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Obtaining the optimal control low for nonlinear systems is one of the most active subjects in the control theory. Solutions become more complicate in the presence of physical constraints, especially input constrains. In this paper, a new method has been developed to design a constrained nonlinear controller. In this method, the performance index is developed based on the predictive approach. Then, an optimization problem is solved to obtain the optimal control law in the presence of input constraints. Two constrained optimization methods have been used in this paper. The KKT-based method provides an analytical solution for obtaining the control law. The other method is based on the genetic algorithm (GA) which is considered a numerical optimization technique. These methods are implemented on an example and the results are compared

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In this study, a dual-mode Delta Sigma modulator for both ‎WLAN and GSM standards has been designed. In order to reduce the power consumption and to ‎optimize the chip area, a reconfigurable structure, which can be separately tuned for these two different ‎applications, is used. The proposed modulator is based on a multi-bit VCO instead of a conventional flash ‎quantizer. Due to the possibility of lower voltage application and lower power consumption, the VCO-‎based Delta Sigma modulator can be a good choice especially for sub-100nm technology, where the ‎performance of the analog circuits becomes limited, and conversely superior switching rates can be ‎achieved. The idea of VCO-based Delta Sigma converter has been recently developed, however the multi-‎mode reconfigurable structure is proposed and analyzed for the first time in this research. The proposed ‎modulator is dedicated to operate with the two major WLAN and GSM standards. It is designed at ‎system level in general, but the VCO part is implemented at transistor level using 180nm CMOS ‎technology. The simulations’ results show a saving of at least 86% of power consumption in the ‎VCO part, in the GSM standard compared to WLAN.‎

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Epilepsy is the most common serious brain disorder that characterized by recurrent seizures. Epilepsy affects 65 million people worldwide today and about two million new cases occur each year. The most negative aspect of seizure that causes the patient couldn’t have normal life, is its sudden and incontrollable features. So, the achievement of an algorithm that is capable to predicting the occurrence of seizures would help sufferers to live a normal life safe, and they can move out of harm's way. In this study, we proposed a prediction method for absence seizures based on the time-frequency analysis and complexity measure in EEG signals of WAG/Rij rats as a valid animal model of human absence epilepsy. We investigated the changes of permutation Entropy and the wavelet power of theta frequency range, simultaneously. The proposed seizures prediction algorithm was applied to long-term EEG recordings of WAG/Rij rats. The results indicate that the algorithm successfully detected the pre-ictal state prior to onset of seizures in 210 out of 298 seizures.The dependence of accuracy, sensitivity and anticipation time of prediction algorithm on program settings and attributes of EEG recordings are discussed.In this study, we found that the measure of PE reduced in pre-ictal and ictal states of EEG signals in these rats. The reduction of complexity of EEG signals prior to onset of seizures that was demonstrated by means of PE might be indicating the neural synchronization of brain networks in WAG/Rij rats.

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Steady-state and transient response of the active power filter based on instantaneous power theory compensation method is dependent on the accurately and quickly extract the DC component of load active power. In this paper, for improving the response of this method, the numerical filter based on recursive least squares with variable forgetting factor in order to separation of DC component of load active power is proposed. Unlike conventional low-pass filters, the feature of designed numerical filter is independent of load harmonic components, quick and precision of the answer. Also, using wavelet transform the performance of active power filter under the non-ideal voltage condition is improved. Finally, the second order delta-sigma modulation is used to generate the switching pulses. Simulation results on MATLAB / SIMULINK software shows that the proposed method is properly.

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In this paper, we investigate the design method for interval type-2 (IT2) T-S fuzzy controller based on IT2 T-S fuzzy observer for nonlinear systems along with uncertainty parameters. In order to analyze the stability and synthesis the control methods conveniently, an IT2 (T–S) fuzzy model is applied through representing the dynamic of nonlinear systems and dynamic of observer. Uncertainty parameters are captured by IT2 membership function characterized by the lower and upper membership functions. In this paper, for IT2 fuzzy controller, the membership functions and number of rules can be freely chosen different from the IT2 T–S fuzzy model and IT2 T-S fuzzy observer. This method is known non- Parallel Distributed Compensation. To reduce the conservativeness of stability analysis, a fuzzy Lyapunov function candidate is applied. The stability conditions in term of linear matrix inequlities (LMIs) are obtained.

