cycles, four optimized FLCs are designed. In contrast with first strategy these four FLCs can manage energy and power between EB and ICM for an unknown driving cycle. Recognition of deriving cycle is based on main features extraction of each driving cycle. A learning vector quantization (LVQ) neural network is used to recognized pattern of unknown driving cycle. Finally after recognition of driving cycle, a Driving cycle recognition (DCR) network algorithm is used to manage switching between optimized FLCs. To verify performance and efficiency of proposed method simulation performed due to Matlab/Advisor environment

This paper presents robust optimal control of an uncertain nonlinear switched system with forced subsystems. The uncertainties include external disturbance and parametric uncertainties. Switching signal and control input are designed to minimize a given cost function. Approximate dynamic programming (ADP) has been efficiently applied to certain switched systems as an optimal control strategy. Since approximate dynamic programming method is model based, there would seem to be some difficulties to apply approximate dynamic programming to uncertain switched system. To overcome these mentioned problems, this paper presents an appropriate model. In order to apply proposed control approach, robust time-delay controller is added with ADP control. At first uncertainties are compensated by robust time-delay controller. Then the switching signal and the control input are design by approximate dynamic programming that provides a feedback solution for unspecified initial conditions. The discussing boundedness of states and simulation results verify the effectiveness of the proposed control approach.

Supervisor reduction procedure can be used to construct the reduced supervisor with a reduced number of states in discrete-event systems. However, it was proved that the reduced supervisor is control equivalent to the original supervisor with respect to the plant; it has not been guaranteed that the reduced supervisor and the original one are control equivalent under partial observation. In this paper, we extend the supervisor reduction procedure by considering partial observation; namely not all events are observable. A feasible supervisor which is constructed under partial observation becomes reduced based on control consistency of uncertainty sets of states, instead of the original supervisor. In order to construct a partial observation reduced supervisor, a partial observation control cover is constructed based on control consistency of uncertainty sets in the supervisor. Four basic functions are defined in order to capture the control and marking information on the uncertainty sets. In the resulting reduced supervisor, only observable events can cause state changes. The results are illustrated by some examples.

This research aims to describe a novel model, namely Hybrid Adaptive-Neuro Fuzzy Inference System-Particle Swarm Optimization (ANFIS-PSO), for predicting corrosion rate of 3C steel considering different marine environment factors. In the present research, five parameters (temperature, dissolved oxygen, salinity, pH, and oxidation–reduction potential) were used as input variables, with corrosion rate being the only output variable. In the proposed hybrid ANFIS-PSO model, the PSO served as a tool to automatically search for and update optimal parameters for the ANFIS, so as to improve generalizability of the model. Eeffectiveness of the hybrid model was then compared those to two other models, namely Adaptive-Neuro Fuzzy Inference System–Genetic Algorithm (ANFIS-GA) and Support Vector Regression (SVR) models, by evaluating their results against the same experimental data. The results showed that the proposed hybrid model tends to produce a lower prediction error than those of ANFIS-GA and SVR with the same training and testing datasets. Indeed, the hybrid ANFIS-PSO model provides engineers with an applicable and reliable tool to conduct real-time corrosion prediction of 3C steel considering different marine environment factors.

In recent decades, the researchers have been attracted in utilizing of the multi-agent systems due to the sophistication in industrial processes, the cost of performing them and increasing the reliability. One of the interesting problems in this field of study is formation control of agents. In this paper, we are going to design a decentralized control strategy for the formation control of a group of quadrotors. To be more specific, we simplify the nonlinear dynamic of a quadrotor by using motion approximation and feedback linearization. Then, we solve the formation control problem of quadrotors by the utilization of leader-follower strategy with a decentralized protocol. In this control strategy, only do a partial number of followers have access to the leader’s information. This matter can reduce noticeably the energy consumption of the leader since it requires to send less amount of information. Thereafter, we will corroborate the convergence of quadrotors to the predefined formation and leader tracking mathematically. Finally, the simulation example will be presented in order to validate the theoretical results.

This paper proposes the states estimation of one-sided Lipschitz fractional order systems. In this regard, using an LMI based continuous frequency distribution results in H nonlinear resilient observer design in the presence of an exogenous disturbance input and observer's gain perturbation. Since one-sided Lipschitz class of nonlinear systems encompasses a wide range of nonlinearities including the Lipschitz class, It is shown that the proposed observer design based on the one-sided Lipschitz systems has simpler LMI and it can tolerate a wide range of changes in gains perturbation and exogenous disturbances compared to recent research findings for the counterpart Lipschitz systems. Finally, a financial fractional order system with Lipschitz nonlinearity is presented as an example, which can illustrate the effectiveness of the proposed one-sided Lipschitz observer design and compare the feasible region of input disturbance and observer’s gain for both Lipschitz and one-sided Lipschitz observer design.

This paper presents the effects of body doping concentration on a carbon nanotube field-effect transistor (CNTFET) with symmetrical coaxial geometry structure, that transistor has a wrapped gate dielectric around the nanotube. At first, Gordon Moore's law is expressed briefly. Then, the limitations and problems after 10nm channel lengths of the traditional metal-oxide-semiconductor field-effect transistors (MOSFETs) scaling are introduced and the structure of carbon nanotube field-effect transistor as a solution for limitations is illustrated. The simulation process is performed, then the effect of body doping concentration is investigated on the current-voltage characteristics, energy profile and electron density of transistor. The simulation is based on a self-consistent solution of the two dimensions Poisson equation and Schrodinger solver. Results show that with increasing body doping concentration level, the transistor output current, energy profile and electron density of transistor will increase and on/off current ratio will decrease.

In this paper, we introduce a new algorithm to deal with the stalling effect in the LMS algorithm used in adaptive filters. We modify the update rule of the tap weight vectors by adding noise, generated by a noise generator. The properties of the proposed method are investigated by two novel theorems. As it is shown, the resulting algorithm, called Added Noise LMS (AN-LMS), improves the resistance capability of the conventional LMS algorithm against the stalling effect. The probability of update with additive white Gaussian noise is calculated in the paper. Convergence of the proposed method is investigated and it is proved that the rate of convergence of the introduced method is equal to that of LMS algorithm in the expected value sense, provided that the distribution of the added noise is uniform. Finally, it is shown that the order of complexity of the proposed algorithm is linear as the conventional LMS algorithm.