Relevant bibliographies by topics / Neural network controller

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Neural network controller'

Author: Grafiati
Published: 3 June 2025
Last updated: 13 July 2025

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Journal articles on the topic "Neural network controller"

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Leal, Hugo M., Ramiro S. Barbosa, and Isabel S. Jesus. "Control of a Mobile Line-Following Robot Using Neural Networks." Algorithms 18, no. 1 (2025): 51. https://doi.org/10.3390/a18010051.

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This work aims to develop and compare the performance of a line-following robot using both neural networks and classical controllers such as Proportional–Integral–Derivative (PID). Initially, the robot’s infrared sensors were employed to follow a line using a PID controller. The data from this method were then used to train a Long Short-Term Memory (LSTM) network, which successfully replicated the behavior of the PID controller. In a subsequent experiment, the robot’s camera was used for line-following with neural networks. Images of the track were captured, categorized, and used to train a convolutional neural network (CNN), which then controlled the robot in real time. The results showed that neural networks are effective but require more processing and calibration. On the other hand, PID controllers proved to be simpler and more efficient for the tested tracks. Although neural networks are very promising for advanced applications, they are also capable of handling simpler tasks effectively.
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Gücüyener, İsmet. "Fuzzy Neural-Network-Based Controller." Solid State Phenomena 220-221 (January 2015): 407–12. http://dx.doi.org/10.4028/www.scientific.net/ssp.220-221.407.

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Using a controller is necessary for any automation system. The controller must be cheap, reliable, user friendly and not cause any problems for inputs and outputs. Classical control systems like proportional integral derivative (PID) put adequate results of linear systems and continuous-time. In fact, real control systems are time-variant, with non-linearity and poorly calculated dynamic variables. For this reason, conventional control systems need an expert person to adjust controller parameters in general. Sometimes an operator is required to solve control problems. Human control is not completely reliable. Also, it does not include any electronic communication. In modern factories, every point must be monitored and electronically controlled from remote points when necessary. In this study, including every electronic communication channel, a simplified handling, low-cost, reliable, Fuzzy Neural Network Controller (FNNC) is designed.
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Yamada, Takayuki, and Tetsuro Yabuta. "Adaptive Neural Network Controllers for Dynamics Systems." Journal of Robotics and Mechatronics 2, no. 4 (1990): 245–57. http://dx.doi.org/10.20965/jrm.1990.p0245.

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Many studies such as Kawato's work have been undertaken in order to apply both the flexibility and learning ability of neural networks to dynamic system controllers. However, their characteristics have not yet been completely clarified. On the other hand, many studies have established conventional control theories such as adaptive control. If we can clarify the relationship between neural network controllers and adaptive controllers, the two control algorithms will be developed considerably by making use of the advantages of each. Therefore, this paper proposes a neural network direct controller in order to construct an interface between neural network and conventional control theories. This paper also proposes an open loop type of controller in order to realize inverse dynamics using only the neural network. Analytical approaches prove the local stability of the proposed controllers. Simulated and experimental results verify their realization and confirm their characteristics. This paper also discusses the relationship between neural network controllers and adaptive controllers.
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Zhang, Yong Chao, Wen Zhuang Zhao, and Jin Lian Chen. "The Research and Application of the Fuzzy Neural Network Control Based on Genetic Algorithm." Advanced Materials Research 403-408 (November 2011): 191–95. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.191.

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How fuzzy technology and neural networks and genetic algorithm combine with each other has become the focus of research. A fuzzy neural network controller was proposed based on defuzzification and optimization around the fuzzy neural network structure. Genetic algorithm of fuzzy neural network was brought forward based on optimal control theory. Optimal structure and parameters of fuzzy neural network controller were Offline searched by way of controller performance indicators of genetic algorithm. Fuzzy neural network controller through genetic algorithm was accessed in fuzzy neural network intelligent control system.
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Sunar, M., A. M. A. Gurain, and M. Mohandes. "Substructural neural network controller." Computers & Structures 78, no. 4 (2000): 575–81. http://dx.doi.org/10.1016/s0045-7949(00)00039-0.

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Ruano, A. E. B., D. I. Jones, and P. J. Fleming. "A neural network controller." IFAC Proceedings Volumes 24, no. 7 (1991): 27–32. http://dx.doi.org/10.1016/b978-0-08-041699-1.50009-4.

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Karunasena, G. M. K. B., H. D. N. S. Priyankara, and B. G. D. A. Madhusank. "Artificial Neural Network vs PID Controller for Magnetic Levitation System." International Journal of Innovative Science and Research Technology 5, no. 7 (2020): 505–11. http://dx.doi.org/10.38124/ijisrt20jul432.

