Journal articles: 'ANN CONTROLLER'

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Banda, Gururaj, and Sri Gowri Kolli. "An Intelligent Adaptive Neural Network Controller for a Direct Torque Controlled eCAR Propulsion System." World Electric Vehicle Journal 12, no. 1 (March 17, 2021): 44. http://dx.doi.org/10.3390/wevj12010044.

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This article deals with an intelligent adaptive neural network (ANN) controller for a direct torque controlled (DTC) electric vehicle (EV) propulsion system. With the realization of artificial intelligence (AI) conferred adaptive controllers, the torque control of an electric car (eCAR) propulsion motor can be achieved by estimating the stator reference flux voltage used to synthesize the space vector pulse width modulation (SVPWM) for a DTC scheme. The proposed ANN tool optimizes the parameters of a proportional integral (PI) controller with real-time data and offers splendid dynamic stability. The response of an ANN controller is examined over standard drive cycles to validate the performance of an eCAR in terms of drive range and energy efficiency using MATLAB simulation software.

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Alatshan, Mohammed Salheen, Ibrahim Alhamrouni, Tole Sutikno, and Awang Jusoh. "Improvement of the performance of STATCOM in terms of voltage profile using ANN controller." International Journal of Power Electronics and Drive Systems (IJPEDS) 11, no. 4 (December 1, 2020): 1966. http://dx.doi.org/10.11591/ijpeds.v11.i4.pp1966-1978.

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The electronic equipments are extremely sensitive to variation in electric supply. The increasing of a nonlinear system with several interconnected unpredicted and non-linear loads are causing some problems to the power system. The major problem facing the power system is power quality, controlling of reactive power and voltage drop. A static synchronous compensator (STATCOM) is an important device commonly used for compensation purposes, it can provide reactive support to a bus to compensate voltage level. In this paper, the Artificial Neural Network (ANN) controlled STATCOM has been designed to replace the conventional PI controller to enhance the STATCOM performance. The ANN controller is proposed due to its simple structure, adaptability, robustness, considering the power grid non linearities. The ANN is trained offline using data from the PI controller. The performance of STATCOM with case of Load increasing and three-phase faults case was analyzed using MATLAB/Simulink software on the IEEE 14-bus system. The comprehensive result of the PI and ANN controllers has demonstrated the effectiveness of the proposed ANN controller in enhancing the STATCOM performance for Voltage profile at different operating conditions. Furthermore, it has produced better results than the conventional PI controller.

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Chen, Wei Lun, and Gong Cai Xin. "Research on ANN Dynamic Inversion Control of UAV." Advanced Materials Research 466-467 (February 2012): 1353–57. http://dx.doi.org/10.4028/www.scientific.net/amr.466-467.1353.

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The paper proposes a method to design AANN dynamic inversion controller through online ANN compensating inversion error. It mainly aims at evident shortage of dynamic inversion controller of UAV. A single hidden layer ANN structure is constructed and the stability of the whole closed loop system is proved. Also the stable adjustment arithmetic of online ANN weight is proposed. The robustness, the adaptability to fault and the response capability to actuator delay time of the scheme are verified by simulation. It is also proved that the online ANN has improved the performance of dynamic inversion controller well. It has important reference value for designing advanced flight control systems of UAV.

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Lee, Heung-Jae, Seong-Su Jhang, Won-Kun Yu, and Jung-Hyun Oh. "Artificial Neural Network Control of Battery Energy Storage System to Damp-Out Inter-Area Oscillations in Power Systems." Energies 12, no. 17 (September 2, 2019): 3372. http://dx.doi.org/10.3390/en12173372.

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This paper proposed an ANN (Artificial Neural Network) controller to damp out inter-area oscillation of a power system using BESS (Battery Energy Storage System). The conventional lead-lag controller-based PSSs (Power System Stabilizer) have been designed using linear models usually linearized at heavy load conditions. This paper proposes a non-linear ANN based BESS controller as the ANN can emulate nonlinear dynamics. To prove the performance of this nonlinear PSS, two linear PSS are introduced at first which are linearized at the heavy load and light load conditions, respectively. It is then verified that each controller can damp out inter-area oscillations at its own condition but not satisfactorily at the other condition. Finally, an ANN controller, that learned the dynamics of these two controllers, is proposed. Case studies are performed using PSCAD/EMTDC and MATLAB. As a result, the proposed ANN PSS shows a promising robust nonlinear performance.

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Mugheri, N. H., M. U. Keerio, S. Chandio, and R. H. Memon. "Robust Speed Control of a Three Phase Induction Motor Using Support Vector Regression." Engineering, Technology & Applied Science Research 11, no. 6 (December 11, 2021): 7861–66. http://dx.doi.org/10.48084/etasr.4476.

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The Three Phase Induction Motor (TIM) is one of the most widely used motors due to its low price, robustness, low maintenance cost, and high efficiency. In this paper, a Support Vector Regression (SVR) based controller for TIM speed control using Indirect Vector Control (IVC) is presented. The IVC method is more frequently used because it enables better speed control of the TIM with higher dynamic performance. Artificial Neural Network (ANN) controllers have been widely used for TIM speed control for several reasons such as their ability to successfully train without prior knowledge of the mathematical model, their learning ability, and their fast implementation speed. The SVR-based controller overcomes the drawbacks of the ANN-based controller, i.e. its low accuracy, overfitting, and poor generalization ability. The speed response under the proposed controller is faster in terms of rising and settling time. The dynamic speed response of the proposed controller is also superior to that of the ANN-PI controller. The performance of the proposed controller was compared for TIM speed control with an ANN-PI controller via simulations in SIMULINK.

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Albert Alexander, S., R. Harish, M. Srinivasan, and D. Sarathkumar. "Power Quality Improvement in a Solar PV Assisted Microgrid Using Upgraded ANN-Based Controller." Mathematical Problems in Engineering 2022 (October 7, 2022): 1–12. http://dx.doi.org/10.1155/2022/2441534.

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This paper proposes the design of a controller using the artificial neural network (ANN) for a solar photovoltaic (PV)-fed cascaded multilevel inverter (CMLI) to enhance the power quality. The objective of this presented ANN controller is to obtain a maximum output voltage with no filter components. This paper also investigates and eliminates the voltage harmonics that occurred in a solar-fed cascaded 3-stage inverter using various techniques such as pulse width modulation (PWM), digital logic control (DLC), fuzzy logic controller (FLC), and ANN, and the results are compared. Based on the results, the proposed ANN-based controller efficiently reduces harmonics and improves the power quality. This is achieved by solving the harmonic equations and thereby changing the switching angles of each semiconductor to a minimum value. The ANN is trained by a dataset consisting of varying input voltage and switching angles. The simulation was performed using MATLAB/Simulink for different types of controllers like PWM, DLC, FLC, and ANN for the 3-stage inverter. The simulated results are compared with the results obtained from a 3 kWp photovoltaic plant connected to the CMLI. Finally, on the basis of performance analysis, it was confirmed that the ANN-based controller effectively eliminates harmonics and improves the power quality.

