نشریه سامانه های غیرخطی در مهندسی برق
سال هشتم شماره 1 (پیاپی 13، بهار و تابستان 1400)

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 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.

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.

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.

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.

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 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.

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.