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In this paper, the guidance law designing problem in the presence of the control loop dynamics using sliding mode control has been studied. For this purpose in the design process, stable control loop dynamic considered that usually not considered by the designers. In practice there is a lag for control loop that may lead to instability in the guidance loop. In this paper the control loop dynamic that is stabilized with an autopilot, approximated as first order lag and then is considered with kinematic equation of motion in designing procedure. To solve the problem because of the nonlinearity in equations and target maneuvers as uncertainty, the sliding mode control scheme is used. So just having the bounds of the uncertainty we can design guidance law and the measure or estimate of uncertainty is not required. The sliding variable is defined with respect to parallel navigation idea using relative lateral velocity between the interceptor and the target. Then a controller is designed for reaching the sliding variable to sliding surface. Therefore the line of sight rate will be zero and collision is inevitable. Also for removing chattering, the continues approximation method is used.

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In this study a model reference rough-radial basis function neural network controller with feedback error learning for control of a class of nonlinear systems subject to unknown bounded uncertainty is proposed. The proposed controller in hybrid form includes the classic controller and rough- radial basis function neural network controller. Because of using the classic controller with the neural network controller, it is expected that the transient response is bounded. The weights of the output layer of the neural network controller are interval variables. Using an appropriate Lyapunov function, stable adaptation laws for these weights according to the output of the classic controller and based on stability are derived. To show the efficacy of the proposed controller, results of simulation that is applied to Duffing Oscillator and Genesio- Tesi are shown and results are compared with the results when simple model reference radial basis function neural network is used as the controller. The results show that the proposed method is more robust against uncertainty when it is compared to model reference radial basis function neural network controller. Also, using the proposed controller, synchronization of chaotic systems is performed. The results verified the effectiveness of the proposed controller.

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The dynamic equations of an autonomous underwater vehicle (AUV) are described as a nonlinear system with multiple hydrodynamic coefficients which strongly affect the performance, maneuverability and controllability of AUV. On the other hand, the values of these coefficients depend on the vehicle speed and the geometric properties. In this paper, the nonlinear model identification problem of NPS AUV II, as a six degree-of-freedom (DOF) autonomous underwater vehicle, is addressed by using the nonlinear continuous-time extended Kalman filter (EKF) observer with guaranteed convergence. To this end, the hydrodynamic coefficients of AUV are considered as the augmented state variables of a six DOF nonlinear model. Based on the input-output data at the presence of the measurement noise of sensors, the state variables and the hydrodynamic coefficients of the nonlinear model in a (path) helical maneuver, are suitably estimated by using the EKF observer. In order to analyze the numerical performance of the proposed method, the dynamic equations of the vehicle are introduced, and a comparison is made between the identified model outputs and those of the real model.

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In recent years, simultaneous localization and mapping (SLAM) has become a very challenging matter as a basic problem in the mobile robots navigation. This paper describes a new efficient inertial SLAM algorithm for a UAV or an airborne. This inertialSLAM could be properly applied to two different kinds of sensors: (i)Range/Bearing sensorsand (ii)Bearing-only sensors and so it does not need to any other external positioning systems like as GPS or any preprovided data. In this study, acomprehensive system has been presented which not only enhances the three-dimensional SLAM accuracy and performance, but overcomes the two fundamental problems that have less been noticed in previous researchesA) To consider all the degrees of freedom for the UAV that lets the UAV height changesbe usedin addition to two x and y directions. B) It does not experience any limit for the symbols status which means that the system is able to observe all the symbolsin different heights based on inertia sensors. Finally, the accuracy of proposed algorithm has been approved due to the simulation results using the actual aircraft flight data.