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Liu, Bao, Na Gao, Fei Liu, Ling Fan, and Yi Yong Sui. "An Improved ANN Controller on the Efficiency Optimization of Offshore Petroleum Platform." Applied Mechanics and Materials 571-572 (June 2014): 1042–46. http://dx.doi.org/10.4028/www.scientific.net/amm.571-572.1042.

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An improved ANN controller is presented inspired from hormone modulation function. This ANN controller consists of the main ANN controller and the conventional controller. To increase the learning efficiency, the slop of the excitation function is changed by the correcting parameters according to the hormone modulation law. To improve the control accuracy, we chose the accumulation of control error during the regulating process. And to avoid the integrated saturation, we judge the input of BP based on the absolute value of error. The main ANN controller adjusts the control input of the secondary conventional controller. To testify the effectiveness of the improved ANN controller, we apply it on the experiment device of offshore oil platform. The results show that the improved ANN controller has better control performance than the conventional controller and the normal ANN controller.

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Woodford, Grant W., and Mathys C. du Plessis. "Complex Morphology Neural Network Simulation in Evolutionary Robotics." Robotica 38, no. 5 (July 22, 2019): 886–902. http://dx.doi.org/10.1017/s0263574719001140.

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SUMMARYThis paper investigates artificial neural network (ANN)-based simulators as an alternative to physics-based approaches for evolving controllers in simulation for a complex snake-like robot. Prior research has been limited to robots or controllers that are relatively simple. Benchmarks are performed in order to identify effective simulator topologies. Additionally, various controller evolution strategies are proposed, investigated and compared. Using ANN-based simulators for controller fitness estimation during controller evolution is demonstrated to be a viable approach for the high-dimensional problem specified in this work.

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Jarupula, Somlal, Narsimha Rao Vutlapalli, and Narsimha Rao Vutlapalli. "Power Quality Improvement in Distribution System using ANN Based Shunt Active Power Filter." International Journal of Power Electronics and Drive Systems (IJPEDS) 5, no. 4 (April 1, 2015): 568. http://dx.doi.org/10.11591/ijpeds.v5.i4.pp568-575.

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This paper focuses on an Artificial Neural Network (ANN) controller based Shunt Active Power Filter (SAPF) for mitigating the harmonics of the distribution system. To increase the performance of the conventional controller and take advantage of smart controllers, a feed forward-type (trained by a back propagation algorithm) ANN-based technique is implemented in shunt active power filters for producing the controlled pulses required for IGBT inverter. The proposed approach mainly work on the principle of capacitor energy to maintain the DC link voltage of a shunt connected filter and thus reduces the transient response time when there is abrupt variation in the load. The entire power system block set model of the proposed scheme has been developed in MATLAB environment. Simulations are carried out by using MATLAB, it is noticed that the %THD is reduced to 2.27% from 29.71% by ANN controlled filter. The simulated experimental results also show that the novel control method is not only easy to be computed and implemented, but also very successful in reducing harmonics.

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Mahar, Hina, Hafiz Mudasir Munir, Jahangir Badar Soomro, Faheem Akhtar, Rashid Hussain, Mohamed F. Elnaggar, Salah Kamel, and Josep M. Guerrero. "Implementation of ANN Controller Based UPQC Integrated with Microgrid." Mathematics 10, no. 12 (June 9, 2022): 1989. http://dx.doi.org/10.3390/math10121989.

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This study discusses how to increase power quality by integrating a unified power quality conditioner (UPQC) with a grid-connected microgrid for clean and efficient power generation. An Artificial Neural Network (ANN) controller for a voltage source converter-based UPQC is proposed to minimize the system’s cost and complexity by eliminating mathematical operations such as a-b-c to d-q-0 translation and the need for costly controllers such as DSPs and FPGAs. In this study, nonlinear unbalanced loads and harmonic supply voltage are used to assess the performance of PV-battery-UPQC using an ANN-based controller. Problems with voltage, such as sag and swell, are also considered. This work uses an ANN control system trained with the Levenberg-Marquardt backpropagation technique to provide effective reference signals and maintain the required dc-link capacitor voltage. In MATLAB/Simulink software, simulations of PV-battery-UPQC employing SRF-based control and ANN-control approaches are performed. The findings revealed that the proposed approach performed better, as presented in this paper. Furthermore, the influence of synchronous reference frame (SRF) and ANN controller-based UPQC on supply currents and the dc-link capacitor voltage response is studied. To demonstrate the superiority of the suggested controller, a comparison of percent THD in load voltage and supply current utilizing SRF-based control and ANN control methods is shown.

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Bahamani, Akhib Khan, G. M. Sreerama Reddy, and V. Ganesh. "Power Quality Improvement in Fourteen Bus System using Non-Conventional Source Based ANN Controlled DPFC System." Indonesian Journal of Electrical Engineering and Computer Science 4, no. 3 (December 18, 2016): 499. http://dx.doi.org/10.11591/ijeecs.v4.i3.pp499-507.

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DPFC can be used to improve receiving end voltage of fourteen bus system. This paper shows the conception and simulation of wind and solar based distribution power flow controller for sag compensation and ohmic loss reduction. The objectives of this work are to improve the voltage and reduce the line losses. Fourteen bus systems with DPFC in open loop is simulated. Fourteen bus system with DPFC in closed loop using PI and ANN are also simulated and the results are presented. The comparative study is presented to demonstrate the improvement in dynamic response of ANN controlled DPFC system. ANN is observed to provide better control than has other controllers and improved damping characterises.

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Hernandez-Lopez, Ybrain, Raul Rivas-Perez, and Vicente Feliu-Batlle. "Design of a NARX-ANN-Based SP Controller for Control of an Irrigation Main Canal Pool." Applied Sciences 12, no. 18 (September 13, 2022): 9180. http://dx.doi.org/10.3390/app12189180.

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The management of irrigation main canals are studied in this research. One way of improving this is designing an efficient automatic control system of the water that flows through the canal pools, which is usually carried out by PI controllers. However, since canal pools are systems with large time delays and nonlinear hydrodynamics, these PIs are tuned in a very conservative way so that the closed-loop instability that may appear depending on the chosen operation regime is avoided. These controllers are inefficient because they have slow time responses. In order to obtain faster responses that remain stable independently of the operation regime, a control system that combines a Smith predictor, which is appropriate to control linear systems with large time delays, with a NARX artificial neural network (ANN), that models the nonlinear dynamics of the pools, is proposed. By applying system identification procedures, two nonlinear NARX-ANN-based models and a linear mathematical model of a real canal pool were obtained. These models were applied to implement a modified NARX-ANN-based SP controller and a conventional linear SP controller. Experimental results on our real canal pool showed that our modified NARX-ANN-based SP controller overcomes conventional linear SP controllers in both setpoint tracking and load disturbance rejection.

