---

In this paper, a new output feedback control method was used based on a linear matrix inequality to control the angular position of AC servo motor shaft. The proposed control method does not need to measure all of the AC servo motor statuses; it only uses the output feedback and is robust against the uncertain servo motor parameters and the disturbances applied to it. The proposed control method was compared in several scenarios with a Standard Internal Model Control-Sliding Mode Control (SIMC-SMC) method, 2-Degree-of-Freedom Internal Model Control-Sliding Mode Controller (2DOF-IMC-SMC) method, 2-Degree-of-Freedom Internal Model Control-Proportional-Integral-Derivative (2DOF-IMC-PID) method, Standard Internal Model Control-Proportional-Derivatives (SIMC-PD) method, and Internal Model Control-Proportional-Integral-Derivative-Extended State Observer (IMC-PID-ESO) method. The simulation results show that the proposed controller has desirable performance against disturbances and uncertain parameters of the AC servo motor compared with other mentioned controllers. This method relative to other controllers decreased the error of tracking the angular position of the servo motor to 30% .The simulation was performed in the Matlab Software. 

---

Away to decrease the costs of generation and improve the performance of the grid generator, solving the problem of OPF based on line congestion management. As the power flow equation is nonlinear, this paper has performed the PSO algorithm to solve the OPF problem. By considering two technique this paper has performed the PSO algorithm for improving the performance. The first technique is to use a chaos generator to prevent PSO particles from sticking to local minimum points and the second is to consider the GSF in the WPSO algorithm structure so that the power passing through network lines can be simultaneously calculated and real power flow. Finally, the result of  WPSO-GSF algorithm which includes the bus voltage values, line losses, injection power to b buses, power passing through lines, total generation cost, setting electricity prices in two ways, UMP or LMP, depending on filling line capacity and calculating generators' profits has carried out .and also, to check the accuracy of the algorithm, the proposed method has been tested on IEEE 14-BUS, 30-BUS, 57-BUS standard networks, the results which indicate an increase in the speed and accuracy of the WPSO-GSF algorithm compared to other methods in improving the OPF problem.

---

In this paper, a real-time interactive high resolution soft tissue modeling is implemented that enriches a coarse model in a data-driven approach to produce a fine model. As a preprocess step, a set of corresponding coarse and fine models are simulated for the database. In the test step, by using a regressor, the coarse model in the test set is compared to the coarse models in the training set and the blending weights are assigned to the training coarse models. These weights are used for approximating the fine model as a linear combination of the corresponding fine models in the train set. To decrease the computational complexity, assuming that applying a force on the tissue results in a local deformation, a feature extraction algorithm is proposed that considers the displacements of the contact node and its neighbor nodes and ignores the rest. This results in a low dimensional feature vector and decreases the computational complexity. In order to compute the blending weights, a nonlinear regressor with Gaussian kernel is leveraged. To eliminate the artefacts resulting from negative weights, a nonnegative least square algorithm is used for regression. Simulation results of applying the proposed method on two soft tissue models are investigated regarding the reconstruction accuracy, computational complexity and running time.

---

 In this paper an observer-based robust fault estimation scheme is proposed for a special class of Lipchitz nonlinear systems where the disturbances and faults are assumed to be coupled with the main system states. In the considered model of system, fault is assumed to enter both of the state and output equations as an unmeasured nonlinear function and coupled with the states. The disturbances and the uncertainties are considered as nonlinear functions coupled with the states. To the best of the authors’ knowledge these conditions have not been previously considered in related papers. In the proposed approach, a Luenberger observer is designed for the estimation of faults and states of system simultaneously. The effect of system disturbances is attenuated with the L2  norm. The necessary conditions for the existence of such observer is expressed in the form of Linear Matrix Inequality. The Lipchitz constant of the nonlinear function is obtained by solving the proposed Linear Matrix Inequality. Finally, the performance of the proposed method is simulated on a three-phase induction motor. The results indicate good performance of the proposed method.
 

---

---

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. 

---

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.

---

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.

