Showing papers in "Automatika in 2022"
TL;DR: In this article, the authors proposed DC-AC Dual-leg dual-stage conversion (DDC) and DDC-AC Direct single-stage conversions (DSC), which can handle a wide range of voltage.
Abstract: This paper proposes DC–AC Dual-leg dual-stage Conversion (DDC) and DC–AC Direct single-stage conversions (DSC). Conventional energy conversion system has only two-stage conversion, so it has some drawbacks such as huge power loss, less conversion range and lower power rating. So direct conversion, dual-leg step-up and step-up conversions are the solutions to get wide voltage conversion efficiently. The proposed converter can perform the power conversion from battery DC supply into AC with 1:1 ratio, step-up AC, and step-down AC in both directions. Also, it can perform rectifier operation from grid AC supply into DC with 1:1 ratio, step-down DC, and step-up DC. Step-up, step-down and ideal operations are possible within a single circuit; its operation is similar to solid-state DC–AC/AC–DC transformer. The ideal operation, Step-down to Step-up conversion and Step-up to Step-down conversion are possible on both sides, so this converter can handle a wide range of voltage. Power distribution is achieved with voltage regulation between battery/DC-load and AC-load/grid using the proposed control strategy with proper modulation. A prototype model of a 2-kW power rating validates the advantages and feasibility of the proposed methodology.
24 citations
TL;DR: In this paper , a DC-AC three phase bidirectional converter (DATBC) with an encapsulated DC-DC converter (EDC) for the energy storage system (ESD) is analyzed and investigated.
Abstract: ABSTRACT A newly designed DC–AC three phase bidirectional converter (DATBC) with an encapsulated DC–DC converter (EDC) for the energy storage system (ESD) is analysed and investigated in this research paper. By using encapsulated or embedded or hidden DC–DC converter a stable and constant DC bus is developed between the encapsulated DC–DC converter and DC–AC three phase bidirectional converter. The proposed converter is entirely different from the traditional dual-stage DC–AC converter, because it takes less than 20% of power used for the DC–AC conversion process. So, this reduced power consumption increases efficiency to a considerable value. A new control technique for zero sequence has been adopted components are inserted in the modulating signal based on carrier pulse width modulation (CPWM). Working principle, implementation and characteristics of the DC–AC three phase bidirectional converter are analysed. Effectiveness and feasibility of the developed converter are examined with a proto-type model.
16 citations
TL;DR: In this paper , the authors proposed a layered switching paradigm that employs packet-based transfer techniques to prevent DoS attacks in Cyber-Physical Systems (CPPSs) by comparing sensor-controller and controller-to-actuator DoS attack channels.
Abstract: The internet, like automated tools, has grown to better our daily lives. Interacting IoT products and cyber-physical systems. Generative Adversarial Network's (GANs') generator and discriminator may have different inputs, allowing feedback in supervised models. AI systems use neural networks, and adversarial networks analyse neural network feedback. Cyber-physical production systems (CPPS) herald intelligent manufacturing . CPPS may launch cross-domain attacks since the virtual and real worlds are interwoven. This project addresses enhanced Cyber-Physical System(CPS) feedback structure for Denial-of-Service (DoS) defence . Comparing sensor-controller and controller-to-actuator DoS attack channels shows a swapping system modelling solution for the CPS's complex response feedback. Because of the differential in bandwidth between the two channels and the suspects' limited energy, one person can only launch so many DoS assaults. DoS attacks are old and widespread. Create a layered switching paradigm that employs packet-based transfer techniques to prevent assaults. The discriminator's probability may be used to assess whether feedback samples came from real or fictional data. Cognitive feedback can assess GA feedback data.
13 citations
TL;DR: A new optimum interval type-2 fuzzy fractional-order controller for a class of nonlinear systems with incipient actuator and system component faults is introduced and its effectiveness is demonstrated when compared to IT2FPID and existing passive fault tolerant controllers in recent literature.