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Boudjellal, Bilal, and Tarak Benslimane. "Active and Reactive Powers Control of DFIG Based WECS Using PI Controller and Artificial Neural Network Based Controller." Modelling, Measurement and Control A 93, no. 1-4 (December 31, 2020): 31–38. http://dx.doi.org/10.18280/mmc_a.931-405.

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The purpose of this study is to improve the control performance of a Doubly Fed Induction Generator (DFIG) in a Wind Energy Conversion System (WECS) by using both of the conventional Proportional-Integral (PI) controllers and an Artificial Neural Network (ANN) based controllers. The rotor-side converter (RSC) voltages are controlled using a stator flux oriented control (FOC) to achieve an independent control of the active and reactive powers, exchanged between the stator of the DFIG and the power grid. Afterward, the PI controllers of the FOC are replaced with two ANN based controllers. A Maximum Power Point Tracking (MPPT) control strategy is necessary in order to extract the maximum power from the of wind energy system. A simulation model was carried out in MATLAB environment under different scenarios. The obtained results demonstrate the efficiency of the proposed ANN control strategy.

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A, Mr Aneerudh. "Design and Analysis of ANN Control based LLC Resonant Converter." International Journal for Research in Applied Science and Engineering Technology 11, no. 2 (February 28, 2023): 73–80. http://dx.doi.org/10.22214/ijraset.2023.48952.

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Abstract: In this, artificial neural network controller is designed for LLC resonant converter for voltage regulation. The performance of the proposed converter with proportional-integral (PI) controller and ANN controller are analysed from the simulation results. A voltage mode control is provided to get regulated load voltage irrespective of the changes in supply. ANN controller is used for the voltage mode control and the efficiency of the proposed ANN controller is estimated and comparison is made with conventional PI controller. The simulation work is done with MATLAB/Simulink software

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Ahmed, Shouket A., Abadal Salam T. Hussain, F. Malek, Taha A. Taha, S. Faiz Ahmed, Nursabrina Noorpi, Gomesh Nair Shasidharan, Mohd Irwan Yusoff, and Muhammad Irwanto Misrun. "Intelligent Controller of High Voltage Power Station Based Artificial Neural Network." Applied Mechanics and Materials 793 (September 2015): 505–9. http://dx.doi.org/10.4028/www.scientific.net/amm.793.505.

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In this papermulti-layer perceptron (MLP) artificial neural networks (ANN) theory is presented as an efficient controllerfor the high voltage direct current (HVDC) power station systems. The results demonstrated successful performance for single mode control using an MLP-ANN based on-line power controller. The main advantage by using ANN controllers such as optimal control system over a wide operating range, which is a capable of on-line adaptation makes the power systems no a prior knowledge and has a huge database with capacity to learn from previous experience.

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Sim, S. Y., C. K. Chia, W. M. Utomo, H. H. Goh, Y. M. Y. Buswig, A. J. M. S. Lim, S. L. Kek, A. A. Bohari, and C. L, Cham. "Enhance Cascaded H-Bridge Multilevel Inverter with Artificial Intelligence Control." Indonesian Journal of Electrical Engineering and Computer Science 11, no. 1 (July 1, 2018): 105. http://dx.doi.org/10.11591/ijeecs.v11.i1.pp105-112.

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This paper proposed a 7-level Cascaded H-Bridge Multilevel Inverter (CHBMI) with two diffenrent controller, ie, PID and Artificial Neural Network (ANN) controller to improve the output voltage performance and achieve a lower Total Harmonic Distortion (THD). A PWM generator is connected to the 7-level CHBMI to provide switching of the MOSFET. The reference signal waveform for the PWM generator is set to be sinusoidal to obtain an ideal AC output voltage waveform from the CHBMI. By tuning the PID controller as well as the self-learning abilities of the ANN controller, switching signals towards the CHBMI can be improved. Simulation results from the general CHBMI together with the proposed PID and ANN controller based 7-level CHBMI models will be compared and discussed to verifyl the proposed ANN controller based 7-level CHBMI achieved a lower output voltage THD value with a better sinusoidal output performance.

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Bakou, Youcef, Mohamed Abid, Lakhdar Saihi, Abdel Ghani Aissaoui, and Youcef Hammaoui. "Hybrid sliding neural network controller of a direct driven vertical axis wind turbine." Bulletin of Electrical Engineering and Informatics 12, no. 1 (February 1, 2023): 10–20. http://dx.doi.org/10.11591/eei.v12i1.4214.

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This study aims to propose a robust hybrid sliding mode artificial neural network control (SM-ANN) scheme for controlling the stator power (active/reactive) of a doubly fed induction generator (DFIG)-based direct drive vertical axis wind turbine (VAWT) power system under a real-world scenario wind speed that will be installed in the Adrar region (Saharan zone) of Algeria. The SM-ANN scheme will control the stator power of the direct drive VAWT power. The chattering phenomenon is the most significant disadvantage associated with sliding mode control (SMC). In order to find a solution to this issue, the artificial neural network (ANN) method was applied to pick the appealing part of the SMC. MATLAB/Simulink is used to do an evaluation, after which the SM-ANN controller being suggested is compared to both traditional sliding mode (SM) and proportional-integral (PI) controllers. The results of the simulation demonstrated that the recommended SM-ANN controller has good performance in terms of enhancing the quality of energy that is delivered to the power network. This is in comparison to the traditional SM and PI controllers, which both have a long history of use. Notwithstanding the fact that there is DFIG parameter fluctuation present.

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Purushotham, K. "Design and Implementation of Electric Vehicle Technology by Using ANN Controller." International Journal for Research in Applied Science and Engineering Technology 10, no. 1 (January 31, 2022): 1376–87. http://dx.doi.org/10.22214/ijraset.2022.40065.

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Abstract: It is possible to utilise EVs as both a load and provider of energy using the Vehicle-to-Grid (V2G) approach (or Gridto-Vehicle technique if EVs are used as a load). With this technology, industrial microgrids may have voltage and power flow regulation and congestion management. An no of electric vehicles with a variety of charging profiles, battery states of charge and electric vehicle counts may benefit from two separate controllers (grid regulation and charger controller), according to the controllers, It is possible to regulate the main power flow and voltage drop in an industrial microgrid by allowing bidirectional power flow. Simulations indicate that the suggested controllers can regulate an industrial microgrid's voltage levels and power flow. According to industrial microgrids include solar, wind farms, electric car fleets, industrial loads, commercial loads, and a diesel generator. MATLAB/SIMULINK is used to simulate and analyze the results. Keywords: Electric vehicles (EV), State of Charge (SOC), Grid Regulation Power Genereation Controller(GRPGC), Charge Controller(CC), Grid to Vehicle(G2V), Vehicle to Grid(V2G), Industrial Microgrid(IMG), Grid Regulation Controller(GRC), Destributed Energy Resources(DER), Diesel Generators(DGs).