---

---

In this paper, a low power and wideband Regulated Cascode (RGC)-based Transimpedance Amplifier (TIA) is presented to be used for the short range optical receiver systems. In this structure, input dominant parasitic capacitance is isolated by adding a cascoded inverter amplifier as a fully active feedback network in the booster of an RGC amplifier. As a result, a 6.4 GHz bandwidth is obtained at a lower power consumption. In addition, for eliminating the effect of output parasitic capacitance by resonating with an inductor and widening the bandwidth, an active inductive load is implemented at the output node of the proposed TIA circuit. Therefore, considering two main points of isolation of input parasitic capacitance effect and reduction of load parasitic capacitance effect, bandwidth is increased without using a high amount of power consumption. Based on the results simulated in HSPICE using 90 nm CMOS technology, the proposed TIA can reach the data bit rate of 10Gb/s. In addition, the proposed TIA consumes only 1.6mW of power, and has the gain of 40dBΩ across the 6.4 GHz of bandwidth.

---

Nowadays, various kind of smart phones and 3D software are produced, which require large memory space. However, large number of vertices and faces in 3D models not only decrease the speed of sending and receiving of data but also can make problem in systems with low memory space. In this paper, an anisotropic re-meshing of 3D models is proposed. In this algorithm, the Nyquist theorem is employed for sampling from each selected segment of the mesh, locally. Then, the re-triangulation algorithm is applied to the selecte samples to construct the simplified mesh. In order to construct a high quality mesh from the remeshed model, a non linear subdivision is employed. The achieved results show that the algorithm can reduce the number of vertices and faces beside preserving details of model. The proposed method is also compared to the state-of-the-art algorithms are used in simplification studies, the outcomes illustrate the ability of the proposed method in producing high quality models.
 

---

This paper considers the design of observer-based distributed adaptive controllers to achieve a leader-follower consensus for high-order nonlinear multi-agent systems in the presence of non-symmetric input saturation constraint and system uncertainties. Solving the consensus problem for nonlinear multi agent systems in the presence of unknown terms in the dynamics equations of follower that are due to model simplification, parameters uncertainty or external disturbances are investigated. In order to reduce the conservatism, the upper bound of uncertain term is considered to be unknown, which is obtained by adaptive laws. In addition, it is assumed that all states variables of agents are not directly measurable; therefore firstly, by designing the nonlinear distributed observers, the states variables of agents are estimated. Then, by using the sliding mode technique, the observer-based distributed adaptive control laws are designed to ensure the consensus between the agents, and the output of the followers can track the output of the leader, in the presence of non-symmetric input saturation and uncertain terms. Finally, the results of the simulations have completely confirmed the achievements of the proposed laws.

---

This paper is concerned with the problem of output feedback fuzzy mixed H2/H_inf tracking control design for nonlinear systems. A general and modified class of output-feedback controller structure is assumed. Using parallel distributed compensation, a fuzzy controller is proposed which not only satisfies an H_inf tracking control constraint, but also optimally minimizes a H2control performance measure. The proposed method leads to a trade-off between the tracking performance and the amount of control input effort. The problem formulation and the method of finding the optimal fuzzy tracking controller parameters involve a single step linear matrix inequalities form. On a benchmark example, the proposed method is applied on the inverted-pendulum system and is compared with traditional H_inf-only results from diffrent perspectives.
 

---

Owing to an increasing need of control community for providing a precise and integrated model of natural and practical structures switching systems have attracted much attention. On the other hand, the multi-model inherent in many practical systems has increased the importance of reviewing these types of systems. In this paper, the problem of adaptive fault tolerant finite-time control of a class of nonlinear switching systems in the presence of actuator fault, external disturbances and dead-zone input nonlinearity is investigated. The boundary of the uncertain terms of the system is assumed to be unknown and adaptive rules are used to eliminate the destructive effects of these terms on the system response. The subsystems of switching system are considered as nonlinear systems with a canonical structure. This paper sets no restrictive assumption on the switching logic of the system. Therefore, the purpose is to propose a controller that works under any desired switch signal and can overcome the actuator fault, disturbances and dead-zone input nonlinearity. To achieve this purpose, after providing a smooth sliding manifold, an adaptive control input is developed such that the system trajectories approach the prescribed sliding mode dynamics in finite-time sense. Finally, by using the Lyapunov stability theory, it is proved that the origin is the finite-time stable equilibrium point of the overall closed-loop system. The simulation results provided by MATLAB software show the performance of the proposed controller.

---

Abstract: This paper designs an optimal controller for the simultaneous determination of physical model parameters and LQR controller parameters. In some systems, it is possible to determine some of the model parameters by the designer. In conventional methods of optimal controller design for this group of systems, first, the model parameters are determined by the designer, and then in a separate step, the controller is designed for the definite model. In this paper, a method for the simultaneous determination of these two sets of parameters is presented for continuous-time linear systems. Simultaneous parameter determination is a nonlinear and non-convex optimization problem that in this paper a new method is considered to solve this problem. The non-convex optimization problem is transformed into a convex optimization problem by performing simplifications and then solved by the CVX toolbox of MATLAB software. The result is a controller with less control cost in comparison to conventional methods for this group of systems. By providing a simulation example, the performance improvement of the proposed method is shown.