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This research investigates the acceptability of the Artificial Neural Networks (ANN) over the PID Controller for the control of the Magnetic Levitation System (MLS). In the field of advanced control systems, this system identifies as a feedback-controlled, single input- single output (SISO) system. This SISO system used a PID controller for vertical trajectory controlling of a metal sphere in airspace by using an electromagnetic force that directed to upward. The vertical position of the metal sphere controlled according to the applied magnetic force generated by a powerful electromagnet and the electromagnetic force controlled by varying the supply voltage. To control this nonlinear system, we develop a multilayer artificial neural network by using Matlab software and integrate that with the physical magnetic levitation model. According to specific initial conditions, the actual responses of the magnetic levitation system with artificial neural network compares the desire response of the metal sphere. The ability of control this nonlinear system by using neural networks validate by comparing results obtained by the PID controller and artificial neural network.
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Lippe, Wolfram-M., Steffen Niendieck, and Andreas Tenhagen. "On the Optimization of Fuzzy-Controllers by Neural Networks." Journal of Advanced Computational Intelligence and Intelligent Informatics 3, no. 3 (1999): 158–63. http://dx.doi.org/10.20965/jaciii.1999.p0158.

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Methods are known for combining fuzzy-controllers with neural networks. One of the reasons of these combinations is to work around the fuzzy controllers’ disadvantage of not being adaptive. It is helpful to represent a given fuzzy controller by a neural network and to have rules adapted by a special learning algorithm. Some of these methods are applied in the NEFCONmode or the model of Lin and Lee. Unfortunately, none adapts all fuzzy-controller components. We suggest a new model enabling the user to represent a given fuzzy controller by a neural network and adapt its components as desired.
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Jiang, Yiming, Chenguang Yang, Shi-lu Dai, and Beibei Ren. "Deterministic learning enhanced neutral network control of unmanned helicopter." International Journal of Advanced Robotic Systems 13, no. 6 (2016): 172988141667111. http://dx.doi.org/10.1177/1729881416671118.

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In this article, a neural network–based tracking controller is developed for an unmanned helicopter system with guaranteed global stability in the presence of uncertain system dynamics. Due to the coupling and modeling uncertainties of the helicopter systems, neutral networks approximation techniques are employed to compensate the unknown dynamics of each subsystem. In order to extend the semiglobal stability achieved by conventional neural control to global stability, a switching mechanism is also integrated into the control design, such that the resulted neural controller is always valid without any concern on either initial conditions or range of state variables. In addition, deterministic learning is applied to the neutral network learning control, such that the adaptive neutral networks are able to store the learned knowledge that could be reused to construct neutral network controller with improved control performance. Simulation studies are carried out on a helicopter model to illustrate the effectiveness of the proposed control design.
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Liang, Haijun, Changyan Liu, Kuanming Chen, Jianguo Kong, Qicong Han, and Tiantian Zhao. "Controller Fatigue State Detection Based on ES-DFNN." Aerospace 8, no. 12 (2021): 383. http://dx.doi.org/10.3390/aerospace8120383.

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The fatiguing work of air traffic controllers inevitably threatens air traffic safety. Determining whether eyes are in an open or closed state is currently the main method for detecting fatigue in air traffic controllers. Here, an eye state recognition model based on deep-fusion neural networks is proposed for determination of the fatigue state of controllers. This method uses transfer learning strategies to pre-train deep neural networks and deep convolutional neural networks and performs network fusion at the decision-making layer. The fused network demonstrated an improved ability to classify the target domain dataset. First, a deep-cascaded neural network algorithm was used to realize face detection and eye positioning. Second, according to the eye selection mechanism, the pictures of the eyes to be tested were cropped and passed into the deep-fusion neural network to determine the eye state. Finally, the PERCLOS indicator was combined to detect the fatigue state of the controller. On the ZJU, CEW and ATCE datasets, the accuracy, F1 score and AUC values of different networks were compared, and, on the ZJU and CEW datasets, the recognition accuracy and AUC values among different methods were evaluated based on a comparative experiment. The experimental results show that the deep-fusion neural network model demonstrated better performance than the other assessed network models. When applied to the controller eye dataset, the recognition accuracy was 98.44%, and the recognition accuracy for the test video was 97.30%.
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Dissertations / Theses on the topic "Neural network controller"

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Sagiroglu, Serkan. "Adaptive Neural Network Applications On Missile Controller Design." Master's thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/3/12611106/index.pdf.