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Chen, Guoshao, and Zhiping Liu. "Artificial Neural Network-Based Feed-Forward and Feedback Control Design and Convergence Analysis." Mathematical Problems in Engineering 2022 (May 6, 2022): 1–10. http://dx.doi.org/10.1155/2022/1238020.

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A feed-forward and feedback control scheme based on artificial neural network (ANN) and iterative learning control is proposed. Iterative learning control and ANN are combined as a feed-forward controller, which makes the output track the desired trajectory. Feedback control is introduced to reduce the effect of disturbances. To combine the feed-forward controller and the feedback controller, the ANN is employed to simulate the plant. Since the ANN can update the weights online, it is always consistent with the plant. The convergence and robustness of the system are analyzed, and the simulation shows the feasibility of the proposed control scheme.

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Idrissi, Yassine El Aidi, Khalid Assalaou, Lahoussine Elmahni, and Elmostafa Aitiaz. "New improved MPPT based on artificial neural network and PI controller for photovoltaic applications." International Journal of Power Electronics and Drive Systems (IJPEDS) 13, no. 3 (September 1, 2022): 1791. http://dx.doi.org/10.11591/ijpeds.v13.i3.pp1791-1801.

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This paper details an maximum power point tracking (MPPT) approach based on artificial neural network (ANN) to track the maximum power produced by a PV panel. This approach is rapid and accurate for following the maximum power point (MPP) during changes in weather conditions such as solar irradiation and temperature. A PV system structure including an MPPT controller is studied, designed, and simulated in this work. The aim of this paper is to use the artificial neural network (ANN) technique to develop a MPPT controller for PV applications. To increase the performance of the ANN-MPPT controller, a proportional integral (PI) controller is also included. In addition, the performance of an ANN-based MPPT controller is also compared to the conventional perturb and observe (P&O) method. To analyze the results, simulations are performed by using MATLAB software.

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Giri, Surya Prakash, and Sunil Kumar Sinha. "Four-Area Load Frequency Control of an Interconnected Power System Using Neuro-Fuzzy Hybrid Intelligent Proportional and Integral Control Approach." Journal of Intelligent Systems 22, no. 2 (June 1, 2013): 131–53. http://dx.doi.org/10.1515/jisys-2012-0025.

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AbstractThis article presents a novel control approach, hybrid neuro-fuzzy (HNF), for the load frequency control (LFC) of a four-area interconnected power system. The advantage of this controller is that it can handle nonlinearities, and at the same time, it is faster than other existing controllers. The effectiveness of the proposed controller in increasing the damping of local and inter-area modes of oscillation is demonstrated in a four-area interconnected power system. Areas 1 and 2 consist of a thermal reheat power plant, whereas Areas 3 and 4 consist of a hydropower plant. Performance evaluation is carried out by using fuzzy, artificial neural network (ANN), adaptive neuro-fuzzy inference system, and conventional proportional and integral (PI) control approaches. Four different models with different controllers are developed and simulated, and performance evaluations are carried out with said controllers. The result shows that the intelligent HNF controller has improved dynamic response and is at the same time faster than ANN, fuzzy, and conventional PI controllers.

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Gounder, Yasoda Kailasa, and Sowkarthika Subramanian. "Application of machine learning controller in matrix converter based on model predictive control algorithm." International Journal of Power Electronics and Drive Systems (IJPEDS) 14, no. 3 (September 1, 2023): 1489. http://dx.doi.org/10.11591/ijpeds.v14.i3.pp1489-1496.

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Finite control set model predictive control (FCS-MPC) algorithms are famous in power converter for its easy implementation of constraints with cost function than classical control algortihms. However computation complexity increases when swicthing state is high for converters such as matrix converter, multilevel converters and this impose a serious drawback to compute multi-step prediction horizon MPC algorithm which further increases the computation. To overcome the above said difficulty, machine learning based artificial neural network (ANN) controller for matrix converter is proposed. The training data for ANN controller is derived from MPC algorithm and trained offline with an accuracy of 70.3%. The proposed ANN controller shows a similar and better performance than MPC controller in terms of total harmonic distortion (THD), peak overshoot during dynamic change in reference current and dynamic change in load parameter and less computation with less execution time. Further, ANN controller for matrix converter is tested in OPAL-RT using hardware in-loop (HIL) simulation and showed that it outperforms MPC controller.

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Pajchrowski, Tomasz, Konrad Urbański, and Krzysztof Zawirski. "Artificial neural network based robust speed control of permanent magnet synchronous motors." COMPEL - The international journal for computation and mathematics in electrical and electronic engineering 25, no. 1 (January 1, 2006): 220–34. http://dx.doi.org/10.1108/03321640610634461.

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PurposeThe aim of the paper is to find a simple structure of speed controller robust against drive parameters variations. Application of artificial neural network (ANN) in the controller of PI type creates proper non‐linear characteristics, which ensures controller robustness.Design/methodology/approachThe robustness of the controller is based on its non‐linear characteristic introduced by ANN. The paper proposes a novel approach to neural controller synthesis to be performed in two stages. The first stage consists in training the ANN to form the proper shape of the control surface, which represents the non‐linear characteristic of the controller. At the second stage, the PI controller settings are adjusted by means of the random weight change (RWC) procedure, which optimises the control quality index formulated in the paper. The synthesis is performed using simulation techniques and subsequently the behaviour of a laboratory speed control system is validated in the experimental set‐up. The control algorithms of the system are performed by a microprocessor floating point DSP control system.FindingsThe proposed controller structure with proper control surface created by ANN guarantees expected robustness.Originality/valueThe original method of robust controller synthesis was proposed and validated by simulation and experimental investigations.

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Faiz, Aunowar Mohammad, and Jacqueline Lukose. "Optimization of Series Compensation in Transmission Networks Using Artificial Neural Networks." Journal of Computational and Theoretical Nanoscience 16, no. 8 (August 1, 2019): 3443–54. http://dx.doi.org/10.1166/jctn.2019.8306.

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To respond to the ever-increasing power demand of load centers, power is transmitted at extrahigh voltages. However, an increase in power transfer level should be supported by an enhanced level of security. Flexible AC Transmission System (FACTS) devices present an economical and efficient alternative to consider for achieving higher power transfer level with enhanced security instead of introducing new transmission facilities, to maintain a large stability margin of power in transmission line. This project aims to optimize the level of series compensation in transmission networks using Artificial Neural Network (ANN). Series compensation enables higher level of power to be transferred by reducing (reactive) losses. Among the series FACTS controllers, Thyristor Controlled Series Compensator (TCSC) has been chosen to be optimized on an SMIB system. Lead-Lag (LL) based TCSC remain the controller of choice due to the favorable performance to cost ratio. Nevertheless, modeling the highly non-linear power system with a linear controller, limits the system’s performance during adversities. ANN being non-linear per se and possessing high generalization capabilities, offers more versatility in modeling the power system. Indeed, an ANN based TCSC was designed and the performance during contingencies was compared to that of the LL based TCSC. As expected, the ANN based TCSC demonstrated a damping capability twice as fast and offering the SMIB system with a higher robustness as well as better resistance to fault condition. To increase the accuracy and reliability of the proposed controller, the investigation can be performed on a multi-machine system with different loading conditions as well as determining the optimal location of the TCSC module.

