scholarly journals Hedge-Algebra-Based Phase-Locked Loop for Distorted Utility Conditions

2019 ◽  
Vol 2019 ◽  
pp. 1-17 ◽  
Author(s):  
Dinko Vukadinović ◽  
Tien Duy Nguyen ◽  
Cat Ho Nguyen ◽  
Nhu Lan Vu ◽  
Mateo Bašić ◽  
...  
Keyword(s):  
Fuzzy Controller ◽  
Optimal Parameter ◽  
Moving Average ◽  
Absolute Error ◽  
Phase Locked Loop ◽  
Feedback Signal ◽  
Rule Base ◽  
Electric Grid ◽  
Output Stage ◽  
Linguistic Rule

This paper presents the first application of the hedge-algebra theory in the field of grid synchronization. For this purpose, an optimized hedge-algebra controller (HAC) is developed and incorporated within the three-phase phase-locked loop (PLL) with moving average filters (MAFs) inside its feedback loop. Optimized fuzziness parameters and linguistic rule base of the HAC are obtained by a genetic algorithm using the integral of absolute error as the performance index during optimization. Calculated optimal parameter values of the HAC depend on the most frequently occurring disturbance in the electric grid. Two different PLL structures are proposed, depending on the types of disturbances occurring in the electric grid. The first structure is the conventional synchronous reference frame PLL with the nonadaptive MAF (i.e., MAF without order adjustment), but with the PI/PID controller in the phase loop replaced by the developed HAC. Such PLL structure is suitable for all analyzed disturbance types, expect for step-changes in the grid frequency. The second PLL structure introduces the adaptive MAF (i.e., MAF with order adjustment) and a new feedback signal in the output stage of the controller to achieve zero steady-state error in the case of step-changes in the grid frequency. The disturbance rejection capability of the two developed PLLs with the HAC (HAC-PLLs) is tested separately and compared experimentally with the PID- and fuzzy-controller-based PLLs.

scholarly journals Design and Lyapunov Stability Analysis of a Fuzzy Logic Controller for Autonomous Road Following

2010 ◽  
Vol 2010 ◽  
pp. 1-20 ◽  
Author(s):  
Yi Fu ◽  
Howard Li ◽  
Mary Kaye
Keyword(s):  
Fuzzy Logic ◽  
Stability Analysis ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Intelligent Vehicles ◽  
Intelligent Vehicle ◽  
Rule Base ◽  
Control Laws ◽  
Robotic Vehicle ◽  
Road Following

Autonomous road following is one of the major goals in intelligent vehicle applications. The development of an autonomous road following embedded system for intelligent vehicles is the focus of this paper. A fuzzy logic controller (FLC) is designed for vision-based autonomous road following. The stability analysis of this control system is addressed. Lyapunov's direct method is utilized to formulate a class of control laws that guarantee the convergence of the steering error. Certain requirements for the control laws are presented for designers to choose a suitable rule base for the fuzzy controller in order to make the system stable. Stability of the proposed fuzzy controller is guaranteed theoretically and also demonstrated by simulation studies and experiments. Simulations using the model of the four degree of freedom nonholonomic robotic vehicle are conducted to investigate the performance of the fuzzy controller. The proposed fuzzy controller can achieve the desired steering angle and make the robotic vehicle follow the road successfully. Experiments show that the developed intelligent vehicle is able to follow a mocked road autonomously.

scholarly journals Day-Ahead Forecast of Electric Vehicle Charging Demand with Deep Neural Networks

2021 ◽  
Vol 12 (4) ◽  
pp. 178
Author(s):  
Gilles Van Van Kriekinge ◽  
Cedric De De Cauwer ◽  
Nikolaos Sapountzoglou ◽  
Thierry Coosemans ◽  
Maarten Messagie
Keyword(s):  
Neural Network ◽  
Electric Vehicles ◽  
Negative Impact ◽  
Absolute Error ◽  
Electric Grid ◽  
Electric Vehicle Charging ◽  
The Neural Network ◽  
The Mean ◽  
Ev Charging ◽  
Charging Demand

The increasing penetration rate of electric vehicles, associated with a growing charging demand, could induce a negative impact on the electric grid, such as higher peak power demand. To support the electric grid, and to anticipate those peaks, a growing interest exists for forecasting the day-ahead charging demand of electric vehicles. This paper proposes the enhancement of a state-of-the-art deep neural network to forecast the day-ahead charging demand of electric vehicles with a time resolution of 15 min. In particular, new features have been added on the neural network in order to improve the forecasting. The forecaster is applied on an important use case of a local charging site of a hospital. The results show that the mean-absolute error (MAE) and root-mean-square error (RMSE) are respectively reduced by 28.8% and 19.22% thanks to the use of calendar and weather features. The main achievement of this research is the possibility to forecast a high stochastic aggregated EV charging demand on a day-ahead horizon with a MAE lower than 1 kW.

scholarly journals Fuzzy rule: Based model reference adaptive control for PMSM drives

