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Abstract:This paper presents an analytical study of the nonlinear effect of squeeze film damping on the previously designed ring shape anchored contour mode RF MEMS disk resonators. Varations of damping coefficient and stiffness constant of the resonator due to squeeze film phenomenon are evaluated and their efects on quality factor of the resonator is calculated. Analytical calculations mention that due to ultra high spring constant of the resonator, resulting frequency pulling and damping related to this effect could be neglected. Based on these derivations, there is no requirement for high price
vacuum packaging for these resonators for increasing their quality factor. Extracting of the behavioral equations for the resonator in the presence of squeeze film effect is crucial in design of resonators in packaging, fabrication process and total cost points of view.

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Conventional Off-Line (50-60Hz) light emitting diode (LED) drivers usually need an electrolytic capacitor to reduce power imbalance between ac input and dc output, and to decrease the low-frequency component of the output ripple. However, electrolytic capacitor is the key component which limits the lifespan of LED driver. If a driver uses a pulsating output current, the electrolytic capacitor can be omitted. This paper proposes a high power factor LED driver with high frequency pulsating driving current, in which electrolytic capacitor has omitted from the topology. The driver has two outputs that allow the number of driving LEDs to increase, an LED string will be connected to each output and a single switch is used in series with each LED string, thus the number of switches is equal to the number of outputs. Output and Storage capacitor voltages are sensed and used to implement the control method to regulate the output current. Low cost and high efficiency can be obtained due to the power structure and the proposed control method. Simulations, calculations and experimental results are provided for the proposed circuit.

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In this paper a novel method is introduced for target detection in bistatic passive radars which uses the concept of correntropy to distinguish correct targets from false detections. In proposed method the history of each cell of ambiguity function is modeled as a stochastic process. Then the stochastic processes consist the noise are differentiated from those consisting targets by constructing an FIR adaptive filter. A cost function which is based on correntropy is utilized to update the filter. The performance of the proposed method is evaluated by simulation in presence of rapid and slow moving targets. The obtained results shows the superiority of the proposed method compared to its alternatives in such manner that it detects rapid targets at least 18.7 and 20.1 percent better than HOSCM and PFCM. Furthermore it detects slow targets 19.3 and 21.4 percent better than those alternatives, all in presence of maximum noise (i.e. SCNR=-30dB). 

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In this paper, by combining fractional calculus and sliding mode control theory, a new fractional order adaptive terminal sliding mode controller is proposed for the maximum power point tracking in a solar cell. To find the maximum power point, the incremental conductance method has been used. First, a fractional order terminal sliding mode controller is designed in which the control law depends on knowing the upper bound of uncertainty in the system, but in practical application it is difficult or in some cases impossible to calculate this upper limit. In this paper, an adaptive law is given for online calculating of this parameter. The stability proof of the sliding surface, as well as the proof of finite time convergence of closed-loop system, are investigated using the Lyapunov theory. Finally, the performance of the proposed controller is evaluated both in normal and partial shading conditions. For a better comparison of the proposed controller, the performance of this controller is compared in the presence of load variations and the variations of system parameters with the conventional (integer order) terminal sliding mode control.

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This paper evaluates the problem of user pairing scenario with similar channel conditions in NOMA with three users per pair. The small difference in the channel gain of the paired users leads to interference in the process of successive interference cancelation (SIC). The incidence of imperfect SIC reduces system capacity. Also, mid users in this scenario will be deprived of the advantages provided by the NOMA without pairing, on the other hand, by pairing,  due to the closeness of the users’ channel conditions to each other, leads to the incidence of imperfect SIC. In this paper, in order to solve this problem, we propose a scenario for users to pair, in which all users, including mid users, will be able to use the benefits of NOMA and the problem of incidence of imperfect SIC is minimized. To evaluate the performance of the NOMA, we examine the sum ergodic capacity in both perfect and imperfect SIC modes. In this paper, the concept of an imperfect SIC has calculated analytically for the first time for pairs containing three users. The calculations performed along with the simulations show that in this case, the system's capacity falls sharply. The simulation results also show that the proposed scenario, in addition to minimizing the imperfect SIC event, provides a higher sum ergodic capacity than other scenarios.

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The particle swarm optimizer (PSO) is a population-based metaheuristic optimization method that can be applied to a wide range of problems but it has the drawbacks like it easily falls into local optima and suffers from slow convergence in the later stages. In order to solve these problems, improved PSO (IPSO) variants, have been proposed. To bring about a balance between the exploration and exploitation characteristics of PSO, this paper introduces computationally fast and efficient IPSO algorithms based on a novel class of exponential learning factors (ELF-PSO). This class contains time-varying exponential learning factors (TELF), random exponential learning factors (RELF), self-adjusting exponential learning factors (SELF) and linear-exponential learning factors (LELF) strategies. Experiment is performed and compared with a set of well-known constant, random, time-varying and adaptive learning factors strategies on a suite of nonlinear benchmark functions. The experimental results and statistical analysis prove that ELF-PSO algorithms are able to solve a wide range of difficult nonlinear optimization problems efficiently. Also these results show that the proposed methods outperform other algorithms in most cases.