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In this thesis, adaptive neural network controllers are designed for a high subsonic cruise missile. Two autopilot designs are included in the study using adaptive neural networks, namely an altitude hold autopilot designed for the longitudinal channel and a directional autopilot designed for heading control. Aerodynamic coefficients are obtained using missile geometry<br>a 5-Degree of Freedom (5-DOF) simulation model is obtained, and linearized at a single trim condition. An inverted model is used in the controller. Adaptive Neural Network (ANN) controllers namely, model inversion controllers with Sigma-Pi Neural Network, Single Hidden Layer Neural Network and Background Learning implemented Single Hidden Layer Neural Network, are deployed to cancel the modeling error and are applied for the longitudinal and directional channels of the missile. This approach simplifies the autopilot designing process by combining a controller with model inversion designed for a single flight condition with an on-line learning neural network to account for errors that are caused due to the approximate inversion. Simulations are performed both in the longitudinal and directional channels in order to demonstrate the effectiveness of the implemented control algorithms. The advantages and drawbacks of the implemented neural network based controllers are indicated.
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Haynes, Barry P. "A neural network adaptive controller for non-linear systems." Thesis, University of Portsmouth, 1997. https://researchportal.port.ac.uk/portal/en/theses/a-neural-network-adaptive-controller-for-nonlinear-systems(19584462-246e-4de3-9e80-cda4923a38c1).html.

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Karakasoglu, Ahmet. "Neural network-based approaches to controller design for robot manipulators." Diss., The University of Arizona, 1991. http://hdl.handle.net/10150/185612.

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This dissertation is concerned with the development of neural network-based methods to the control of robot manipulators and focusses on three different approaches for this purpose. In the first approach, an implementation of an intelligent adaptive control strategy in the execution of complex trajectory tracking tasks by using multilayer neural networks is demonstrated by exploiting the pattern classification capability of these nets. The network training is provided by a rule-based controller which is programmed to switch an appropriate adaptive control algorithm for each component type of motion constituting the overall trajectory tracking task. The second approach is based on the capability of trained neural networks for approximating input-output mappings. The use of dynamical networks with recurrent connections and efficient supervised training policies for the identification and adaptive control of a nonlinear process are discussed and a decentralized adaptive control strategy for a class of nonlinear dynamical systems with specific application to robotic manipulators is presented. An effective integration of the modelling of inverse dynamics property of neural nets with the robustness to unknown disturbances property of variable structure control systems is considered as the third approach. This methodology yields a viable procedure for selecting the control parameters adaptively and for designing a model-following adaptive control scheme for a class of nonlinear dynamical systems with application to robot manipulators.
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Chan, Leonard. "Implementation of CMAC as a neural network controller on mechanical systems /." [St. Lucia, Qld.], 2003. http://www.library.uq.edu.au/pdfserve.php?image=thesisabs/absthe17135.pdf.

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Gonzalez, Juan. "Spacecraft Formation Control| Adaptive PID-Extended Memory Recurrent Neural Network Controller." Thesis, California State University, Long Beach, 2019. http://pqdtopen.proquest.com/#viewpdf?dispub=10978237.

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<p> In today&rsquo;s space industry, satellite formation flying has become a cost-efficient alternative solution for science, on-orbit repair and military time-critical missions. While in orbit, the satellites are exposed to the space environment and unpredictable spacecraft on-board disturbances that negatively affect the attitude control system&rsquo;s ability to reduce relative position and velocity error. Satellites utilizing a PID or adaptive controller are typically tune to reduce the error induced by space environment disturbances. However, in the case of an unforeseen spacecraft disturbance, such as a fault in an IMU, the PID based attitude control system effectiveness will deteriorate and will not be able to reduce the error to an acceptable magnitude. </p><p> In order to address the shortcomings a PID-Extended Memory RNN (EMRNN) adaptive controller is proposed. A PID-EMRNN with a short memory of multiple time steps is capable of producing a control input that improves the translational position and velocity error transient response compared to a PID. The results demonstrate the PID-EMRNN controller ability to generate a faster settling and rise time for control signal curves. The PID-EMRNN also produced similar results for an altitude range of 400 km to 1000 km and inclination range of 40 to 65 degrees angles of inclination. The proposed PID-EMRNN adaptive controller has demonstrated the capability of yielding a faster position error and control signal transient response in satellite formation flying scenario. </p><p>
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Lang, Michael. "A real-time implementation of a neural-network controller for industrial robotics." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/tape17/PQDD_0008/NQ35217.pdf.

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Rose, Stephen Matthew. "Online training of a neural network controller by improved reinforcement back-propagation." Thesis, Georgia Institute of Technology, 2002. http://hdl.handle.net/1853/19177.