---

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.

---

Today, differential relays are used in order to protect power transformers against all kinds of faults and events. Despite advances in relay fabrication technology, the detection and discrimination of different events is still one of the most important challenges for the protection engineers in this field. In this paper, an intelligent hybrid method has been proposed to detect and classify internal electrical faults, external faults while saturating Current Transformers (CTs) and inrush current in transformers. First, the internal and external fault currents and the inrush currents of power transformers are simulated by the Real-Time Digital Simulator (RTDS) and its software package (RSCAD). Then, the sampled signals in different events are transmitted to MATLAB software for detection and discrimination. At this stage, using the Bayesian Classifier method, which directly evaluates the training data information, external faults are separated from the other operating conditions of the transformer. Then, other events such as inrush current and internal electrical faults will be distinguished from each other by Decision Tree (DT) and Support Vector Machine (SVM) methods. The results show that the proposed intelligent hybrid protection method has the ability to detect and classify different disturbances in transformers in real time state with appropriate accuracy, which is one of the main innovations of this study compared to other published research.

---

In this paper, a control input based on terminal sliding mode control is provided for a mobile robot with four Mecanum wheels to move in a predetermined path and convergence into the path in a fixed-time. First, according to the robot structure, a dynamic model of the robot is presented. The dynamic model follows a nonlinear second-order equation. Based on terminal sliding mode control, a nonlinear sliding surface which is a function of position error vector is defined and then the control input is designed based on this sliding surface. Using the Lyapunov theorem, it has been proven that, using this control input, the robot converges to the predetermined path at a fixed time. The convergence time is a function of the constants defined in the control input. Finally, the simulation is presented based on the control input and the results are shown.

---

Face plays an important role in visual communication. By looking at the face, it can be automatically extracted many non-verbal messages, such as identity, intention, and emotion. In computer vision, localization of the key points of the face is usually a key step for automatic extraction of face information, and many facial analysis techniques are built on the precise recognition of these embossed. Facial landmark detection and alignment in images with occlusion is a very important and challenging task in many visual and image processing tasks. In this paper, a comprehensive method for initialization and alignment of facial landmark through training of local binary features (LBP) and histogram orientated gradient (HOG) and a facial landmark detection method using robust cascade pose regression, which are specified as pixel difference features of landmarks, is introduced. At first, by analyzing the correlation of the local binary pattern histogram (LBP) and then by using histogram orientated gradient, the features of the training images are obtained. For the test image using these features the instructional images are estimated as optimal guide points. In the test stage, according to initialization of the image, the selection of the appropriate feature for the image is used to speed up the process, which means the number of steps to be chosen for each image is better. A strong cascade mode regression is then used to adjust the face, and a local principle is applied to learn the features of the guide points. The local principle helps to learn a set of highly distinctive binary features for the face guide points independently; these local binary features are used to jointly learn the cascade mode regression for the final output. The results show that the initialization used in this work has increased the accuracy of the estimation in the cascade state regression and has obtained better results than the random initialization.

---

In this paper, the problem of stabilization and synchronization of Lorenz and Chua chaotic in the presence of uncertainty using fractional order sliding mode control strategy based on nonlinear adaptation law has been investigated. Lorenz and Chua systems denote third order dynamics models which are chaotic for certain parameters. The proposed control law is composed of two prats sliding mode control and adaptive control law. Firstly, by supposing that instantaneous information of nonlinear part of chaotic system is not available, a linear regressor equation including an unknown section has been used. Using Lyapunov stability theorem and based on fractional calculus, adaptation law is developed to instantaneous estimation of unknown part. Moreover, by defining based on error signals and realizing exponential reaching law for insuring closed-loop stability, the sliding mode control law including equivalent and switching control has been derived. Eventually, the final control law has been derived by synthesizing sliding mode control and adaptive laws. The important aspect of the proposed approach is ability to encounter unstructured uncertainties and nonlinear effects of chaotic systems dynamic and guiding the state variables into sliding surface for arbitrary initial conditions. The performance of the proposed algorithm has been evaluated by realizing the stabilization problem of chaotic Lorenz system and synchronization of chaotic Lorenz and Chua systems.