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This paper presents a new circuit to configure power amplifier (PA) for return-to-zero on-off-keying (RZ-OOK) transmitters. The proposed PA works as a multimode structure with configurable data rate and output power. The programmable data rate function is achieved by duty cycle adjustment of input data and producing input RZ-data by a simple circuit, which leads to a linear scale of data rate with power consumption. This implies that any desired level of output power can be transmitted with different power consumption according to the power budget. The RZ-data is also utilized to perform the output power reconfiguration. The PA represents data rate of 0.3Mb/s to 3Mb/s and it can deliver output power level from -23dBm to 0dBm. During data rate adjustment, power consumption varies from 0.099mW to 0.99mW when the output power is 0dBm. Also, PA consumes 0.07mW to 0.99mW at the output power tuning range with a data rate of 3Mb/s.

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The forward kinematic problem of parallel robots is always considered as a challenge in the field of parallel robots due to the obtained nonlinear system of equations. In this paper, the forward kinematic problem of planar parallel robots in their workspace is investigated using a neural network based approach. In order to increase the accuracy of this method, the workspace of the parallel robot is divided into a number of smaller subspaces using the classifier and the boundary overlap method. After estimating the corresponding subspace, two separate neural networks are used in each subspace to determine the position and orientation of the moving platform. This approach is implemented on a 3-PRR planar parallel robot and its results are compared with the results obtained from the MLP, WaveNet, GMDH, Dual and Independent neural networks. Moreover, in order to evaluate the efficiency of the proposed method, a circular motion path is simulated using this approach and its performance is compared with the five mentioned methods. The results obtained from the implementation of this approach and comparison with the conventional methods indicates that the proposed method analyzes the forward kinematic problem of planar parallel robot with proper accuracy.

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In this paper, a wideband rectenna without using matching networks is presented. In addition of wide bandwidth, the omnidirectional radiation pattern, maximum radio frequency to dc conversion efficiency, harvesting capability of the minimum input power level, easy fabrication process cause this antenna be a good choice for radio frequency energy harvesting applications. Matching network has been eliminated and the antenna structure has been used to match with rectifier for energy harvesting with maximum efficiency. Based on the minimum input power level, two different structures have been suggested as the rectenna. The suggestive model meets proper efficiency within the frequency band of 1.71-2.5GHz and harvests the input power levels of 0dBm and -5dBm by implementing the two proposed structures with the maximum efficiencies of 74 and 68%, respectively. The multi-tone state has been also investigated which indicated the maximum obtained efficiencies of about 42 and 44% for the 2- and 3-tone modes, respectively.

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This paper is concerned with the problem of improvement of fuzzy H_infinity tracking controller for nonlinear systems modeled by T-S fuzzy scheme. The fuzzy tracking controller not only stabilizes the closed-loop system, but also results in the H_infinity tracking error norm to all the bounded external signals to be less than some given value. A new tracking control law is proposed for each linear local subsystem of T-S fuzzy model. A Linear Matrix Inequalities (LMIs) approach is proposed to find all the parameters of the control laws. The proposed approach results in a noticeable improved tracking performance with respect to the existing approaches. An investigation of the tracking performance of the proposed approach on the inverted pendulum system, in comparison with the other approaches, shows the improvement. 

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Robust algorithm is known as the one of high potential models for strengthening in optimization of large and complex distribution networks considering uncertainty. In this research we are looking for some suitable template and appropriate clustering pattern in order to analyze the operation of distribution network. The analysis of optimization process in both individual and adaptive cases of distributed generation sources applied to some standard distribution network shows that the adaptive case of DGs determines less partial and total operation cost. except in islanded case of DGs. The results of clustering approach based on choosing the candidate points which includes DGs and feeders are used in medium voltage (MV) network level. By investigating the software data in both single cluster with first up to fifth and tenth repeatition, one to five clusters and finally ten clusters case shows the superiority of mean data besed clustering. because of having the three following properties such as being sensitive to all weighted candidate points, the speed of acceptable operation and feeding the clusters in islanded case in multiplicity of clusters, it is suitable to designate the robust optimization method using pattern random clustering.

