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This paper proposes an adaptive control method based on the feedback linearization technique and a proposed neural network,  for tracking and position control of an industrial manipulator. At first, it is assumed that the dynamics of the system are known and the control signal is constructed  by the feedback linearization method. Then to eliminate the effects of the uncertainties and external disturbances, the parameters of the proposed neural network are learned based on the Lyapunov method such that the sliding condition to be satisfied. The performance of the proposed method is compared with the sliding mode technique in presence of external disturbance, delay and uncertainties. The simulation results verify that the proposed method is effective and can be used in many applications.

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A novel current source multilevel inverter is introduced in this paper which is an appropriate alternative to be employed for low/medium power applications. the proposed converter is formed basic modules which paralleling these modules increse output current levels and improve quality of injected current to load or grid. in order to validate advantages of proposed converter versus the several multilevel current source inverters, a full comparison is provided. the simulation results shows the good performance of the proposed converter in off grid and grid-connected applications. Also experimental results for single-phase load confirm the practicablity of the proposed converter.

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Wind powers are very unstable in voltage fluctuations, especially in short circuit error and sharp and sudden voltage drops, which one of its main reasons is the use of induction generators in these power plants and thus need to reactive power and high magnetizing current. To improve the ride_through voltage from WECS in error conditions and damping the oscillations of the induction generator rotor, a UPFC is used the controller FOPID is used in UPFC controllers for the first time. Since FOPID has two parameters more than IOPID, it has recently attracted much attention (needing more work and research motivation) as it gives more flexibility to a control system designing and a better opportunity to adjust the system dynamics, especially, if a system is to be controlled be a fractional system. The investigations indicate that controller FOPID adjusted by presented PSO algorithm, show improved dynamic performance than traditional PID and feedback controller in a wide range of operating conditions.

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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 presents a robust model predictive control scheme for a class of discrete-time nonlinear systems subject to state and input constraints. Each subsystem is composed of a nominal LTI part and an additive uncertain non-linear time-varying function which satisfies a quadratic constraint. Using the dual-mode MPC stability theory, a sufficient condition is constructed for synthesizing the MPC’s stabilizing components; i.e. the local terminal cost function and the corresponding terminal set. The proposed control approach is applied to a CSTR. Simulation results show that the proposed robust MPC scheme is quite effective and it has a remarkable performance.

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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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In this paper, a fractional-order robust adaptive intelligent controller (FRAIC) is designed for a class of chaotic fractional order systems with uncertainty, external disturbances and unknown time-varying input time delay. The time delay is considered both constant and time varying. Due to changes in the equilibrium point, adaptive control is used to update the system's momentary information and the intelligent controller is used to estimate the uncertainties and disturbances and non-linearities of the system according to the momentary information obtained. The sliding mode control, which provides closed loop asymptotic stability in the system despite the uncertainties and disturbances, is used as a robust controller. Using the Lyapunov theorem and Barbalat's Lemma, the asymptotic stability of a chaotic fractional order system with input delay and uncertainty as well as external disturbance is proved by designed controller. Finally, using the simulation results of financial as well as supply and demand systems, the performance of designed controller would be examined.

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Human facial generation of example image is used as a requirement for biometric applications for the purpose of identifying individuals. In this paper, face generation consists of three main steps. In the first step, detection of significant lines and edges of the example image are carried out using nonlinear grayscale morphology. Then, hair areas are identified from the face of sample. The final step combines images from previous steps. Similarity and matching between synthesized face sketch and artistic sketch are compared with two methods of extracting features, Principle Component Analysis and Linear Discriminant Analysis, and time of the process is calculated. The experiments on the pair of CUHK database images show that the proposed method compared with state of the art methods such as: Eigen transformation, LLE, and MRF, has no computational complexity and creates a person's face with good quality and much less time. Matching of synthesized face sketch of the proposed method is achieved with a maximum value of 90% when Linear Discriminant Analysis is used to extract feature. The proposed method is also resistant to background effects and brightness of example images.

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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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In this paper, a flexible Continuous time (CT) feedforward (FF) quadrature delta sigma modulator (QDSM) with variable bandwidth (BW) is proposed. The modulator BW and center frequency (fc) are variable which can be adjusted with two separate parameters. The modulator signal-to-noise ratio (SNR) will be optimized by these parameters at every center frequency and bandwidth. The modulator BW is changed by varies in the complex coefficients, and the structure of the modulator is no changed.
 The proposed modulator is usable in multi standard receivers as well as in receivers with variable frequency input signal or frequency hopping. The center frequency of this modulator is from 0 to 1/4 fs and its bandwidth can be changed from zero to 1/12 fs.An example of quadrature modulator is designed with the proposed design method at the circuit level. The modulator is simulated for 2MHz and 5MHz bandwidths with a sampling frequency of 64MHz. The SNR for these bandwidths are 76.42dB and 56.59dB, respectively. The FOM has been calculated to be 0.375 and 1.46 (pj / conv), respectively.

 

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In this paper, a robust model predictive control (MPC) algorithm is designed for nonlinear uncertain systems in presence of the control input constraint. To achieve this goal, first, the additive and polytopic uncertainties are formulated in the nonlinear uncertain system. Then, the control policy is chosen as a state feedback control law in order to minimize a given cost function at each known sample-time. Finally, the robust MPC problem is transformed into another optimization problem subject to some linear matrix inequality (LMI) constraints. The controller gains are determined via the online solution of the proposed minimization problem in real-time. The suggested method is simulated for a second order nonlinear uncertain system. The closed-loop performance is compared to other control techniques. The simulation results show the effectiveness of the proposed algorithm compared to some existing control methods.
 

