International Journal of Industrial Electronics, Control and Optimization
Volume:4 Issue: 1, Winter 2021

In this study, an adaptive controller for LTI systems with unknown and time varying input time delay is presented with the purpose of tracking. Due to the large area considered for time delay variations, the structure of the proposed controller is considered to be in form of Multiple Model Adaptive Control (MMAC). The presented adaptive control system is of indirect type, i.e., at any moment of time, first, one band of time delay is identified using a proposed estimator, and then with a switching rule in the supervisory subdivision, the main control signal, which is a linear combination of multiple controllers output, forms. In fact, each of the multiple controllers in MMAC structure with optimal weights, participate in forming the main control signal. The multiple controllers used in this study are of PID type. It should be noted that the parameters for each of the multiple controllers, for the system under control, are adjusted offline and proportional to its corresponding time-delay sub-band using the genetic algorithm. Finally, simulation results show the relatively desirable performance of the proposed control system and observer in facing with large unknown and time varying delays.

Recent research on the backstepping control of robotic systems has motivated us to design a robust backstepping voltage-based controller with computational simplicity and ease of implementation. In this paper, an adaptive robust tracking controller based on backstepping method (ARTB) is presented for uncertain electrically-driven robotic manipulators in the framework of voltage control strategy. It is intended to convert robot control problem to motor control problem. In the design procedure, the manipulator dynamics are incorporated into a lumped uncertainty, such that the proposed adaptation law promptly compensates for it. Hence, high tracking accuracy, robust behavior and less complexity are the prominent features of the proposed control system in the presence of external disturbances, parametric uncertainties and un-modeled dynamics. Moreover, the control approach is useful for high-speed tracking purposes. The stability of the closed-loop system is guaranteed based on the Lyapunov theory and the tracking error converges to zero asymptotically. As a case study, the proposed ARTB is simulated on a two-link robot manipulator driven by permanent magnet DC motors. Numerical simulations are included to show the superiority of the proposed controller to a state augmented adaptive backstepping method, a sliding backstepping controller and an adaptive backstepping sliding mode control in tracking the desired trajectory.

Today, stock market plays a key role in the economy of any country and is considered as one of the growth indicators of any economy. Gaining the skills of gathering and analyzing data simultaneously, as well as using this knowledge in economic investigations, make time and precision factors to be the drawcard of any investor in competition with others. Therefore, having a predictive approach with the lowest degree of error will lead to smarter management of resources. Due to the complex and stochastic nature of the stock market, conventional forecasting approaches in this field have usually faced serious challenges, most notably losing the robustness when the data type changed over time. Moreover, by focusing on point forecasting, some useful statistical information about the objective random variable has been ignored inadvertently, undermining the prediction efficiency. The focus of this study is on density forecasting models which, unlike point forecasting, contain a description of uncertainty. Also, to take advantage of the diversity and robustness features of the combination, instead of an individual prediction, a combination of the density forecasting given by the different structures of ARMA, ANN, and RBF models is presented. In order to analyze the capabilities of these approaches in Tehran Stock Exchange (TSE), two basic methods of this category have been used to predict the price of MAPNA stock -one of the fifty active companies in this market- in the period 2012 to 2019.

Fossil fuel combustion in power plants is the world’s most significant threat to people’s health and the environment. Recently, wind power, as a clean, sustainable and renewable source of energy, has attracted many researchers. The present paper studies how to maximize the extraction of wind power and the efficiency of a switched reluctance generator (SRG) by firing angles control. The proposed scenario comprises the optimization of turn-on and turn-off angles in the offline mode using a particle swarm optimization algorithm to control the system in the online mode with linear interpolation. The present approach simultaneously investigates the firing angles; also, it has simple structure, low execution time, and efficient convergence rate that are independent of machine characteristics (regardless of high nonlinearity of SRG). Furthermore, copper losses, as well as switching and conduction losses of semiconductors, were considered in simulations to achieve a more realistic outcome. Ultimately, the simulation results of a typical three-phase 6/4 generator using Matlab confirmed the validity of the presented control strategy that can easily find applications in the future.