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In this paper, the problem of robust stabilization of uncertain and perturbed switched nonlinear systems has been investigated. It’s well-known that the solution of H_∞ control problem may not exist for switched systems with a common storage function for all subsystems. On the other hand, by adopting multiple storage functions, different Hamilton-Jacobin inequalities need to be solved. Hence, the robust control problem is addressed here based on passivity, in two cases. First, it is assumed that there is at least one passive subsystem in the whole state space, and the problem is solved based on the average dwell time approach. In this case, by defining the concept of system passivity rate, the admissible range of average dwell time is obtained. In the second case, none of subsystems is passive and the H_∞ control problem is solved by using the feedback passification. In addition to theoretical analysis of the design algorithms, the performance of the theorems for uncertain switched nonlinear systems has been investigated by providing two simulation examples and numerical analysis.

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Since the introduction of the first silicon solar cell, there have been steady improvements in its performance parameters such as light trapping, solar absorption, cell efficiency and manufacturing costs. In thin silicon cells, some of the light photons that are not absorbed by the semiconductor are always lose in various ways. The diffraction grating causes the photons to travel a longer light path due to the collision with this structure, which increases the length of the light path of the photons and cell absorption, that thus improving cell efficiency. In each of the mentioned structures, optimal materials and geometric properties have been used to achieve maximum efficiency of silicon cells. Intelligent optimization methods have been used to find the optimal geometric parameters for the structure. In choosing search methods from the two algorithms particle swarm optimization and genetics and creating a combination of the both, the positive feature of both algorithms was used to achieve the best answer. This combination has produced very positive results, which thereby, 23.293 efficiencies and 35.41 mA/cm2 short circuit current were obtained.

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In chemical processes, thermal reactors are described by nonlinear closed-loop dynamic models. Timely detection of simultaneous fouling phenomena in the heat transfer system is a concern of this art. In this work, a new incipient fault diagnosis approach is proposed for application in the closed-loop non-isothermal continuous stirred-tank reactor (CSTR) system subjected to simultaneous Gaussian and non-Gaussian noises. First, the state vector is estimated by applying the well-known particle filter estimator. Then, the primary residual signal is generated using the system measurements, and the fault vector estimation is obtained. After that, by an adaptive either fixed threshold design applied in the online monitoring devised with the proposed evaluation technique, while the fault detectability is improved, the false detection problem is restricted to the system permitted number. Bank on, preventive maintenance scheduling also incipient fault trend prediction have become possible using the Gauss-Newton identification method. Finally, in order to evaluate the proposed approach, the simultaneous fouling incipient fault diagnosis over the heat transfer unit built-in nonlinear closed-loop CSTR system is considered. Furthermore, the confusion matrix and associated evaluation indices are employed to assess the simulation results quantitatively.

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Polymer electrolyte membrane fuel cells (PEMFC) have been considered by researchers due to their high efficiency, low pollution, and high-power density in distributed generation systems. In this paper, the connected PEMFC fuel cell power recovery system with an LCL filter is evaluated in the harmonic Grid. LCL filters, despite their greater ability to attenuate harmonics, can lead to system resonance and instability. In this research, a transformer has been used to connect the fuel cell inverter to the Grid and its leakage inductance has been used as the inductor on the network side. Besides, for optimal resonance damping, and attenuation of current ripple caused by grids voltage harmonics, capacitor voltage through feedback control has been used. Complete control of capacitor voltage feedback includes proportional, derivative, and second-order components. In the proposed control scheme, the capacitor voltage derivative component opposes the capacitor current feedback due to identical and symmetrical loop gain. Therefore, both of them can be deleted. Thus, the capacitor current sensor is saved. Instead, the LCL filter resonance is damped by a proportional component and a second-order derivative of the capacitor voltage. A low-pass filter is also added to the second-order derivative in the controllable frequency range to ensure system stability. The simulation results of the PEMFC power recovery system in different conditions confirm the proper attenuation of the grid-connected inverter, the injection of current of suitable quality into the contaminated and harmonic grid, the stability, and the appropriate dynamic response of the proposed system.
 

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Currently, renewable energy is rapidly developing across the world in response to technical, economic, and environmental developments, as well as political and social initiatives. Moreover, the excessive penetration of distributed generation (DG) systems into electrical networks may lead to various problems and operational limit violations, such as over and under voltages, excessive line losses, overloading of transformers and feeders, protection failure and high harmonic distortion levels exceeding the limits of international standards. These problems occur when the system exceeds its Hosting Capacity (HC) limit. The HC is a transactive approach that provides a way for the distribution network to be integrated with different types of energy systems.
Distributed Generation (DG) sources are one of the important componeents of novel distribution systems, among which renewable and clean energy sources have received more attention due to the important role of these sources in reducing greenhouse pollutants.
The use of renewable sources such as wind and photovoltaic sources is expanding day by day with the increasing demand for electric energy supply. However, the limitations in the amount of the penetration of DG resources are one of the main challenges in the development of the use of these resources.
This paper is looking for a method to improve the HC of the distribution network from DG sources by using reactive power compensation and the reconfiguration of distribution systems. The results of the simulation show the advantages of using the proposed method in increasing the HC and as a result the development of the use of renewable resources.
 