Abstract: A new optimum interval type-2 fuzzy fractional-order controller for a class of nonlinear systems with incipient actuator and system component faults is introduced in this study. The faults of the actuator and system component (leak) are taken into account using an additive model. The Interval Type-2 Fuzzy Sets (IT2FS) is used to design an optimal fuzzy fractional order controller, and two different nature inspired metaheuristic algorithms, Follower Pollination Algorithm (FPA) and Genetic Algorithm (GA), are used to optimize the parameters of the fuzzy PID controller and Interval Type-2 Fuzzy Tilt-Integral-Derivative Controller (IT2FTID) for nonlinear system. The suggested control approach consists of two parts: an Interval Type-2 Fuzzy Logic Controller (IT2FLC) controller and a fractional order TID controller. Additionally, the two inputs of the IT2FLC are also calibrated using two fine tuning parameters and , respectively. The stability of the proposed controller is presented with some conditions. In addition to unknown dynamics, some unknown process disturbances, such as rapid changes in the control variable, are taken into account to check the efficacy of the proposed control scheme. Two nonlinear conical two-tank level systems are used in the simulation as a case study. The performance of the suggested approach is also compared to that of a widely recognized Interval Type-2 Fuzzy Proportional-Integral-Derivative (IT2FPID) Controller. Finally, the proposed control scheme's fault-tolerant behaviour is demonstrated using fault-recovery time results and statistical Z-tests for both controllers, and the proposed IT2FTID controller's effectiveness is demonstrated when compared to IT2FPID and existing passive fault tolerant controllers in recent literature.
10 citations
TL;DR: In this article , a sliding mode controller (SMC) is designed to track 2D image features in an image-based visual servoing task, which helps to keep the image features always in the camera field of view and thereby ensures the shortest trajectory of the end-effector.
Abstract: As vision is a versatile sensor, vision-based control of robot is becoming more important in industrial applications. The control signal generated using the traditional control algorithms leads to undesirable movement of the end-effector during the positioning task. This movement may sometimes cause task failure due to visibility loss. In this paper, a sliding mode controller (SMC) is designed to track 2D image features in an image-based visual servoing task. The feature trajectory tracking helps to keep the image features always in the camera field of view and thereby ensures the shortest trajectory of the end-effector. SMC is the right choice to handle the depth uncertainties associated with translational motion. Stability of the closed-loop system with the proposed controller is proved by the Lyapunov method. Three feature trajectories are generated to test the efficacy of the proposed method. Simulation tests are conducted and the superiority of the proposed method over a Proportional Derivative – Sliding Mode Controller (PD-SMC) in terms of settling time and distance travelled by the end-effector is established in the presence and absence of depth uncertainties. The proposed controller is also tested in real-time by integrating the visual servoing system with a 6-DOF industrial robot manipulator, ABB IRB 1200.
9 citations
8 citations
TL;DR: This paper proposes a new hybrid algorithm called H-GA-GSA, created by combining the advantages of the Genetic Algorithm and Gravitational Search Algorithm to optimize FLC to reach the best speed control performance of the permanent magnet synchronous motor (PMSM).
Abstract: Fuzzy logic controllers (FLCs) are widely used to control complex systems with model uncertainty, such as alternating current motors. The design process of the FLC is generally based on the designer’s adjustments on the controller until the desired performance is achieved. However, doing the controller design in this way makes the design process quite difficult and time-consuming, so it is often impossible to make a suitable and successful design. In this study, the output membership functions of the FLC are optimized with heuristic algorithms to reach the best speed control performance of the permanent magnet synchronous motor (PMSM). This paper proposes a new hybrid algorithm called H-GA-GSA, created by combining the advantages of the Genetic Algorithm (GA) and Gravitational Search Algorithm (GSA) to optimize FLC. The paper presents a convenient adjustment and design method for optimizing FLC with heuristic algorithms considered. To evaluate the effectiveness of H-GA-GSA, the proposed hybrid algorithm has been compared with GA and GSA in terms of convergence rate, PMSM speed control performance and electromagnetic torque variations. Optimization performance and results obtained from simulation studies verify that the proposed hybrid H-GA-GSA outperforms GA and GSA.
6 citations
TL;DR: In this article , a new direct power synergetic-sliding mode (DPSSM) technique based pulse width modulation (PWM) strategy for doubly-fed induction generator (DFIG) integrated to variable speed dual-rotor wind power (DRWP) systems is designed.