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Katuri, Raghavaiah, and Srinivasa Rao Gorantla. "Design and Analysis of Math Function Based Controller Combined with Fuzzy Logic Applied to the Solar-Powered Electric Vehicle." Fronteiras: Journal of Social, Technological and Environmental Science 11, no. 1 (April 29, 2022): 315–32. http://dx.doi.org/10.21664/2238-8869.2022v11i1.p315-332.

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The transition between the battery and ultracapacitor (UC) according to the driver requirements is the key obstacle that is related to the hybrid energy storage system (HESS) powered electric vehicles (EVs). In this effort, an innovative control scheme has been proposed, to switch the power sources corresponding to the vehicle dynamics. An MFB controller is considered with 4- math functions and programmed independently. Thereafter, a new hybrid controller has been formed by joining the designed MFB controller with an artificial neural network (ANN) to achieve the precise transition between battery and UC. The battery gets charged from the photovoltaic (PV) panel corresponding to the irradiance and temperature availability. The charging and discharging periods of battery mainly depending upon the control switches (CS) present in the circuit. From those two controllers, ANN generates required switching pulses whereas the MFB controller regulates the pulse depending upon the speed of the motor. Finally, the combination of MFB with ANN controller produces the required pulse signals to switches present in unidirectional (UDC) and bidirectional converter (BDC) related to the speed of the motor. The MATLAB/Simulink model of the system is done in four modes with different loads, and all results are discussed in the simulation and results section.

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Omran, Mohamed Asghaiyer, Izzeldin I. Ibrahim, Abu Zaharin Ahmad, Mohamed Salem, Mohamad Milood Almelian, Awang Jusoh, and Tole Sutikno. "Comparisons of PI and ANN controllers for shunt HPF based on STF-PQ Algorithm under distorted grid voltage." International Journal of Power Electronics and Drive Systems (IJPEDS) 10, no. 3 (September 1, 2019): 1339. http://dx.doi.org/10.11591/ijpeds.v10.i3.pp1339-1346.

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This paper proposes a shunt hybrid power filter (HPF) for harmonic currents and reactive power compensation under a distorted voltage and in a polluted environment. For this purpose, the reference current of the shunt HPF is computed based on the instantaneous reactive power (p-q) theory with self-tuning filter (STF). In order to adjust the dc voltage as a reference value, PI and ANN controllers have been utilized. Moreover, the system has been implemented and simulated in a MATLAB-SIMULINK platform, and selected results are presented. Therefore, the results verified the good dynamic performance, transient stability and strong robustness of the ANN controller. Furthermore, the shunt HAPF with ANN controller has been found to be in agreement with the IEEE 519-1992 standard recommendations on harmonic levels.

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Ibrahim, Mohammad Ahmed, Ali Saleh Saleh, and Ali Nathem Hamoody. "Performance enhancement of small-scale wind turbine based on artificial neural network." International Journal of Power Electronics and Drive Systems (IJPEDS) 14, no. 3 (September 1, 2023): 1722. http://dx.doi.org/10.11591/ijpeds.v14.i3.pp1722-1730.

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Small-scale wind turbine is typically designed to resisted extreme wind; this work aims to adjust their pitch angle based on simulations that use standardization codes for wind turbines. Proportional integral derivative (PID) and artificial neural network (ANN) controllers are used to control the speed of wind turbines. The ideal action for controlling the blade pitch angle can be attained by providing the controller with speed information ahead of time, allowing the controller to provide the best action for blade pitch angle control. The results of this work represent the relationship between the turbine speed with respect to time at different pitch angle. It has been concluded that the ANN controller produced the best time response as compared with the PID controller.

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Khomenko, Veligorskyi, Chakirov, and Vagapov. "An ANN-Based Temperature Controller for a Plastic Injection Moulding System." Electronics 8, no. 11 (November 1, 2019): 1272. http://dx.doi.org/10.3390/electronics8111272.

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This paper proposes an approach to an ANN-based temperature controller design for a plastic injection moulding system. This design approach is applied to the development of a controller based on a combination of a classical ANN and integrator. The controller provides a fast temperature response and zero steady-state error for three typical heaters (bar, nozzle, and cartridge) for a plastic moulding system. The simulation results in Matlab Simulink software and in comparison to an industrial PID regulator have shown the advantages of the controller, such as significantly less overshoot and faster transient (compared to PID with autotuning) for all examined heaters. In order to verify the proposed approach, the designed ANN controller was implemented and tested using an experimental setup based on an STM32 board.

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Jawad, Raheel, Majda Ahmed, Hussein Salih, and Yasser Mahmood. "Variable Speed Controller of Wind Generation System using Model predictive Control and NARMA Controller." Iraqi Journal for Electrical and Electronic Engineering 18, no. 2 (June 29, 2022): 43–52. http://dx.doi.org/10.37917/ijeee.18.2.6.

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This paper applied an artificial intelligence technique to control Variable Speed in a wind generator system. One of these techniques is an offline Artificial Neural Network (ANN-based system identification methodology, and applied conventional proportional-integral-derivative (PID) controller). ANN-based model predictive (MPC) and remarks linearization (NARMA-L2) controllers are designed and employed to manipulate Variable Speed in the wind technological knowledge system. All parameters of controllers are set up by the necessities of the controller’s design. The effects show a neural local (NARMA-L2) can attribute even higher than PID. The settling time, upward jab time, and most overshoot of the response of NARMA-L2 is a notable deal an awful lot less than the corresponding factors for the accepted PID controller. The conclusion from this paper can be to utilize synthetic neural networks of industrial elements and sturdy manageable to be viewed as a dependable desire to normal modeling, simulation, and manipulation methodologies. The model developed in this paper can be used offline to structure and manufacturing points of conditions monitoring, faults detection, and troubles shooting for wind generation systems.

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Moore, Jared M., and Philip K. McKinley. "Evolution of Joint-Level Control for Quadrupedal Locomotion." Artificial Life 23, no. 1 (February 2017): 58–79. http://dx.doi.org/10.1162/artl_a_00222.

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We investigate a hierarchical approach to robot control inspired by joint-level control in animals. The method combines a high-level controller, consisting of an artificial neural network (ANN), with joint-level controllers based on digital muscles. In the digital muscle model (DMM), morphological and control aspects of joints evolve concurrently, emulating the musculoskeletal system of natural organisms. We introduce and compare different approaches for connecting outputs of the ANN to DMM-based joints. We also compare the performance of evolved animats with ANN-DMM controllers with those governed by only high-level (ANN-only) and low-level (DMM-only) controllers. These results show that DMM-based systems outperform their ANN-only counterparts while also exhibiting less complex ANNs in terms of the number of connections and neurons. The main contribution of this work is to explore the evolution of artificial systems where, as in natural organisms, some aspects of control are realized at the joint level.