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In this paper, a fluidic biosensor with possibility to fabricate by Micro-Electro-Mechanical Systems (MEMS) technology is proposed for biomedical mass detection and lab-on-chip applications. This is designed by electromechanical coupling of harmonic micromechanical resonators with harmonic springers as a mechanical resonator array. It can disperse mechanical wave along the array by electrostatic method using interdigitate capacitors as actuators and sensors. It has some vital advantages like: low cost fabrication method, low fluidic interference damping effect, and high sensitivity with large absorbent area. In order to estimate the sensitivity of the proposed biosensor against the mass perturbation, the measurability of capacitance changes and fluidic interference damping effect, the stimulated analysis is conducted by COMSOL. It results, a suitable sensitivity and possibility to measure the biosensor outputs by available electronic instrumentations.

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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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  Common Gate (CG) topologies are commonly used as the first stage in Transimpedance Amplifiers (TIA), due to their low input resistance. But, this structure is not solely used as a TIA and comes with other topologies such as differential amplifiers or negative resistances and capacitances. This paper deals with analyzing the effect of adding an active feedback network to a common gate topology. Generally, the feedback network is used to reduce the input resistance of the CGs topology, but in this paper it is shown that an active feedback network, which occupies a small area, not only reduces further the input resistance of CG topologies, but also forms an active inductive behavior, which can be used to resonate with the large parasitic capacitance of the photodiode and hence obtain a wide bandwidth. Mathematical analysis is done in this paper to prove the existence of this active inductor, which is also proved in the simulations. Finally, it is shown that this stage alongside its active feedback can be used as a high-speed and low-power transimpedance amplifier for optical communication applications.

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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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The modeling of high frequency electromagnetic transients and the simulation of the voltage and the current distribution in the multi-winding traction transformer's windings due to these transient waves are very important. In the present article, in addition of presenting finite element models, the coupled field-circuit approach is proposed for the modeling of high frequency electromagnetic transients in a multi-winding traction transformer. The proposed method uses two-dimensional finite element models coupled with an external circuit to model the electromagnetic transient behavior of the multi-winding traction transformer. Afterwards, the results of the presented method have been compared with the results obtained from a complete three-dimensional finite element model as well as the detail model's results and the results are validated. Finally, the validated high-frequency model has been used to study the impulse response of the transformer. As shown, the proposed approach is a simple and fast method, and also has good accuracy in modeling of the impulse voltage distribution in the multi-winding traction transformer's windings.
 

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With the increasing expansion of power systems, random factors affecting the performance of these systems have also increased. Rising demand for electrical energy, along with the aforementioned random factors, has led to uncertainty analysis methods being of particular importance in analyzing the small signal stability of the power systems. In this paper, a method based on polynomial approximation for probabilistic small signal stability analysis of the power systems is presented. Since the correct determination of unknown coefficients has a direct effect on the accuracy of the polynomial approximation method, this paper presents a method that is able to determine these coefficients with more coverage on the probable input space of the problem and in addition, is able to maintain its efficiency even by increasing the number of random input variables. After determining unknown coefficients, the load flow results and system state matrix are determined for random changes of all loads and based on Hermit's polynomial approximation. Then, the eigenvalues ​​of the system are determined and the stability of the small signal of the system is probabilistically studied. In order to evaluate the accuracy and effectiveness of the proposed method, the IEEE 14-bus benchmark system is simulated in MATLAB software and the results of the proposed method is compared with the results of the two conventional methods of Point Estimation and Monte Carlo. Examination of the results has shown that the proposed method in this paper, in addition to validity, has good accuracy and high computational speed.
 

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The very low cost of renewable energy resources and the increase of the greenhouse gas emissions and fuel cost have led to a simultaneous increase in utilizing the renewable energy resources (RESs) and electric vehicles (EVs). In this paper, a mixed-integer linear programming (MILP) model is proposed for the stochastic unit commitment problem with the aim of minimizing the operation cost and the emission in the presence of EVs and RESs. EVs with the capability of vehicle-to-grid (V2G) can operate as energy storage units in the smart grid and, if necessary, be connected to the network as generation resources. In this paper, an aggregator is responsible for coordinating the charging and discharging of EVs. The RESs uncertainties has complicated the management of electric vehicles and the unit commitment problem. Therefore, in this paper, Monte Carlo simulation method is used for modeling the uncertainties of the wind and solar power and the load demand. The simulation results show that the simultaneous utilization of the proposed MILP model and the probability distance method for reducing the number of scenarios, can minimize the operation cost of thermal units and pollutant emissions while reduces the solution time, significantly

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Sign language recognition systems help deaf people to access various media. In this paper, the Leap Motion Controller (LMC) and the image of the hand are exploited for sign language recognition. The LMC provides 3D position of the hand joints. The first set of features are extracted from the data provided by the LMC. When the hand is not located in vertical view of the LMC or when the hand posed like a fist, the precise position of the hand joints is not recognizable. The second feature extracted from hand image helps most hand gestures be recognized precisely. The second feature includes histogram of oriented gradients and the distance of the hand contour form the center of the hand. Also, a dataset composed of variant American sign language gestures is created which includes 64000 samples. In recognition stage, random forest is applied which is a good option for large datasets. The experimental results show that the proposed method performs better than similar methods.