نشریه سامانه های غیرخطی در مهندسی برق
سال هشتم شماره 2 (پیاپی 14، پاییز و زمستان 1400)

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, 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.

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.

In this paper, a new adaptive controller based on the barrier function is designed for high-order nonlinear systems with uncertainties in mind. Accordingly, this paper uses a sliding mode controller that can simultaneously create asymptotic convergence and deal with perturbations. The main problems controlling the slip mode can be considered asymptotic convergence, umbrella phenomenon, stimulus saturation, control gain estimation and failure to deal with time-varying uncertainties. In this paper, the terminal slip mode controller is used to deal with the phenomenon of asymptotic convergence and umbrella and the barrier function is used to overcome the uncertainties of time variable. The advantages of the proposed method include the elimination of the Chattering phenomenon, convergence in finite time, compatibility with time-varying uncertainties, no use of estimates and no need for information on the high limit of perturbations. Stability analysis shows that in the proposed controller, the tracking errors approach the convergence region in the zero range and provide faster convergence. Finally, to prove the efficiency of the controller, based on the chaos synchronization theory, we apply the proposed controller to a new 5D hyperchaotic system. The results show that the proposed controller, despite the disturbances applied to the system, provides rapid convergence and eliminates the umbrella phenomenon.

In this paper, a new adaptive model predictive control based on Laguerre functions is proposed for the load-frequency control problem of a multi-area power system, in which the estimation of the internal model of the power system is updated online using the recursive least squares method. The use of the adaptive reduced-order internal model in the structure of model predictive control is the innovation of this research. In the studied system, the controller of each area is designed independently so that the stability of the overall closed-loop system is guaranteed. Numerical simulations for a three-area power system are carried out to validate the effectiveness of the proposed scheme and the results were compared with those of conventional model predictive control (MPC) and proportional-integral-derivative control (PID). The simulation results show that the proposed scheme performs better than PID and MPC in rejecting step load disturbance (with respect to nominal and uncertain parameters) and nevertheless, thanks to the use of the reduced-order model and Laguerre functions, reduces the computational burden significantly compared to conventional MPC.

Electromagnetic suspension technology has been developed in recent years due to advantages such as no contact and reduced friction. Of course, ensuring efficiency in these systems requires precise control of the position of the suspended object. Therefore, electromagnetic suspension is considered as a process by control engineers. The dynamics of electromagnetic suspension systems is nonlinear and also include model and parametric uncertainties such as the weight of the suspended object. In this paper, a finite time nonlinear hybrid method is used to stabilize the electromagnetic suspension system. Proof of finite time stability of the proposed method is performed using Lyapunov theory and a relation for calculating the convergence time depends on the controller gains is presented. To ensure the finite time convergence of the system state and output variables, the backstepping algorithm is used and in each step, the finite-time convergence theory is used. The controller designed in this paper is compared with the backsteping method and the superiority of the proposed method in various simulations is shown.

This paper investigates the performance of differential protection of power transformers in the presence of internal faults, external faults, and cross-country faults in the presence of current transformers saturation, which is one of the main innovations of this study. Today, detection and discrimination of cross-country faults from other disturbances are one of the most important challenges facing protection engineers. Therefore, in this study, maximum overlap discrete wavelet transform has been used in order to accurately detect and classify these disturbances based on the extraction of energy coefficient indices of superior features. First, the cross-country faults, internal faults and external electrical faults, and inrush current phenomenon on the system under study in the EMTP software are simulated and differential current is sampled in different disturbances. Then, the mean indices of the sum of energy coefficient each level are calculated by MODWT by MATLAB software, and based on the values of indices, discrimination and classification of events are done. The results obtained from the simulations confirm that the proposed protection algorithm can detect and classify cross-country faults from other disturbances. Also, this method will improve the differential protection performance in different operating conditions and increase the reliability of power systems.