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In an islanded microgrid, power electronic converters are used to exchange power, and these converters have very low inertia, thus compromising the frequency stability of the microgrid. Virtual inertia control is used to improve the frequency stability of an islanded microgrid. The derivative control technique is usually used to implement virtual inertia control in the microgrid. Factors such as disturbance and uncertainty of parameters of the islanded microgrid compromise the performance of virtual inertia control and may cause system frequency instability. Therefore, the virtual inertia control structure, a complementary controller is needed that can weaken the effect of disturbance on the microgrid as much as possible and be resistant to the uncertainty of parameters of the microgrid. In this paper, a robust control method is used in a virtual inertia control structure that uses system output feedback. The proposed method is expressed based on linear matrix inequality and is proved based on the Lyapunov criterion. Among the advantages of the proposed method is the attenuation of disturbance, resistance to the uncertainty of parameters of the microgrid, and increasing the degree of freedom to control the system in this method. The results of the proposed method to improve the performance of virtual inertia control in several different scenarios by considering the uncertainty of parameters of the two-zone microgrid and disturbances on the microgrid are compared with several methods and the effectiveness of the proposed method in terms of improving frequency stability is shown.

 

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Extracting maximum power, especially with partial shading conditions, is one of the most critical issues in using a photovoltaic system. Under partial shading conditions, the power-voltage characteristic of photovoltaic arrays has several local maximum points. A maximum power point tracking method for photovoltaic systems should enable fast and accurate tracking of the global maximum during partial shading conditions to minimize power losses and steady-state fluctuations. This research presents an algorithm for tracking the maximum power point in a photovoltaic system under partial shading conditions using the gray wolf optimization technique. The gray wolf algorithm is a new optimization method that overcomes limitations such as poor tracking, steady-state fluctuations, and undesirable transients in perturb and observe and particle swarm optimization techniques. The proposed algorithm based on the gray wolf optimization algorithm is implemented on a photovoltaic system in MATLAB software to prove its efficiency. The performance of the proposed design is compared with two maximum power point tracking techniques based on cuckoo search and particle swarm optimization. The simulation results show that the performance of the proposed maximum power point tracking technique is superior to the compared designs in terms of speed and steady-state stability of the response, so that it reduces the values of maximum overshoot, settling time, and sustained fluctuations up to 40.91%, 66.67% and 59.1% respectively.
 

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This paper presents a new method of the adaptive non-singular Second-order Terminal Sliding Mode (SOTSM) control for the fast and finite time stabilization of Cyber-Physical Systems (CPSs) in the simultaneous presence of parametric uncertainties, unwanted disturbances and actuator cyber-attacks. By utilizing the presented non-linear manifold and sliding surface, the reaching mode is deleted and the entire system’s robust performance is improved. The proposed online adaptive laws deal with parametric uncertainties, unwanted disturbances and cyber-attacks, so that there is no need to identify their upper bounds. The designed adaptive non-singular SOTSM control method guarantees the robust performance of the system in the mentioned conditions along with fast and smooth response, high accuracy and flexibility, without transient fluctuations and chattering, as well as proper convergence in finite time. The numerical simulation results show the effectiveness and success of the adaptive non-singular second-order terminal sliding mode control method in comparison with the results of adaptive integral sliding mode control, traditional sliding mode control and state feedback control.
 

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Estimating the state of charge of lithium- ion batteries is of great importance not only for optimal energy management, but also for ensuring safe operation, preventing charging and discharging, and as a result reducing the life of the battery. However, this parameter cannot be measured directly from the battery terminals. Therefore, there is a need to estimate it. In this paper an improved auxiliary marginal particle filter is presented to estimate the state of charge of lithium-ion batteries. In the proposed method, unlike the particle filter, sampling is done on the marginal distribution and the sampling dimensions do not increase with the passage of time. In addition, genetic operators and M-H algorithm have been used in the proposed method to increase diversity among particles. The use of genetic operators and the M-H algorithm causes the resampled particles to asymptotically approximate the samples from the posterior probability density function of the true state and increases the compatibility. The performance of the proposed method for estimating the state of charge of the battery has been compared with the estimation of the state of charge based on the developed particle filter and traceless particle filter. The results show the effective performance of the proposed method in comparison with other methods. The proposed method to obtain the same estimation accuracy as the particle filter requires far fewer particles and the amount of calculations is low. The root mean square error in the proposed method with different particles is close to 0.007, while in other methods, the root mean square error increases with the decrease of particles.