Abstract: In this work, a new direct power synergetic-sliding mode (DPSSM) technique based pulse width modulation (PWM) strategy for doubly-fed induction generator (DFIG) integrated to variable speed dual-rotor wind power (DRWP) systems is designed. The designed strategies produce voltage pulse width locations identical to those of a classical two-level inverter. In this novel control a synergetic-sliding mode (SSM) command and PWM technique to replace the hysteresis comparators and the switching table, for generating the reference voltage using PWM strategies for a classical inverter. Compared with traditional direct active and reactive powers control (DARPC), in this novel strategy, the switching frequency is maintained constant, and the undulations of the reactive power, current, torque, and active power are minimized remarkably. Simulation results verify the validity of the designed strategy.
6 citations
TL;DR: In this article , three multi-objective optimization algorithms (MOOAs) have been utilized to mitigate the attenuation of underwater sensors in water due to the water tide, namely MOSFP, SPEA2 and NSGA-II.
Abstract: “Extremely High Frequency (EHF)” and “Very high frequency (VHF)” bands are mainly utilized with “Underwater Wireless Sensor Networks (UWSNs)” for communication purposes. However, due to the mobility of underwater sensors in water because of the water tide, the EHF/VHF signals may attenuate, lose or fade depending on the condition of the water. Therefore, it is a challenging stint of finding the optimal parameters of UWSN topology planning. In this paper, three “Multi-Objective Optimization Algorithms (MOOAs)” have been utilized to mitigate this problem, namely MOSFP, SPEA2 and NSGA-II. This work also intends to minimize path loss. On the other hand, it intends to maximize the power density of the network. Various network configurations, such as distance between sender and receiver, water conductivity and water permeability, are considered to evaluate the proposed objective models. Qualitative and quantitative tests have been conducted to analyze the results. From the analysis of the intersection point of Pareto-front of the objective functions, it is shown that all the algorithms find the optimal distance between transmitter and receiver, which balances the aforementioned maximization and minimization objective functions. This value is 36 m.
5 citations
TL;DR: In this article, a decentralized robust interval type-2 fuzzy model predictive control for Takagi-Sugeno large-scale systems is studied and the MPC is designed for a nonlinear IT2 fuzzy large scale system with uncertainties and disturbances.
Abstract: In this manuscript, decentralized robust interval type-2 fuzzy model predictive control for Takagi-Sugeno large-scale systems is studied. The mentioned large-scale system consists a number of interval type-2 (IT2) fuzzy Takagi-Sugeno (T-S) subsystems. An important matter and necessities that limit the practical application of model predictive control are the online computational cost and burden of the existence frameworks. For model predictive control of T-S fuzzy large-scale systems, the online computational burden is even worse and in some cases, they cannot be solved in an expected time. Especially for severe large-scale systems with disturbances, existing model predictive control of T-S fuzzy large-scale systems usually leads to a very conservative solution. So, researchers have many challenges and difficulties in finding a reasonable solution in a short time. Although, more relaxed results can be achieved by the proposed fuzzy model predictive control approach which adopts T-S large-scale systems with nonlinear subsystems, many restrictions are not considered in these approaches. In this paper, challenges are solved and the MPC is designed for a nonlinear IT2 fuzzy large-scale system with uncertainties and disturbances. Besides, online optimization problem is solved and results are proposed. Consequently, online computational cost of the optimization problem is reduced considerably. At the end, by two practical examples, the effectiveness of the proposed algorithm is illustrated.
5 citations
TL;DR: In this paper , an automatic respiration system model is designed and controller parameters are tuned using hybrid Optimization techniques and the Integral Square Error is considered as the objective function of the optimization technique to find the controller parameters.
Abstract: Artificial ventilation is widely used for various respiratory problems of human beings. The oxygen level of the corona patients has to be maintained for smooth breathing which is very difficult. For achieving this state, the air pressure should be controlled in the respiration system that has a piston mechanism driven by a motor. An Automatic respiration system model is designed and controller parameters are tuned using hybrid Optimization techniques. Hybrid Controllers like genetic algorithm based Fractional Order Proportional Integral Derivative controller (FOPID), Fmincon-Pattern search Algorithm based Proportional Integral Derivative (PID) controller, and Hybrid Model predictive control (MPC) – Proportional Integral Derivative (PID) controllers were designed and verified. Integral Square Error is considered as the objective function of the optimization technique to find the controller parameters. The output responses of all three hybrid controllers are compared based on the error indices, time domain specifications, set-point tracking and Convergence speed graph. The genetic algorithm-based FOPID controller gives better results when compared with the Fmincon-Pattern search Algorithm based Proportional Integral Derivative (PID) controller and Hybrid Model predictive control (MPC) – Proportional Integral Derivative (PID) for the proposed artificial ventilation system.