In this paper a novel and simple approach for detection and classification of wide variety range of power quality(PQ) events based on discrete wavelet transform (DWT) and correlation coefficient is presented. For this purpose, two new indices is proposed and by comparing the values of the correlation coefficient between the value of these indices for pre-stored PQ events and for a recorded indistinct signal, type of PQ events will be detected. This algorithm has advantages of DWT and correlation coefficient which, it does not have disadvantage of neural network or neural network-fuzzy based algorithms such as; training, and high dimension input matrices nor it does not have disadvantage of Fourier transform based approach such as unsuitability for non-stationary signal as it does not track signal dynamics properly due to limitation of fixed window width. The effectiveness of this method has been tested using numerous PQ disturbance and simulation results confirm the competency and the ability of the proposed method in PQ disturbances detection and automatic diagnosis. Compared with the other methods, the simulation under different noises conditions, verify the effectiveness of noise immunity, and relatively better accuracy of the proposed method.

An RF energy harvester (rectenna) consists of a broadband monopole antenna and a quad-band rectifying circuit is designed to harvest EM wave energy in the frequency range of 1.412 GHz to 8.56 GHz, which covers GSM-1800, LTE-band, Wimax, Wi-Fi, and WLAN. The initial component of the rectenna is an antenna that includes a semi-circular radiating patch with 8 circular stubs and a semicircle ground plane. The simulation results show the antenna has −10 dB impedance bandwidth at 7.148 GHz (from 1.412 GHz to 8.56 GHz). The second part of the rectenna is a rectifier circuit with a quad-band matching network for RF to DC conversion. The rectifier benefits from a two-stage Dickson rectifier using Schottky diodes. The RF-DC conversion efficiency and output DC voltage are simulated, and the maximum output voltage of the rectifier with the optimum load resistance of R=12 kΩ is 7.2 V, and the peak conversion efficiency is 65.3% when the input power to the rectifier is -4 dBm at 1.71 GHz.

In this paper, a virtual inertia control strategy based on linear feedback is presented that improves dynamic behavior of islanded dc microgrids interfaced with constant power loads (CPLs). In order to solve the stability challenges caused by low inertia and CPLs, the proposed control scheme is composed of a virtual capacitor and a virtual conductance. It is implemented in the inner loop control, i.e. current loop control to be fast enough emulating inertia and damping concept. In addition, the droop characteristic is modeled by using the virtual resistance which adjusts the steady-state response of the system. In this study a multi-level structure is considered, which comprises the source level, interface converter level, and common load level. In addition, an accurate small-signal model is used to investigate the stability of dc MG interlaced with CPLs, and then, an acceptable range of inertia response parameters is determined by using the root locus analysis. Performance of the proposed control structure is demonstrated through numerical simulations.

This paper presents a novel topology of permanent magnet brushless DC motors. Brushless DC motors usually experience torque ripple mainly caused by cogging torque. In the proposed motor, the whole structure of the rotor’spermanent magnets hasbeen changed so that the cogging torque is considerably mitigated. The philosophy behind this modification depends on the  way  of  flux-path  production  in  the rotor  structure  and  it  should  be  similar  to  the  way  of  motor  phases. Aninitial electromagnetic analysis is, first,carried out using the finite element method. Asensitivity analysis is, next,included to obtain the  most  important  design  parameters  for  the  proposed  structure.  The  different  performance  parameters of  the  motor are calculated and compared betweenthe proposed structures and the conventional BLDC structures. The results revealthat the proposed  motor  has  a  considerably lowertorque  ripple  retaining an average  value  of  the  produced  torque.  The  proposed structure  is  also  compared  with an asymmetrical  V-type  structure  and  the  results  further  show  the  effectiveness  of  the proposed structure.

