نشریه سامانه های غیرخطی در مهندسی برق
سال دهم شماره 1 (پیاپی 17، بهار و تابستان 1402)

In this paper, formation control based on the virtual structure for the non-holonomic mobile robot system with two models of certain and uncertain kinematic equations is discussed. First, the formation equations of a certain model are calculated and then it is proved that it is possible to create a geometric shape and maintain that state by using the sliding model control theory for any two moving mobile robots. Then, after deriving the formation equations of the uncertain model, a sliding model controller is designed that is able to control the uncertain model provided that the uncertainty range of the kinematic equation is present. For each design, the stability of the system is guaranteed using the Lyapunov stability theorem. Finally, in order to compare the performance of the designed controllers, a pre-designed back-stepping controller is introduced and the results will be presented in the form of simulations. The simulation results show the effective performance of the designed controllers.

Disturbance and uncertaities exist in industrial systems and greatly affect the performance and stability of these systems. The robotic manipulator is one the most widely used devices in the industry that is highly affected by various disturbances. Hence establishing a proper control algorithm to estimate and eliminate disturbances seems crucial. Since the robotic manipulator is a highly nonlinear system, we need to design a nonlinear disturbance observer. In this thesis a nonlinear disturbance observer is proposed to estimate the constant and oscillatory disturbances in the studied system. On the other hand, since proportional-derivative controllers (PD) are widely used in industrial systems, so in this thesis, a suitable proportional derivative controller will be designed. This controller is not capable of dealing with disturbances and uncertainties, so a new supervisory controller structure has been proposed to estimate disturbances and stabilize the system. The core of proposed controller uses a new sliding model controller. Finally, some comparisions with PD and super twisting sliding mode controllers have been performed in several cases and the numerical results show the advantages of the proposed controller.

Insufficient training data is one of the main challenges of utilizing deep Convolutional Neural Networks (CNNs) for Environmental Sound Classification (ESC). As a promising solution, Transfer Learning (TL) has addressed this issue by adapting a network pre-trained on a large-scale dataset to the target task. In this paper, we demonstrate that not all neurons/kernels of every layer in CNN networks are equally utilized to process the inputs of different classes, but there is a specific subgroups of neurons/kernels in every layer that play the key role in classification of every output class. Based on this observation and due to similarities that exist between feature spaces of some source and target classes, we propose to concentrate the fine-tuning process only on those neurons/kernels that do need changes and have the greatest impact on misclassifying target data. To identify these neurons/kernels, we pose a nested optimization problem for which we propose an effective evolutionary approach as solution.  Compared to the conventional fine-tuning approach, our proposed method achieves absolute improvements of about 1.9% and 2.3% in accuracy on ESC-50 and DCASE-17, respectively; remarkable improvements produced not by adding augmented data but with a more efficient utilization of knowledge stored in the pre-trained network. It is noteworthy that the computation time overhead of the proposed evolutionary method is rather small (about one third of the time required to train the model from scratch.

Usually, constrained lateral acceleration would have undesirable effects on the stability and performance of a guidance system. The composite nonlinear feedback (CNF) can be effectively used to improve the transient response of the closed-loop system in the presence of the constrained input. In this way, guidance law consists of an extra nonlinear term besides the conventional linear one. As a result, such a term adjusts the qualitative characteristics of the transient response. Meanwhile, the nonlinear term is a function of the rate of line-of-sight (LOS) angle which is not activated at origin and infinity. Thus it would be effective only in a specified region. In this paper, proportional navigation is employed for the linear term of the CNF-based guidance law. Therefore, a guidance algorithm is developed for tracking problems using the CNF idea. Applying the proposed guidance method, the closed-loop stability is analytically proved via the well-known Lyapunov stability theory. The suggested approach is simulated in a numerical example. Then the results are compared with an existing technique. As expected, guaranteeing closed-loop stability, in contrast to a similar method, the addressing scheme considerably improves the performance and transient response of the guidance system in the presence of lateral acceleration limitations.

This paper presents a new online scheme of estimating power system transient voltage instability of interconnected synchronous generators using intelligent Bayesian theory based on wide area signals from WAMS data. For this purpose, by using online measurement of the system oscillatory signals gathered from WAMS technology and developing them as a series of input-output pairs data, the system dynamic category (stable/unstable) are achieved and used for Bayesian training. The proposed scheme is an online non-model-based technique with the ability of estimating binary decisions among the power system dynamic signals. In the case of evaluating effectiveness of the developed algorithm, by using an IEEE 39 test system, different fault events with the potential of transient voltage instabilities are investigated. In this case, considering two sampling data at pre-fault and post-fault occurrence moments, the system dynamic statues are estimated. Results present ability of the proposed scheme for fast and secure estimations of the system transient voltage instability.

The issue of frequency control is very important in the power system. The presence of wind turbine in the power system makes frequency control challenging. In order to improve the frequency control of the power system in the presence of wind turbine, in this paper, a new control method is designed. In this method, the coordinated control of load-frequency control (LFC) system and superconducting magnetic energy storage (SMES) has been discussed using PD-FOPID cascade controller. The PD member in this type of controller responds to the frequency changes of the power system faster and also the FOPID member has a favorable performance against the uncertainty of the system parameters and disturbances. In this paper, the problem of owl search algorithm is solved. Considering that the owl search algorithm may get stuck in the local optimum. In this paper, solutions are presented to solve this problem of the owl search algorithm, which is called the developed owl search algorithm, and in order to improve the performance of the PD-FOPID controller, the developed owl search algorithm is used to optimally adjust its parameters. . The proposed control method with several methods including: Load frequency control (LFC) and superconducting magnetic energy storage (SMES) based on the robust controller, LFC and SMES based on the MSA-PID controller, LFC based on the MSA-PID controller with SMES and LFC based on the MSA-PID controller without SMES has been compared in four scenarios and the results show the superiority of the proposed method over the other mentioned methods. Is. The proposed method is resistant to load disturbances, disturbances caused by wind turbines, and uncertainty related to system parameters.