TL;DR: In this article , an extended Kalman filter (EKF) estimator and Takagi-Sugeno-Kang (TSK) fuzzy logic controller (FLC) were used for sensorless speed control of DC motor.
Abstract: In this article, sensorless speed control of DC motor has been proposed using the extended Kalman filter (EKF) estimator and Takagi–Sugeno-Kang (TSK) fuzzy logic controller (FLC). In the industry, high-cost measurement systems/sensors are necessary for better controlling and monitoring, which can be replaced by a sensorless control technique to reduce the cost, size and increase system reliability and robustness. EKF has been used to perform the sensorless speed control by estimating the speed of the DC motor using the armature current only and TSK-FLC is used to reduce the effect of motor parameter variation and load torque nonlinearity in close loop speed control for various speed references. The performance of EKF-based TSK-FLC is compared with EKF-based PID controller. The time-domain specification and absolute error performance indices indicate that EKF-based TSK-FLC is superior to the EKF-based PID under similar conditions. The proposed system is executed in the MATLAB/Simulink environment, and sensorless speed control of DC motor prototype model has been developed for validating the proposed technique with the help of a micro-controller.
TL;DR: In this article , a robust control approach for a nonlinear uncertain vehicle suspension system with time-delayed actuation and bounded disturbances is presented, where sliding mode and backstepping methods have been used to overcome them.
Abstract: This paper presents a novel robust control approach for a nonlinear uncertain vehicle suspension system with time-delayed actuation and bounded disturbances. Three factors affect the stability and performance of the suspension system: (1) Uncertainty that arises from the difference between the model and the real system. (2) The disturbances that enter mostly from the side of the road to the suspension system. (3) Input delay that occurs by actuator performance. In this study, all three components are considered simultaneously, and sliding mode and backstepping methods have been used to overcome them. A nonlinear model is considered to more accurately describe the behaviour of the suspension and controller design. The Lyapunov function is inspired by the backstepping algorithm, and stability in the Lyapunov concept is obtained for the closed-loop system under the proposed robust finite time control. To demonstrate the capabilities of the proposed controller, simulation scenarios are considered in the MATLAB environment. Simulation results verify good active suspension performance regardless of the presence of unknown time delay and disturbances in the nonlinear model.
TL;DR: In this paper , an attempt is made to improve the performance of permanent magnet DC (PMDC) motor using third order sliding mode control using MATLAB/Simulink simulation.
Abstract: ABSTRACT In this paper, an attempt is made to improve the performance of permanent magnet DC (PMDC) motor using third order sliding mode control. From the derived mathematical modelling for buck converter fed permanent magnet DC motor, expressions for both classical sliding surface (CSS) and proportional integral derivative sliding surface (PIDSS) with the third order sliding mode control is derived and compared analytically. Simulation work is done for PI controller, sliding mode control (SMC), third order CSS and third order PIDSS by using Matlab/Simulink to validate the performance of the above said controllers under no-load condition and various load torque conditions such as: constant load torque, frictional load torque, fan type load torque, propeller type load torque and undefined load torque. Experimental results are obtained with PMDC motor to validate the proposed control method for various speeds with different constant load torque conditions. Comparisons are carried out both in simulation and real time for PI controller, SMC, CSS and PIDSS based on the speed settling time and steady state error. Satisfactory results are obtained and presented in this paper.
TL;DR: A feature selection technique that selects a minimal number of well-playing features based on the features dynamically and a model Supervised Learning Approach to unfold Student’s Academic Future Progression through Super supervised learning approach (SLASAFP) algorithm that recommends the best fitting machine learning algorithm based onThe features dynamically.