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Nagendar, Kavati, and V. Vijaya Rama Raju. "ANN Based Current Controller for Hybrid Electric Vehicles." E3S Web of Conferences 309 (2021): 01065. http://dx.doi.org/10.1051/e3sconf/202130901065.

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The use of Hybrid Electric Vehicles (HEVs) across the world is growing enormously every day. The single-phase bi-directional convertors are presented in this study for HEVs on-board charging(OBC). In HEVs, we use power electronics converters for the converting and inverting operations. Artificial Neural Network(ANN) is presented in this study for simple operation and high optimization approaches. ANN control technique regulates the system's THD and enhances charging system optimization, enables two-way power delivery that is from the grid to vehicle and the vehicle to grid. An ANN based current controller model that achieves fast-dynamic reaction and that improves grid current harmonic characteristics is proposed in this study. The system's THD is reduced by the ANN controller being suggested. The results prove the validity and feasibility of design and control technique of the proposed integrated charging system.

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Chien, Ting-Hsuan, Yu-Chuan Huang, and Yuan-Yih Hsu. "Neural Network-Based Supplementary Frequency Controller for a DFIG Wind Farm." Energies 13, no. 20 (October 13, 2020): 5320. http://dx.doi.org/10.3390/en13205320.

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An artificial neural network (ANN)-based supplementary frequency controller is designed for a doubly fed induction generator (DFIG) wind farm in a local power system. Since the optimal controller gain that gives highest the frequency nadir or lowest peak frequency is a complicated nonlinear function of load disturbance and system variables, it is not easy to use analytical methods to derive the optimal gain. The optimal gain can be reached through an exhaustive search method. However, the exhaustive search method is not suitable for online applications, since it takes a long time to perform a great number of simulations. In this work, an ANN that uses load disturbance, wind penetration, and wind speed as the inputs and the desired controller gain as the output is proposed. Once trained by a proper set of training patterns, the ANN can be employed to yield the desired gain in a very efficient manner, even when the operating condition is not included in the training set. Therefore, the proposed ANN-based controller can be used for real-time frequency control. Results from MATLAB/SIMULINK simulations performed on a local power system in Taiwan reveal that the proposed ANN can yield a better frequency response than the fixed-gain controller.

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G. M. Abdolrasol, Maher, Mahammad Abdul Hannan, S. M. Suhail Hussain, Taha Selim Ustun, Mahidur R. Sarker, and Pin Jern Ker. "Energy Management Scheduling for Microgrids in the Virtual Power Plant System Using Artificial Neural Networks." Energies 14, no. 20 (October 11, 2021): 6507. http://dx.doi.org/10.3390/en14206507.

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This study uses an artificial neural network (ANN) as an intelligent controller for the management and scheduling of a number of microgrids (MGs) in virtual power plants (VPP). Two ANN-based scheduling control approaches are presented: the ANN-based backtracking search algorithm (ANN-BBSA) and ANN-based binary practical swarm optimization (ANN-BPSO) algorithm. Both algorithms provide the optimal schedule for every distribution generation (DG) to limit fuel consumption, reduce CO2 emission, and increase the system efficiency towards smart and economic VPP operation as well as grid decarbonization. Different test scenarios are executed to evaluate the controllers’ robustness and performance under changing system conditions. The test cases are different load curves to evaluate the ANN’s performance on untrained data. The untrained and trained load models used are real-load parameter data recorders in northern parts of Malaysia. The test results are analyzed to investigate the performance of these controllers under varying power system conditions. Additionally, a comparative study is performed to compare their performances with other solutions available in the literature based on several parameters. Results show the superiority of the ANN-based controllers in terms of cost reduction and efficiency.

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Xavier, S. Arockia Edwin, P. Venkatesh, and M. Saravanan. "A Perfomance study of Ann and Anfis Controller for Statcom in dSpace Environment." Journal of Electrical Engineering 64, no. 3 (May 1, 2013): 159–65. http://dx.doi.org/10.2478/jee-2013-0023.

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Reactive power compensation is an important issue in the control of electric power system. Reactive power from the source increases the transmission losses and reduces the power transmission capability of the transmission lines. Moreover, reactive power should not be transmitted through the transmission line to a longer distance. Hence Flexible AC Transmission Systems (FACTS) devices such as static compensator (STATCOM) unified power flow controller (UPFC) and static volt-ampere compensator (SVC) are used to alleviate these problems. In this paper, a voltage source converter (VSC) based STATCOM is developed with Artificial Neural Network Controller (ANNC) and Adaptive Neuro Fuzzy Inference System(ANFIS) controllers. The conventional PI controller has more tuning difficulties while the system parameter changes, whereas a trained neural network and ANFIS controllers requires less computation time. They have the ability to generalize and can interpolate in between the training data. The ANNC and ANFIS controllers designed were tested on a 75 V, 100 VA STATCOM in real time environment via state-of-the-art of digital signal processor advanced control engineering (dSPACE) DS1104 board and it was found that ANFIS controller was producing better results than the ANNC.

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Rameshappa, Basavarajappa Sokke, and Nagaraj Mudakapla Shadaksharappa. "An optimal artificial neural network controller for load frequency control of a four-area interconnected power system." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 5 (October 1, 2022): 4700. http://dx.doi.org/10.11591/ijece.v12i5.pp4700-4711.

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In this paper, an optimal artificial neural network (ANN) controller for load frequency control (LFC) of a four-area interconnected power system with non-linearity is presented. A feed forward neural network with multi-layers and Bayesian regularization backpropagation (BRB) training function is used. This controller is designed on the basis of optimal control theory to overcome the problem of load frequency control as load changes in the power system. The system comprised of transfer function models of twothermal units, one nuclear unit and one hydro unit. The controller model is developed by considering generation rate constraint (GRC) of different units as a non-linearity. The typical system parameters obtained from IEEE press power engineering series and EPRI books. The robustness, effectiveness, and performance of the proposed optimal ANN controller for a step load change and random load change in the system is simulated through using MATLAB-Simulink. The time response characteristics are compared with that obtained from the proportional, integral and derivative (PID) controller and non-linear autoregressive-moving average (NARMA-L2) controller. The results show that the algorithm developed for proposed controller has a superiority in accuracy as compared to other two controllers.

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Swami, Omkar, and Seema Diwan. "Control Algorithms for DSTATCOM Using ANN Controller." International Journal of Electrical and Electronics Engineering 8, no. 7 (July 25, 2021): 18–22. http://dx.doi.org/10.14445/23488379/ijeee-v8i7p105.

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Poyen, Faruk Bin, Soumya Roy, Apurba Ghosh, and Rajib Bandyopadhyay. "RETRACTED: Automated Irrigation by an ANN Controller." Procedia Computer Science 46 (2015): 257–67. http://dx.doi.org/10.1016/j.procs.2015.02.019.