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Rathbone, Kevin. "Evolving visually guided neural network robot arm controllers for lifetime learning." Thesis, University of Sheffield, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.327646.

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Ariss, Joseph, and Salim Rabat. "A comparison between a traditional PID controller and an Artificial Neural Network controller in manipulating a robotic arm." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-259365.

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Robotic and control industry implements different control technique to control the movement and the position of a robotic arm. PID controllers are the most used controllers in the robotics and control industry because of its simplicity and easy implementation. However, PIDs’ performance suffers under noisy environments. In this research, a controller based on Artificial Neural Networks (ANN) called the model reference controller is examined to replace traditional PID controllers to control the position of a robotic arm in a noisy environment. Simulations and implementations of both controllers were carried out in MATLAB. The training of the ANN was also done in MATLAB using the Supervised Learning (SL) model and Levenberg-Marquardt backpropagation algorithm. Results shows that the ANN implementation performs better than traditional PID controllers in noisy environments.<br>Robotoch kontrollindustrin implementerar olika kontrolltekniker för att styra rörelsen och placeringen av en robotarm. PID-styrenheter är de mest använda kontrollerna inom roboten och kontrollindustrin på grund av dess enkelhet och lätt implementering. PID:s prestanda lider emellertid i bullriga miljöer. I denna undersökning undersöks en styrenhet baserad på Artificiell Neuralt Nätverk (ANN) som kallas modellreferenskontrollen för att ersätta traditionella PID-kontroller för att styra en robotarm i bullriga miljöer. Simuleringar och implementeringar av båda kontrollerna utfördes i MATLAB. Utbildningen av ANN:et gjordes också i MATLAB med hjälp av Supervised Learning (SL) -modellen och LevenbergMarquardt backpropagationsalgoritmen. Resultat visar att ANN-implementeringen fungerar bättre än traditionella PID-kontroller i bullriga miljöer.
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Conlon, Martin J. "Design and evaluation of a neural network-based controller for an artificial heart." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape3/PQDD_0015/MQ57723.pdf.

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Books on the topic "Neural network controller"

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Haynes, B. P. A neural network adaptive controller for non-linear systems. University of Portsmouth, Faculty of Technology, 1997.

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Denise, Taylor Lynore, and United States. National Aeronautics and Space Administration., eds. Artificial neural network implementation of a near-ideal error prediction controller. Dept. of Electrical Engineering, School of Engineering and Applied Science, University of Virginia, 1992.

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Jorgensen, Charles C. Development of a sensor coordinated kinematic model for neural network controller training. Research Institute for Advanced Computer Science, NASA Ames Research Center, 1990.

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Jorgensen, Charles C. Distributed memory approaches for robotic neural controllers. Research Institute for Advanced Computer Science, NASA Ames Research Center, 1990.

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Rylatt, R. Mark. Investigations into controllers for adaptive autonomous agents based on artificial neural networks. De Montfort University, 2001.

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Blake, Joseph. Neural network controllers: Software implementation and a hardware implementation based on a reconfigurable computing application. The Author], 1996.

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1944-, Nguyen Hung T., ed. A first course in fuzzy and neural control. Chapman & Hall/CRC Press, 2003.

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1944-, Nguyen Hung T., ed. A first course in fuzzy and neural control. Chapman & Hall/CRC Press, 2003.

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United States. National Aeronautics and Space Administration., ed. Object-oriented control system design using on-line training of artificial neural networks: Final report, grant no. NAG3-1661, December 01, 1996 - April 30, 1997 ... Howard University/NASA Lewis cooperative research studies. Howard University, College of Engineering, Architecture and Computer Sciences, Electrical Engineering Dept., 1997.

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United States. National Aeronautics and Space Administration., ed. Object-oriented control system design using on-line training of artificial neural networks: Final report, grant no. NAG3-1661, December 01, 1996 - April 30, 1997 ... Howard University/NASA Lewis cooperative research studies. Howard University, College of Engineering, Architecture and Computer Sciences, Electrical Engineering Dept., 1997.

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Book chapters on the topic "Neural network controller"

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Enderle, John D., and Wei Zhou. "Neural Network for the Saccade Controller." In Models of Horizontal Eye Movements, Part II. Springer International Publishing, 2010. http://dx.doi.org/10.1007/978-3-031-01643-1_2.

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Liu, Xiaoming, Yulin Tian, Chunlin Shang, Peizhou Yan, and Lu Wei. "Design of Traffic Signal Controller Based on Network." In Neural Information Processing. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70139-4_65.