Abstract: Graduate students are unaware of their final qualification for a course. Even though there were many models available, few works with feature selection and prediction with no control over the number of features to be used. As a result of the lack of an improved performance forecasting system, students are only qualified on the second or third attempt. A warning system in place could help the students reduce their arrear count. All students undertaking higher education should obtain the qualification at their desired level of education without delay to transit to their careers on time. Therefore, there should be a predictive system for students to warn during the course work period and guide them to qualify in a first attempt itself. Although so many factors were present that affected the qualifying score, here proposed a feature selection technique that selects a minimal number of well-playing features. Also proposed a model Supervised Learning Approach to unfold Student’s Academic Future Progression through Supervised Learning Approach for Student’s Academic Future Progression (SLASAFP) algorithm that recommends the best fitting machine learning algorithm based on the features dynamically. It has proven with comparable predictive accuracy.
TL;DR: In this article , a single-phase hybrid multilevel inverter topology based on a switched capacitor is proposed, which is capable of generating 9-levels along with a voltage gain of 2.
Abstract: Switched capacitor based multilevel inverters with boosting capability are emerging as single stage DC–AC conversion in utilizing low voltage DC sources such as solar PV and fuel cell. This paper proposes a single-phase hybrid multilevel inverter topology based on a switched capacitor that is capable of generating 9-levels along with a voltage gain of 2. The components required to construct the basic module of topology are 11 switches, 1 diode and 2 capacitors. The voltage balancing of the switched capacitors is achieved with the help of a modulation strategy, thereby eliminating the need of sensors. The theoretical loss analysis of the inverter is presented and the nearest level control based fundamental switching frequency modulation technique is employed to study the performance of the proposed inverter. The effectiveness of the suggested topology is validated with the help of a prototype built in the laboratory. The superiority of the proposed topology is assessed with the help of comparison with existing topologies.
TL;DR: In this paper , the authors used the next generation simulation data set and back propagation neural network to train the vehicle lane change recognition model to recognize the lane change behaviour of the preceding vehicle.
Abstract: The traditional adaptive cruise system is responsible for delay in recognizing the cut-in/cut-out behaviour of front vehicle, and there is significant longitudinal acceleration of the vehicle fluctuation leading to reduced driver’s comfort level and even dangerous situation. In this paper, the next generation simulation data set and back propagation (BP) neural network are used to train the vehicle lane change recognition model to recognize the lane change behaviour of the preceding vehicle. The higher controller adopts variable weight linear quadratic optimal control to adjust the weight parameters according to the recognition results of front vehicle to reduce the fluctuation of vehicle acceleration. The lower layer adopts fuzzy proportional-integral-derivative (PID) control to follow the expected acceleration and builds the vehicle inverse dynamic model. Through CarSim/Simulink co-simulation, the results show that, under the cut-in or cut-out and working conditions, the behaviour of the leading vehicle can be recognized, following target can be switched in advance, weight parameters can be adjusted and the large fluctuation of longitudinal acceleration can be reduced.
TL;DR: In this paper , the giant magnetoresistance broken rotor (GBR) method is used to diagnose the induction motor (IM) rotor bar fault at an early stage from outward magnetic flux developed by IM.
Abstract: In this paper, the giant magnetoresistance broken rotor (GBR) method is used to diagnose the induction motor (IM) rotor bar fault at an early stage from outward magnetic flux developed by IM.The outward magnetic field signal has anti-clockwise radiation due to broken rotor bar current.In this paper, the outward magnetic signal is acquired using a giant magnetoresistance (GMR) sensor. In the GBR method, IM rotor fault is analysed with a non-decimated wavelet transform (NDWT)-based outward magnetic signal. Experimental result shows the difference in statistical features and energy levels of sub-bands of NDWT for healthy and faulty IM. Least square-support vector machine(LS-SVM)-based classification results are verified by confusion matrix based on 150 outward magnetic signals from a healthy and damaged rotor (broken rotor). The proposed method identifies IM rotor faults with 95% sensitivity, 90% specificity and 92.5% classification accuracy. Furthermore, run-time IM condition monitoring is performed through the ThinkSpeak internet of things (IoT) platform for collecting outer magnetic signal data. ThinkSpeak streaming data of outward magnetic field help detect rotor fault at the initial stage and understand the growth of rotor fault in the motor. The proposed GBR method overcomes sensitivity, translation-invariance limitations of existing IM rotor fault diagnosis methods.
TL;DR: In this article , a robust adaptive backstepping (RABS) control strategy for a pediatric exoskeleton system during passive-assist gait rehabilitation is proposed, where the robustness of the proposed control is validated by varying the limb segment masses and inducing the periodic external disturbances.