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In this paper, a new combination of fractional order calculus and finite time sliding mode control, used to design an aircraft autopilot. This combination aims to reduce the chattering phenomena and have a smoother control signal than conventional sliding mode. Fractional order control uses fractional integrator and derivative to improved integer order control methods. The sliding surface and sliding mode control law is proposed to reduce the chattering phenomena and also, closed-loop stability is guaranteed too. Using this algorithm, a robust autopilot against aerodynamic coefficients uncertainty is designed for an aircraft and proposed control law is utilized to stabilize the close loop system by Lyapunov stability theorem. The proposed autopilot is applied to the aircraft model and simulation results illustrate the reduction of chattering phenomena.

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In this paper, electric power production using airborne systems (kites) has been investigated. In the first step, an appropriate model is extracted to describe the behavior of airborne systems. Based on this model, a new path planning algorithm is proposed for the airborne system in the traction phase. Then, in order to achieve the proper operation, tracking the desired path and thus extracting optimal wind energy, a robust controller based on the sliding mode approach is designed in the presence of variations in atmospheric parameters and uncertainties in the system model. In the proposed method, the control strategy is obtained based on the speed vector angle of the airborne. In the proposed approach, six target points are used for the path designing of the kite motion in the traction phase, which increases the precision and flexibility of the designed path. Furthermore, the effect of adjusting the shape of the flight path of the airborne system during the traction phase on the system performance and extraction of the maximum wind force is also investigated.

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In this paper joint effect of transmitter and receiver IQ imbalance under insufficient and sufficient cyclic prefix (CP) is studied. The case of insufficient CP length leads to increase in the rate of symbol transmission, on the other hand it causes inter-symbol-interference. Morever, existence of IQ imbalance in the transmitter and receiver causes distortion in the received signals. So this problem leads to increase of bit error rate. To compensation of this effect and other impairments, simultaneously, per-tone equalization (PTEQ) structure implementation is necessary. Regarding to topology of this structure and required high length filters, the system computations will be very high, even using simplest adaptive algorithms. In this work to reduce the computational burden, an adaptive algorithm based on selective coefficient updating (SCU) has been presented. In addition, to increase of the convergence speed of adaptive algorithms,wavelet packet transform (WPT) is applied to branches of PTEQ structure in second stage. Then SCU method based on the rate of wavelet papcket entropy has been used. It must be mentioned that in this method not only computational complexity has been reduced but also bit error rate has been improved. In the third stage, the combination of both SCU and data selective updating (DSU) to increase of convergence speed and to reduce of computational complexity have been derived. Simultaneous using of two DSU and SCU methods besides WPT, is suitable for compensation ofchannel effect and IQ imbalance. Simulation results show that this algorithm not only causes considerably reduction on amount of calculations, but also have better performance.

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In this paper, a finite-time stabilized guidance law is addressed in presence of some measurement noises. The measurement noise would effect on the guidance system stability and or performances. Hence, in presence of measurement noise, the guidance law must be modified such a way that the noise effect on the guidance system response would be reduced. By using the stochastic stability theory, a modified guidance law, depended on the measurement noises variance, will be proposed such that the line of sight angle rate is stabilized in a finite time. After such a finite-time, no force would be applied to the vehicle actuators. Then the line of sight angle would be a constant one. The proposed method would be used in a two-dimensional numerical example. The effectiveness of the suggested method is shown in the simulation results.

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Electromechanical damping system is essential to ensure acceptable performance. However, when an error occurs in the transmission system, power transmission lines and buses voltage fluctuated severe, To eliminate the fluctuations in the power system stabilizer (PSS) is used to optimize the overall system performance, the problem of PSS parameters to an onlinear optimization problem with the objective function of the proposed conversion and optimization algorithms using Cuckoo (COA) and the cuckoo search algorithm (CS) optimization problem is solved. Stabilizers are adjusted so that the active power oscillations in the transmission system are considerably reduced. PSS parameters in network design and simulation of four standard generators under different working conditions are evaluated and analyzed. By comparing the results of the two algorithms in the network, COA efficiency and excellence in sustainable building several power generator system to the CS algorithm is proved.