2007 ◽  
Vol 4 (1) ◽  
pp. 13-22 ◽  
Author(s):  
Mohamed Kadjoudj ◽  
Noureddine Golea ◽  
Hachemi Benbouzid
Keyword(s):  
Knowledge Base ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Reference Model ◽  
Adaptive Fuzzy Control ◽  
Rule Base ◽  
Motor Speed ◽  
Model Reference ◽  
Adaptive Fuzzy ◽  
Model Reference Adaptive

The objective of the model reference adaptive fuzzy control (MRAFC) is to change the rules definition in the direct fuzzy logic controller (FLC) and rule base table according to the comparison between the reference model output signal and system output. The MRAFC is composed by the fuzzy inverse model and a knowledge base modifier. Because of its improved algorithm, the MRAFC has fast learning features and good tracking characteristics even under severe variations of system parameters. The learning mechanism observes the plant outputs and adjusts the rules in a direct fuzzy controller, so that the overall system behaves like a reference model, which characterizes the desired behavior. In the proposed scheme, the error and error change measured between the motor speed and output of the reference model are applied to the MRAFC. The latter will force the system to behave like the signal reference by modifying the knowledge base of the FLC or by adding an adaptation signal to the fuzzy controller output. In this paper, the MRAFC is applied to a permanent magnet synchronous motor drive (PMSM). High performances and robustness have been achieved by using the MRAFC. This will be illustrated by simulation results and comparisons with other controllers such as PI classical and adaptive fuzzy controller based on gradient method controllers.

Provide A Method Based on Genetic Algorithm to Optimize the Fuzzy Logic Controller for the Inverted Pendulum

2021 ◽  
Author(s):  
Shahrooz Alimoradpour ◽  
Mahnaz Rafie ◽  
Bahareh Ahmadzadeh
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy System ◽  
Inverted Pendulum ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Fuzzy Rule ◽  
Rule Base ◽  
Base System ◽  
Control Rules ◽  
Dynamics And Control

Abstract One of the classic systems in dynamics and control is the inverted pendulum, which is known as one of the topics in control engineering due to its properties such as nonlinearity and inherent instability. Different approaches are available to facilitate and automate the design of fuzzy control rules and their associated membership functions. Recently, different approaches have been developed to find the optimal fuzzy rule base system using genetic algorithm. The purpose of the proposed method is to set fuzzy rules and their membership function and the length of the learning process based on the use of a genetic algorithm. The results of the proposed method show that applying the integration of a genetic algorithm along with Mamdani fuzzy system can provide a suitable fuzzy controller to solve the problem of inverse pendulum control. The proposed method shows higher equilibrium speed and equilibrium quality compared to static fuzzy controllers without optimization. Using a fuzzy system in a dynamic inverted pendulum environment has better results compared to definite systems, and in addition, the optimization of the control parameters increases the quality of this model even beyond the simple case.

scholarly journals Seawater intrusion vulnerability evaluation and prediction: a case study of Qeshm Island, Iran

2020 ◽  
Author(s):  
Amin Zeynolabedin ◽  
Reza Ghiassi ◽  
Moharram Dolatshahi Pirooz
Keyword(s):  
Seawater Intrusion ◽  
Moving Average ◽  
Arima Model ◽  
Chloride Ion ◽  
Absolute Error ◽  
Ion Concentration ◽  
Qeshm Island ◽  
Autoregressive Integrated Moving Average ◽  
Dangerous Condition

Abstract Seawater intrusion is one of the most serious issues to threaten coastal aquifers. Tourian aquifer, which is selected as the case study, is located in Qeshm Island, Persian Gulf. In this study, first the vulnerability of the region to seawater intrusion is assessed using chloride ion concentration value, then by using the autoregressive integrated moving average (ARIMA) model, the vulnerability of the region is predicted for 14 wells in 2018. The results show that the Tourian aquifer experiences moderate vulnerability and the area affected by seawater intrusion is wide and is in danger of expanding. It is also found that 0.95 km2 of the region is in a state of high vulnerability with Cl concentration being in a dangerous condition. The prediction model shows that ARIMA (2,1,1) is the best model with mean absolute error of 13.3 mg/L and Nash–Sutcliffe value of 0.81. For fitted and predicted data, mean square error is evaluated as 235.3 and 264.3, respectively. The prediction results show that vulnerability is increasing through the years.

Navigational Analysis of Multiple Humanoids Using a Hybridized Rule Base-Sugeno Fuzzy Controller

2020 ◽  
Vol 17 (04) ◽  
pp. 2050017
Author(s):  
Manoj Kumar Muni ◽  
Dayal R. Parhi ◽  
Priyadarshi Biplab Kumar ◽  
Asita Kumar Rath
Keyword(s):  
Performance Enhancement ◽  
Fuzzy Controller ◽  
Fuzzy Model ◽  
Humanoid Robots ◽  
Rule Base ◽  
Turning Angle ◽  
Cluttered Environments ◽  
Hybrid Controller ◽  
Final Output ◽  
Set Up