Abstract: ABSTRACT The main purpose of this work is to design a robust adaptive backstepping (RABS) control strategy for a pediatric exoskeleton system during passive-assist gait rehabilitation. The nonlinear dynamics of the exoskeleton system have ill-effects of uncertain parameters and external interferences. In this work, the designed robust control strategy is applied on the exoskeleton to assist children of 08–12 years, 25–40 kg weight, and 115–125 cm height. The dynamic model of the coupled human-exoskeleton system is established using the Euler–Lagrange principle. An appropriate Lyapunov function is selected to prove the uniform boundedness of the control signals. The “explosion of terms” is avoided by establishing a virtual control law without the dynamical system parameters. A Microsoft Kinect-LabVIEW experiment is carried out to estimate the desired gait trajectory. The robustness of the proposed control is validated by varying the limb segment masses and inducing the periodic external disturbances. The proposed control strategy is compared with the decentralized modified simple adaptive-PD (DMSA-PD) control strategy. From simulation results and performance improvement index, it is observed that RABS control outperforms the contrast control (DMSA-PD) to track the desired gait during passive-assist rehabilitation under the effect of model uncertainties and external disturbances.
TL;DR: In this article , a robust adaptive fuzzy control model was proposed to reduce the effect of uncertainty problems and disturbances on the dynamic positioning system (DPS) of vessels in the path planning control process.
Abstract: The thrusters and propulsion propellers systems, as well as the operating situations, are all well-known nonlinearities which are caused less accuracy of the dynamic positioning system (DPS) of vessels in the path planning control process. In this study, to enhance the robust performance of the DPS, we proposed a robust adaptive fuzzy control model to reduce the effect of uncertainty problems and disturbances on the DPS. Firstly, the adaptive fuzzy controller with adaptive law is designed to adjust the membership function of the fuzzy controller to minimize the error in path planning control of the vessel. Secondly, the H∞ performance of robust tracking is proved by the Lyapunov theory. Moreover, compared to the other controller, a simulation experiment comprising two case studies confirmed the efficiency of the approach. Finally, the results showed that the proposed controller reaches control quality, performance and stability.
TL;DR: In this article , a selective level mapping (SLM) based on Chaotic Biogeography Based Optimization (CBBO) algorithm is proposed to offer an efficient solution to the problem of high PAPR, existing in the GFDM waveforms.
Abstract: ABSTRACT High data rates, extremely low power consumption, and minimal end-to-end latency are considered to be mandatory requirements for 5G wireless networks. Rapid improvements in design and performance of 5G physical layer waveforms have become necessary. The drawback of Orthogonal Frequency Division Multiplexing (OFDM) is high PAPR, that causes signal distortion, which reduces system efficiency. Generalized frequency division multiplexing (GFDM) is a promising non-orthogonal multicarrier transmission scheme, which has recently received a great deal of attention towards future fifth generation (5G) wireless networks. It overcomes the limitations of orthogonal frequency division multiplexing (OFDM), while preserving most of the advantages of it. Selective Level mapping (SLM) is one of the PAPR reduction techniques, that uses the phase shift technology. In this paper, SLM based on Chaotic Biogeography Based Optimization (CBBO) algorithm is proposed to offer an efficient solution to the problem of high PAPR, existing in the GFDM waveforms. Experimental results prove that, the proposed CBBO–SLM technique provides significant improvement in terms of PAPR reduction, as compared to the conventional SLM methods, such as conventional GFDM and OFDM-SLM. The proposed novel scheme is most suitable for QAM and QPSK applications, as it provides good PAPR reduction performance, at lower computational complexity.
TL;DR: A novel method for PID (proportional–integral–derivative) controller auto-tuning based on expert knowledge incorporated into a fuzzy logic inference system that achieves a balance between the aggressive and robust closed-loop response.