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BENBOUHENNI, Habib, Abdelkader BELAIDI, and Zinelaabidine BOUDJEMA. "POWER RIPPLE REDUCTION OF DPC DFIG DRIVE USING ANN CONTROLLER." Acta Electrotechnica et Informatica 20, no. 1 (April 23, 2020): 15–22. http://dx.doi.org/10.15546/aeei-2020-0003.

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Almelian, Mohamad Milood, Izzeldin I. Mohd, Abu Zaharin Ahmad, Mohamed Salem, Mohamed A. Omran, Awang Jusoh, and Tole Sutikno. "Enhancing the performance of cascaded three-level VSC STATCOM by ANN controller with SVPWM integegration." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 5 (October 1, 2019): 3880. http://dx.doi.org/10.11591/ijece.v9i5.pp3880-3890.

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This article presents a cascaded three-level voltage source converter (VSC) based STATCOM employing an artificial neuron network (ANN) controller with a new simple circuit of space vector pulse width modulation (SVPWM) technique. The main aim of utilizing ANN controller and SVPWM technique is to minimize response time (RT) of STATCOM and improve its performance regard to PF amplitude, and total harmonic distortion (THD) of VSC output current during the period of lagging/leading PF loads (inductive/capacitive loads). The performance of STATCOM is tested using MATLAB/SIMULINK in IEEE 3-bus system. The simulation results clearly proved that the STATCOM with intelligent controller is more efficient compared to a conventional controller (PI controller), where ANN enables the voltage and current to be in the same phase rapidly (during 1.5 cycles) with THD less than 5%.

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Lin, Chih-Jer, and Ting-Yi Sie. "Design and Experimental Characterization of Artificial Neural Network Controller for a Lower Limb Robotic Exoskeleton." Actuators 12, no. 2 (January 27, 2023): 55. http://dx.doi.org/10.3390/act12020055.

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This study aims to develop a lower limb robotic exoskeleton with the use of artificial neural networks for the purpose of rehabilitation. First, the PID control with iterative learning controller is used to test the proposed lower limb robotic exoskeleton robot (LLRER). Although the hip part using the flat brushless DC motors actuation has good tracking results, the knee part using the pneumatic actuated muscle (PAM) actuation cannot perform very well. Second, to compensate this nonlinearity of PAM actuation, the artificial neural network (ANN) feedforward control based on the inverse model trained in advance are used to compensate the nonlinearity of the PAM. Third, a particle swarm optimization (PSO) is used to optimize the PID parameters based on the ANN-feedforward architecture. The developed controller can complete the tracking of one gait cycle within 3.6 s for the knee joint. Among the three controllers, the controller of the ANN-feedforward with PID control (PSO tuned) performs the best, even when the LLRER is worn by the user and the tracking performance is still very good. The average Mean Absolute Error (MAE) of the left knee joint is 1.658 degrees and the average MAE of the right knee joint is 1.392 degrees. In the rehabilitation tests, the controller of ANN-feedforward with PID control is found to be suitable and its versatility for different walking gaits is verified during human tests. The establishment of its inverse model does not need to use complex mathematical formulas and parameters for modeling. Moreover, this study introduces the PSO to search for the optimal parameters of the PID. The architecture diagram and the control signal given by the ANN compensation with the PID control can reduce the error very well.

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Almusawi, Ahmed R. J., L. Canan Dülger, and Sadettin Kapucu. "A New Artificial Neural Network Approach in Solving Inverse Kinematics of Robotic Arm (Denso VP6242)." Computational Intelligence and Neuroscience 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/5720163.

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This paper presents a novel inverse kinematics solution for robotic arm based on artificial neural network (ANN) architecture. The motion of robotic arm is controlled by the kinematics of ANN. A new artificial neural network approach for inverse kinematics is proposed. The novelty of the proposed ANN is the inclusion of the feedback of current joint angles configuration of robotic arm as well as the desired position and orientation in the input pattern of neural network, while the traditional ANN has only the desired position and orientation of the end effector in the input pattern of neural network. In this paper, a six DOF Denso robotic arm with a gripper is controlled by ANN. The comprehensive experimental results proved the applicability and the efficiency of the proposed approach in robotic motion control. The inclusion of current configuration of joint angles in ANN significantly increased the accuracy of ANN estimation of the joint angles output. The new controller design has advantages over the existing techniques for minimizing the position error in unconventional tasks and increasing the accuracy of ANN in estimation of robot’s joint angles.

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Charnal, Prashant, Raunak Jangid, and Kapil Parikh. "COMPARATIVE PERFORMANCE ANALYSIS ON ARTIFICIAL NEURAL NETWORK BASED MPPT CONTROLLER WITH CONVENTIONAL CONTROLLER FOR GRID CONNECTED WIND ENERGY CONVERSION SYSTEM." International Journal of Technical Research & Science 7, no. 09 (September 25, 2022): 12–19. http://dx.doi.org/10.30780/ijtrs.v07.i09.003.

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This paper focuses on the development of ANN based MPPT interfaced Permanent magnet synchronous generator (PMSG) for wind energy conversion system (WECS). It focuses on design and modeling aspects of the completely different elements of the WECS like the fundamental model of ANN based MPPT controller, MLI, wind turbine, optimum maximum power point tracking system utilizing MATLAB/Simulink. Major object is to extract the maximum energy from the wind which confirms a highest efficiency of developed system. The thesis shows the model of wind turbine together with the model of PMSG. The ANN based mostly MPPT approach used right here is predicated on Perturbation and observation (P&O). To complete the process of modeling and simulation the platform of MATLAB/Simulink is utilized. Developed ANN are most efficient technique compared to conventional methods. It achieves maximum power with more stability, precision and better performance with good dynamic response under variable wind speed conditions. It exhibits the enhancements for the developed system and control action for balance/unbalanced steady state in addition to transient, dynamic response circumstances.

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Eyng, Eduardo, Flávio Vasconcelos da Silva, Fernando Palú, and Ana Maria Frattini Fileti. "Neural network based control of an absorption column in the process of bioethanol production." Brazilian Archives of Biology and Technology 52, no. 4 (August 2009): 961–72. http://dx.doi.org/10.1590/s1516-89132009000400020.

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Gaseous ethanol may be recovered from the effluent gas mixture of the sugar cane fermentation process using a staged absorption column. In the present work, the development of a nonlinear controller, based on a neural network inverse model (ANN controller), was proposed and tested to manipulate the absorbent flow rate in order to control the residual ethanol concentration in the effluent gas phase. Simulation studies were carried out, in which a noise was applied to the ethanol concentration signals from the rigorous model. The ANN controller outperformed the dynamic matrix control (DMC) when step disturbances were imposed to the gas mixture composition. A security device, based on a conventional feedback algorithm, and a digital filter were added to the proposed strategy to improve the system robustness when unforeseen operating and environmental conditions occured. The results demonstrated that ANN controller was a robust and reliable tool to control the absorption column.