This paper describes a rule base-Sugeno fuzzy hybrid controller for path planning of single as well as multiple humanoid robots in cluttered environments. Initially, sensor outputs regarding the obstacle distances are used as inputs to the rule base model, and turning angle is obtained as the output. The rule-based analysis is used for training the fuzzy controller with membership functions. The output from the rule base model along with other regular inputs is supplied to a Sugeno fuzzy model, and effective turning angle is obtained as the final output to avoid the obstacles present in the environment and navigate the humanoids safely to their target points. The proposed hybrid controller is tested on a V-REP simulation platform, and the simulation results are validated in an experimental set-up. To avoid the possibility of any inter-collision during navigation of multiple humanoids on a common platform, a Petri-net scheme is integrated along with the proposed hybrid model. Finally, the results obtained from simulation and experimental platforms are compared against each other with proper agreement and minimal percentage of deviations. To validate the proposed controller, it has also been tested against another existing navigational approach, and satisfactory performance enhancement has been observed.

A phrenic nerve-actuated electronically controlled positive-pressure ventilator

1987 ◽  
Vol 62 (5) ◽  
pp. 2121-2125 ◽  
Author(s):  
E. R. Schertel ◽  
D. A. Schneider ◽  
D. L. Howard ◽  
J. F. Green
Keyword(s):  
Moving Average ◽  
Inspiratory Flow ◽  
Positive Pressure ◽  
Feedback Signal ◽  
Position Signal ◽  
Tracheal Pressure ◽  
Valve Position ◽  
Air Valve ◽  
The Difference ◽  
Operational Modes

We have constructed an electronically controlled positive-pressure ventilator actuated by phrenic neural activity for use in open-chested or paralyzed experimental animals for the study of breathing pattern. A Bird Mark 14 positive-pressure ventilator was modified such that flow is a linear function of a command signal. Flow is delivered by advancing an air valve with a servo-motor that is controlled by one of three different operational modes. In two of the modes, the difference between the electronic average of inspiratory phrenic activity (moving average) and a feedback signal determines the inspiratory flow. The feedback signal is derived from either tracheal pressure or an electronic measure of inspired volume. In the third mode, the moving average is differentiated to provide control of inspiratory flow and volume. Physiological flow profiles were created using all three operational modes. Integration of an air-valve position signal provides an electronic measure of tidal volume. An additional feature of this ventilator allows inspiratory flow and duration to be predetermined for a given breath.

BACKPROPAGATION THROUGH TIME TRAINING OF A NEURO-FUZZY CONTROLLER

2010 ◽  
Vol 20 (05) ◽  
pp. 421-428 ◽  
Author(s):  
PETIA KOPRINKOVA-HRISTOVA
Keyword(s):  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Fuzzy Rule ◽  
Linguistic Meaning ◽  
Rule Base ◽  
Linguistic Variables ◽  
Backpropagation Through Time ◽  
Linear Plant ◽  
Neuro Fuzzy ◽  
Neural Network Structure

The paper considers gradient training of fuzzy logic controller (FLC) presented in the form of neural network structure. The proposed neuro-fuzzy structure allows keeping linguistic meaning of fuzzy rule base. Its main adjustable parameters are shape determining parameters of the linguistic variables fuzzy values as well as that of the used as intersection operator parameterized T-norm. The backpropagation through time method was applied to train neuro-FLC for a highly non-linear plant (a biotechnological process). The obtained results are discussed with respect to adjustable parameters rationality. Conclusions are made with respect to the appropriate intersection operations too.

scholarly journals Leveraging Hybrid Filter for Improving Quasi-Type-1 Phase Locked Loop Targeting Fast Transient Response

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2472 ◽  
Author(s):  
Yunlu Li ◽  
Junyou Yang ◽  
Haixin Wang ◽  
Weichun Ge ◽  
Yiming Ma
Keyword(s):  
Time Delay ◽  
Transient Response ◽  
Moving Average ◽  
Phase Locked Loop ◽  
Small Time ◽  
Dc Offset ◽  
Fast Transient Response ◽  
Grid Codes ◽  
Adaptive Notch Filters

In renewable energy generation applications, phase locked loop (PLL) is one of the most popular grid synchronization technique. The main objective of PLL is to rapidly and precisely extract phase and frequency especially when the grid voltage is under non-ideal conditions. This motivates the recent development of moving average filters (MAFs) based PLL in a quasi-type-1 system (i.e., QT1-PLL). Despite its success in certain applications, the transient response is still unsatisfactory, mainly due to the fact that the time delay caused by MAFs is still large. This has significantly limited the utilization of QT1-PLL, according to common grid codes such as German and Spanish grid codes. This challenge has been tackled in this paper. The basic idea is to develop a new hybrid filtering stage, consisting of adaptive notch filters (ANFs) and MAFs, arranged at the inner loop of QT1-PLL. Such an idea can greatly improve the transient response of QT1-PLL, owing to the fact that ANFs are utilized to remove the fundamental frequency negative voltage sequence (FFNS) component while other dominant harmonics can be removed by MAFs with a small time delay. By applying the proposed technique, the settling time is reduced to less than one cycle of grid frequency without any degradation in filtering capability. Moreover, the proposed PLL can be easily expanded to handle dc offset rejection. The effectiveness is validated by comprehensive experiments.

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