Abstract: This paper presents a novel method for PID (proportional–integral–derivative) controller auto-tuning based on expert knowledge incorporated into a fuzzy logic inference system. The proposed scheme iteratively tries to improve the performance of the closed-loop system. As performance measures, the proposed scheme uses the characteristics of the step response (rise time, overshoot, and settling time). PID parameters in the first iteration can be calculated based on the basic open-loop step response experiment or it is possible to use current parameters. In each successive iteration, step response characteristics are measured and the relative changes expressed in the percentage of value in the first iteration are calculated and converted into linguistic values. The fuzzy expert system computes fuzzy values that are used after defuzzification as multiplying factors for current PID parameters. To achieve a balance between the aggressive and robust closed-loop response, as well as between the slower and the faster one, the fuzzy expert system works in three operating modes: the one for speeding up the system, the one for reducing the overshoot, and the one for a balanced reduction of rise time and overshoot. The performance and robustness are verified by computer simulation using an extensive range of different processes.
TL;DR: In this paper , the average rotor slot size variation (ASSV) in the rotor is predicted during the running condition of the motor through logistic regression, and the accuracy of ASSV prediction is about 92%.
Abstract: Rotor slots in induction motor expand due to thermal imbalance and create magnetic stress. Magnetic stress is a force that develops on the laminated surface of the rotor due to the curving or stretching magnetic flux. Traditional motor fault detection methods never measure magnetic stress on the rotor; a significant problem frequently arises in the motor. Magnetic stress is proportional to slot size variations in the rotor. High slot size variations on the laminated surface of the rotor lead to more curving and stretching magnetic flux and damage the rotor and stator, reducing their efficiency and induce harmonics. In this paper, the Average rotor Slot Size Variation (ASSV) in the rotor is predicted during the running condition of the motor through logistic regression. Logistic regression predicts ASSV by multimodal sensor signal sub-band energy values and measures rotor slot sizes from microscope images. Multimodal sensor signal is obtained from various sensors, such as vibration, temperature, current and Giant Magneto Resistance (GMR). Signal sub-band energy is obtained from Over complete Rational-Dilation Wavelet Transform (ORaDWT). From experimental results, ASSV is more than 75% from standard size, damaging the rotor and stator. The accuracy of ASSV prediction is about 92%.
TL;DR: The unified framework for recursive computationally efficient convergence accelerators and error models for a number of combinations of Richardson and Newton–Schulz iterations is developed and a new nonrecursive parameter estimation concept is introduced and compared in this paper with recursive estimation.
Abstract: Sufficiently accurate, fast and computationally efficient solution of the system of linear equations is required in many estimation problems. Richardson iteration is one of the main solvers for linear equations, which provides optimization possibilities for time critical and accuracy critical applications. Convergence rate improvement and reduction of the computational complexity of the Richardson iteration are the most important problems in the area. The introduction of Newton–Schulz iterations is the efficient way for convergence rate improvement and the paper starts with systematic overview of the high-order Newton–Schulz matrix inversion algorithms. In addition, the unified framework for recursive computationally efficient convergence accelerators and error models for a number of combinations of Richardson and Newton–Schulz iterations is developed. A new nonrecursive parameter estimation concept is introduced and compared in this paper with recursive estimation. Recursive and nonrecursive Richardson algorithms together with the standard LU decomposition method were applied to the electric grid power quality monitoring problem. The algorithms were tested for the detection of the sag and swell signatures in the voltage and current signals on real data in three-phase power system. Nonrecursive Richardson algorithms which save close to half of the computational time compared to LU decomposition method were recommended for power quality monitoring applications.
TL;DR: In this article, a new control method for a bidirectional DC/DC converter equipped with a superconducting magnetic energy storage system (SMES) is presented to exchange the stored energy with the proton-exchange membrane fuel cell (PEMFC) in a high response dynamic to overcome the slow dynamic of the fuel cell feeding load.
Abstract: Nowadays, the use of renewable energy systems, especially fuel cells, has become very widespread in various applications. Some of their most important applications include using electric vehicles and local off-grid power systems. One of the challenges with the fuel cells is the slow dynamic response to load power changes. In fact, when the load power changes suddenly, the fuel cell supplies it with a time delay, and this can cause disturbances in the performance of the loads. In this research, a new control method for a bidirectional DC/DC converter equipped with a superconducting magnetic energy storage system (SMES) is presented to exchange the stored energy with the proton-exchange membrane fuel cell (PEMFC) in a high response dynamic to overcome the slow dynamic of the fuel cell feeding load. The appropriate validation of the proposed system is verified by simulation in the MATLAB SIMULINK environment. The results illustrate that the slow-response of PEMFC is improved appropriately.