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Al-Majidi, Sadeq D., Mohammed Kh. AL-Nussairi, Ali Jasim Mohammed, Adel Manaa Dakhil, Maysam F. Abbod, and Hamed S. Al-Raweshidy. "Design of a Load Frequency Controller Based on an Optimal Neural Network." Energies 15, no. 17 (August 26, 2022): 6223. http://dx.doi.org/10.3390/en15176223.

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A load frequency controller (LFC) is a crucial part in the distribution of a power system network (PSN) to restore its frequency response when the load demand is changed rapidly. In this paper, an artificial neural network (ANN) technique is utilised to design the optimal LFC. However, the training of the optimal ANN model for a multi-area PSN is a major challenge due to its variations in the load demand. To address this challenge, a particle swarm optimization is used to distribute the nodes of a hidden layer and to optimise the initial neurons of the ANN model, resulting in obtaining the lower mean square error of the ANN model. Hence, the mean square error and the number of epochs of the ANN model are minimised to about 9.3886 × 10−8 and 25, respectively. To assess this proposal, a MATLAB/Simulink model of the PSN is developed for the single-area PSN and multi-area PSN. The results show that the LFC based on the optimal ANN is more effective for adjusting the frequency level and improves the power delivery of the multi-area PSN comparison with the single-area PSN. Moreover, it is the most reliable for avoiding the fault condition whilst achieving the lowest time multiplied absolute error about 3.45 s when compared with the conventional ANN and PID methods.

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R, Saranya, and Thangavel S. "Hardware Implementation of Induction Motor using ANN Controller under Low Speed Operation." Indonesian Journal of Electrical Engineering and Computer Science 2, no. 3 (May 7, 2016): 522. http://dx.doi.org/10.11591/ijeecs.v2.i3.pp522-529.

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For the high performance drives the artificial neural network based Induction motor is proposed. During the load variation, the performance of the Induction motor proves to be low. Intelligent controller provided for controlling the speed of induction motor especially with high dynamic disturbances. An effective sensorless strategy based on artificial neural network controller is developed to estimate rotor’s position and to regulate the stator flux under low speed, helps to track the motor speed accurately during the whole operating region. The overall combination of this setup is simulated in the MATLAB/SIMULINK platform. Finally an experimental prototype of the proposed drive has been developed to validate the performance of Induction Motor and the dynamic speed response of Induction motor with proposed controller was estimated for various speed and found that the speed can be controlled effectively.

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Mouna, Ben Hamed, and Sbita Lassaad. "Internal Model Controller of an ANN Speed Sensorless Controlled Induction Motor Drives." Journal of Applied Sciences 7, no. 11 (May 15, 2007): 1456–66. http://dx.doi.org/10.3923/jas.2007.1456.1466.

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Kruse, Nicolas, Wilfried Tiedemann, Ingo Hoven, Rober Deja, Roland Peters, Felix Kunz, and Rudiger-A. Eichel. "Design and Experimental Investigation of Temperature Control for a 10 kW SOFC System Based on an Artificial Neuronal Network." ECS Transactions 111, no. 6 (May 19, 2023): 493–501. http://dx.doi.org/10.1149/11106.0493ecst.

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In this work the authors designed and experimentally evaluated different controller topologies for fuel cell operation (SOFC) of a reversible solid oxide cell (rSOC) system. Aim of the controller is to operate the SOFC system autonomously at a constant maximum temperature for maximum efficiency. The controller design incorporates an artificial neuronal network (ANN) for real time state predictions. The training data for the ANN was generated by a Digital Twin of this system. The generated training data consists of about 16,000 different steady state operating conditions.

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Praveenkumar, B., K. S.Srikanth, M. Kiran kumar, and G. G .Raja sekhar. "ANN Control Based Variable Speed PMSG-Based Wind Energy Conversion System." International Journal of Engineering & Technology 7, no. 2.7 (March 18, 2018): 526. http://dx.doi.org/10.14419/ijet.v7i2.7.10876.

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This paper presents a comparative analysis of three control algorithms for a wind turbine generator using a variable speed permanent magnet synchronous generator (PMSG). The design methodologies of the conventional PI based controller, the Taylor series expansion linear approximation based (TSLA-based) controller and the feedback linearization based (FL-based) nonlinear controller are provided. The objective is to keep the wind turbine operating at normal speed of the rotor at maximum power extraction (MPPT control), while insuring the power extracting from the turbine to the generator, regardless of the wind speed. The controller gains of the nonlinear controller are extended with Artificial Intelligence controller approach. The results show a better control performance for the ANN controller. This performance of Ann controller can be characterized by fast and smooth transient responses as well as a zero steady state error and reference tracking quality.

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Ghedhab, Nabila, Fatiha Youcefettoumi, Abdelhamid Loukriz, and Allaeddine Jouama. "Maximum Power Point tracking for a stand-alone photovoltaic system using Artificial Neural Network." E3S Web of Conferences 152 (2020): 01007. http://dx.doi.org/10.1051/e3sconf/202015201007.

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This paper presents an intelligent method to extract the maximum power from the photovoltaic panel using artificial neural network (ANN). The inputs data required for training the ANN controller are obtained from real weather conditions and the desired output is obtained from perturb and observe (P&O) method. The proposed model is capable to improve the dynamic response and steady-state performance of the system, provides an accurate identification of the optimal operating point and an accurate estimation of the maximum power from the photovoltaic panels. The proposed ANN model is compared with conventional P&O model and shown that ANN controller could increase the power output by approximately 20%. The system is simulated and studied using MATLAB software.

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Islam, M. Fakhrul, Joarder Kamruzzaman, and Guojun Lu. "Improved ANN Based Tap-Changer Controller Using Modified Cascade-Correlation Algorithm." Journal of Advanced Computational Intelligence and Intelligent Informatics 9, no. 3 (May 20, 2005): 226–34. http://dx.doi.org/10.20965/jaciii.2005.p0226.

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Artificial Neural Network (ANN) based tap changer control of closed primary bus and cross network connected parallel transformers has demonstrated potential use in power distribution system. In those research works the proposed ANN for application in this control were developed using various algorithms and concluded that a network trained by Bayesian Regularization (BR) backpropagation algorithm produced the best performance measured in terms of correct tap changing decisions. However, further improvement of ANN based transformer tap changer operation is always desirable. A general rule for obtaining good generalization is to use the smallest network that solves the problem. In this paper, we show that a small sized ANN is obtainable for further improvement of transformer tap changer operation by modifying the standard Cascade-Correlation algorithm. The modification incorporates weight smoothing of output layer weights in Cascade-Correlation learning using Bayesian frame work. Experimental results demonstrate that significant improvement in performance is achieved when an ANN is trained by modified Cascade-Correlation algorithm instead of standard Cascade-Correlation or Bayesian Regularization backpropagation algorithm. A comparison of performances of different algorithms in application to transformer tap changer operation is analyzed and the results are presented.

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Journal articles on the topic 'ANN CONTROLLER'

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