In this paper two linear constrained cooperative distributed extended dynamic matrix control (CDEDMC) and adaptive generalized predictive control (CDGPC) are proposed to control the uncertain nonlinear large-scale systems. In these approaches, a proposed cooperative optimization is employed which improves the global cost function. The cost values and convergence time are reduced using the proposed cooperative optimization strategy. The proposed approaches are designed based on the compensation of the mismatch between linearized and nominal nonlinear models. In CDEDMC the mismatch is considered as a disturbance and compensated; Also in CDGPC it is compensated using online identification of the linearized model. The typical distributed linear algorithms like DMC leads to an unstable response if the reference trajectory is a little far from the equilibrium point. This problem will be partially solved using the CDEDMC and will be completely solved using the CDGPC even if the reference trajectory is too far from the equilibrium point. The performance and effectiveness of proposed approaches are demonstrated through simulation of a typical uncertain nonlinear large-scale system.

Finding effective solutions to enhance the process of electric vehicles’ charging is the main subject of numerous studies. In this paper, a novel bidirectional multiport rectifier has been presented which can be used as a wall-box converter that is installed in the parking lot of smart buildings and is able to provide DC-link for local DC loads such as DC home appliances and charge connected EV, simultaneously. The proposed converter is capable of working in G2H/G2VH/V2H/V2G modes which enables the utility grid and costumer to use the EV as a mobile power source and reactive power compensator. A control method is also presented which enables the converter to control active and reactive power according to the smart grid or customer processed commands. In order to validate features of the proposed converter, it is simulated in MATLAB/SIMULINK and results are analyzed. A reduced-scale experimental setup of the proposed converter is built and tested and experimental results confirm simulation ones.

Synchronous generators are one of the most important components of power systems. Problems a generator may face are internal faults, system disturbances, or operational hazards. The operation of a generator may easily be affected by faults within the machine itself as opposed to external disturbances occurring on the network to which it is connected. Generator protection must therefore be designed to react efficiently in both conditions. Loss of excitation (LOE) is a common fault in synchronous generators. The most common causes of LOE include the loss of field to the main exciter, accidental tripping of the field breaker, short circuits in the field circuit, and poor brush contact in the exciter. The most widely applied method to detect a generator loss of field condition on major generators is the use of distance relays to sense the variations of impedance as viewed from the generator terminals. This approach may not be able to distinguish between LOE and stable power swing (SPS). This paper further explores a new method proposed for LOE detection and corrects its shortcomings. It also presents a new approach for LOE detection that exploits a combined scheme based on the derivative of the terminal voltage and the derivative power angle of the generator. Comprehensive simulation studies are conducted on various generator conditions and system disturbances to determine the relay setting and to evaluate its performance. These studies demonstrate that the proposed strategy enhances the security and operation time of the LOE relay compared with some existing methods.

a new algorithm is presented to reduce the uncertainty effects of wind farms power generation (WFPG) and photo-voltaic generation (PVG) in both day-ahead energy and ancillary services markets. Firstly, this research tries to predict the uncertainty of short-term WFPG with acceptable accuracy. Indeed, it uses the hybrid method of wavelet transform (WT) in order to reduce the fluctuations in the input historical data along with the improved artificial neural network (ANN) based on the nonlinear structure for better training and learning. In addition, regarding the high-level penetration of wind farms (WFs) on the power system, cascaded hydro units (CHUs) and pump-storage units (PSUs) are taken for the first time as supplementary units. Therefore, they are coordinated with WFs and photo-voltaic (PV) operations. Considering uncertainties of energy price, spinning and non-spinning reserves in the electricity market, WFPG, PVG and the availability of WFs, PV, CHUs and PSUs along with their effects on energy supply reliability lead to a scenario-based stochastic optimization problem. The aim of this problem is to increase the profit and decrease the financial risk (FR) of all of the units. The proposed method is implemented on WFs, PV, CHUs and PSUs of IEEE 118-bus standard system. Studying the results of profit and FR in the coordinated operation (CO) and the independent operation (IO) confirms that the profit is increased and the FR is reduced in the CO. Hence, the ability and merit of hybrid method of WT-ANN-ICA is verified.