TL;DR: In this paper , a grid-connected hybrid system using modified Z source converter, bidirectional converter and battery storage system is presented, where the input sources for the proposed system are fed from solar and wind power systems.
Abstract: This paper presents the design of a grid-connected hybrid system using modified Z source converter, bidirectional converter and battery storage system. The input sources for the proposed system are fed from solar andwind power systems. Amodified high gain switched Z source converter is designed for supplying constant DC power to the DC-link of the inverter. A hybrid deep learning (HDL) algorithm (CNN-BiLSTM) is proposed for predicting the output power from the hybrid systems. The HDL method and the PI controller generate pulses to the proposed system. A closed loop control framework is implemented for the proposedgrid integratedhybrid system. A 1.5 Kw hybrid system is designed in MATLAB/SIMULINK software and the results are validated. A prototype of the proposed system is developed in the laboratory and experimental results are obtained from it. From the simulation and experimental results, it is observed that the ANN controller with SVPWM (Space vector Pulse width Modulation) gives a THD (Total harmonic distortion) of 2.2% which is within the IEEE 519 standard. Therefore, from the results, it is identified that the ANN-SVPWM method injects less harmonic currents into the grid than the other two controllers. ARTICLE HISTORY Received 14 November 2021 Accepted 23 August 2022
TL;DR: A simpler NN architecture was employed in the research, and better accuracy rates were achieved with fewer samples, and simultaneous localization and mapping (SLAM) applications can be performed in non-GPS environments using pre-recorded images containing GPS information.
Abstract: In this study, moving object recognition is performed by using images from a camera mounted on an unmanned ground vehicle. A GPS coordinate-based algorithm has been developed to obtain moving object silhouettes. In order to classify these silhouettes, an interconnected artificial neural network (ICANN) architecture consisting of two stages has been developed. The method consists of two phases. In the first phase, real-time images are converted to binary images at the end of the GPS-assisted image registration process. Then, the silhouettes are extracted from the background of the images using connected component labelling. In the second phase, two interconnected neural networks are used. The first neural network classifies silhouettes as objects or noise. The second neural network divides objects into seven subclasses as pedestrians, potholes, cars, etc. Compared to CNN-based techniques, a simpler NN architecture was employed in the research, and better accuracy rates were achieved with fewer samples. Another contribution of the research is simultaneous localization and mapping (SLAM) applications can be performed in non-GPS environments using pre-recorded images containing GPS information. In experimental studies, maximum success rates of 96,1% in object classification were obtained. The results obtained were compared to YOLO, the recently popular algorithm for object recognition.
TL;DR: The proposed research aims to restore deteriorated text sections that are affected by stain markings, ink seepages and document ageing in ancient document photographs, as these challenges confront document enhancement with a tri-level semi-adaptive thresholding technique.
Abstract: The proposed research aims to restore deteriorated text sections that are affected by stain markings, ink seepages and document ageing in ancient document photographs, as these challenges confront document enhancement. A tri-level semi-adaptive thresholding technique is developed in this paper to overcome the issues. The primary focus, however, is on removing deteriorations that obscure text sections. The proposed algorithm includes three levels of degradation removal as well as pre- and post-enhancement processes. In level-wise degradation removal, a global thresholding approach is used, whereas, pseudo-colouring uses local thresholding procedures. Experiments on palm leaf and DIBCO document photos reveal a decent performance in removing ink/oil stains whilst retaining obscured text sections. In DIBCO and palm leaf datasets, our system also showed its efficacy in removing common deteriorations such as uneven illumination, show throughs, discolouration and writing marks. The proposed technique directly correlates to other thresholding-based benchmark techniques producing average F-measure and precision of 65.73 and 93% towards DIBCO datasets and 55.24 and 94% towards palm leaf datasets. Subjective analysis shows the robustness of proposed model towards the removal of stains degradations with a qualitative score of 3 towards 45% of samples indicating degradation removal with fairly readable text. Highlights This work presents a semi-adaptive binarization technique for ancient image enhancement. Main focus of this work is to restore obscured text sections. Multi-level thresholding approach is used for the removal of degradations. Gradient of the original image is used in the computation of reference image to detect deteriorated text sections. Pseudo-colouring and post-enhancement process finally transform to the enhanced image. DIBCO and palm leaf document samples are used for experimentations.