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In this paper, a new nonlinear model for magnetorheological (MR) damper based on the bouc-wen model is identified. This model including the hysteresis loop is linear with respect to the input current. This advantage makes it easy to use the proposed model in semi-active suspension systems as the inverse model. For better comparison, a polynomial model developed in the prevalent papers is also designed. The performance evaluation of two models shows that the proposed bouc-wen model has higher accuracy in the smaller velocity area. At the rest of the paper, an LQR controller designed for calculation of the active suspension force is integrated with the two identified inverse models. The simulation studies are carried out for active and semi-active suspension systems on a standard road. The results show that the simplified nonlinear bouc-wen model has better performance in controlling the semi active suspension system compared with the other models. In high amplitude velocities of the damper, the performance of two models is close to each other.

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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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Downlink channel estimation of Massive MIMO is an important challenge is 5G wireless communication.  A classic method for this purpose is training-based method which leads to decreasing in transmitted information rate. To cope with this problem, recently, a blind channel estimation method has been presented in which by assuming that the value of large scale fading coefficient is known, the channel gain is estimated in multiuser Massive MIMO system. In this paper, we propose a new method that simultaneously estimates both channel gain and large scale fading coefficient by applying two different power control gain in the coherence interval. The proposed method is applicable for ZF and MR precoding. The proposed method has higher transmitted information rate (does not need to transmit the large scale fading coefficient) and lower MSE in high SNR values with respect to reference method.

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The development of renewable energy sources, distributed generation, energy storage and nonlinear controllable loads in modern distribution networks has led to the consideration of the state estimation in intelligent and active distribution networks. The performance of the Energy Management Central's distribution network depends on the results of the state estimation. In this paper, two-stage estimation with the network reduction process is proposed. The number of distribution network meter is low, so obtaining accurate initial information from network conditions improves performance state estimation. The initial state estimation with the network reduction process to obtain accurate initial data is performed. The initial data are used as a measure to improve the performance of secondary estimation. This method solves the problem of scarcity of accurate measurements and improves the accuracy of the state estimation in distribution network. The results of the proposed methodology are demonstrated on the 69 nodes of IEEE  standard distribution network.

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In this paper, a novel structure based on Bridgeless AC/DC Converters for BLDC Motor is proposed. For now, to drive this kind of dc motors, step down and step up converters and converters with capability of increase and decrease in output voltage with bridgeless diode structures are presented. In this paper by using a novel structure, this possibility is provided to have a converter that can work in desired and separate states such as buck, boost or buck boost operations. In this paper focus will be upon buck-boost operation to access appropriate usage of both decrease and increase at output voltage advantages. Also this converter would not need to a diode bridge at primary of circuit. At output of this converter only a dc-link capacitor exists, that it will decrease the need of electrolytic capacitors that result to decreased circuit volume and cost. Removing of input diode bridge and input dc-link capacitor has caused to a better quality of input current waveforms. As well as, power factor for input power supply of this converter will work near to unit amounts.

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3D human pose estimation is one of the most significant tasks in computer vision with wide range of applications. The works for estimating human pose initialized from 2D skeletal estimation from multiple data and has proceeded toward 3D skeletal estimation from minimum input information. In this paper, 3D human pose estimation from a single RGB image is investigated. The proposed work is considered as the ones which firstly estimate 2D pose and then lift the estimated 2D configuration to 3D space. Since most of the errors in this attitude are originated by inaccurate 2D pose inference, we have proposed a method for predicting more accurate 2D poses to obtain 3D poses with less errors. The proposed approach for estimating 2D pose has leveraged deep learning along with the information of the edge map. In other words, we have made use of edge features, which are hand-designed features, in order to guide the deep neural network in training and in learning the features in accordance with the defined objective. Experimental results have demonstrated less errors in 2D and consequently 3D pose estimation in Human3.6M and HumanEva-I benchmarks.