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Zhao, Yibiao, Rui Fang, Shun Zhang, and Siwei Luo. "Vague Neural Network Controller and Its Applications." In Artificial Neural Networks – ICANN 2006. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11840817_83.

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García-Lopera, A., A. Díaz Estrella, F. García Oller, and F. Sandoval. "Neural network routing controller for communication parallel multistage interconnection networks." In New Trends in Neural Computation. Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/3-540-56798-4_207.

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Sui, Jianghua, and Guang Ren. "An AND-OR Fuzzy Neural Network Ship Controller Design." In Neural Information Processing. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11893295_68.

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Cai, ManJun, JinCun Liu, GuangJun Tian, XueJian Zhang, and TiHua Wu. "Hybrid Neural Network Controller Using Adaptation Algorithm." In Advances in Neural Networks – ISNN 2007. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-72383-7_19.

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Chen, Tien-Chi, Tsai-Jiun Ren, and Yi-Wei Lou. "Ultrasonic Motor Control Based on Recurrent Fuzzy Neural Network Controller and General Regression Neural Network Controller." In Studies in Computational Intelligence. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-35638-4_19.

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Potočnik, P., and I. Grabec. "Adaptive Self-Tuning Neural-Network-Based Controller." In Computational Intelligence in Systems and Control Design and Applications. Springer Netherlands, 2000. http://dx.doi.org/10.1007/978-94-010-9040-7_4.

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Stein, Kyle, Arash Mahyari, and Eman El-Sheikh. "Vehicle Controller Area Network Inspection Using Recurrent Neural Networks." In Proceedings of the 2023 International Conference on Advances in Computing Research (ACR’23). Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-33743-7_40.

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Araghi, Sahar, Abbas Khosravi, and Douglas Creighton. "Distributed Q-learning Controller for a Multi-Intersection Traffic Network." In Neural Information Processing. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-26532-2_37.

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Conference papers on the topic "Neural network controller"

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Cui, Lin, Jianshan Zhou, Mingqian Wang, Zixuan Xu, Xuting Duan, and Chenghao Ren. "Neural Network Controller for Drones." In 2024 IEEE International Conference on Unmanned Systems (ICUS). IEEE, 2024. https://doi.org/10.1109/icus61736.2024.10839951.

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Qian, Jiajun, Yizhou Ji, Liang Xu, Xiaoqiang Ren, and Xiaofan Wang. "A Neural Network to A Neural Network: A Structured Neural Network Controller Design with Stability and Optimality Guarantees." In 2024 43rd Chinese Control Conference (CCC). IEEE, 2024. http://dx.doi.org/10.23919/ccc63176.2024.10661413.

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Kumar, Swetha R., R. Gunavarshini, S. Nithiyashri, G. S. Priyanka, and R. Rithiga. "Stabilization of Inverted Pendulum using Neural Network Controller." In 2024 10th International Conference on Advanced Computing and Communication Systems (ICACCS). IEEE, 2024. http://dx.doi.org/10.1109/icaccs60874.2024.10717310.

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Kumar, Manish, and Devendra P. Garg. "Neural Network Based Intelligent Learning of Fuzzy Logic Controller Parameters." In ASME 2004 International Mechanical Engineering Congress and Exposition. ASMEDC, 2004. http://dx.doi.org/10.1115/imece2004-59589.

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Design of an efficient fuzzy logic controller involves the optimization of parameters of fuzzy sets and proper choice of rule base. There are several techniques reported in recent literature that use neural network architecture and genetic algorithms to learn and optimize a fuzzy logic controller. This paper presents methodologies to learn and optimize fuzzy logic controller parameters that use learning capabilities of neural network. Concepts of model predictive control (MPC) have been used to obtain optimal signal to train the neural network via backpropagation. The strategies developed have been applied to control an inverted pendulum and results have been compared for two different fuzzy logic controllers developed with the help of neural networks. The first neural network emulates a PD controller, while the second controller is developed based on MPC. The proposed approach can be applied to learn fuzzy logic controller parameter online via the use of dynamic backpropagation. The results show that the Neuro-Fuzzy approaches were able to learn rule base and identify membership function parameters accurately.
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Veloz, Alejandro, Juan C. Romero Quintini, Mónica Parada, and Sergio E. Diaz. "Experimental Testing of a Magnetically Levitated Rotor With a Neural Network Controller." In ASME Turbo Expo 2012: Turbine Technical Conference and Exposition. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/gt2012-69120.

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Magnetic bearings represent a solution for high rotating speeds and sterile environments where lubrication fluids could contaminate. They can also be used in systems where maintenance is difficult or inaccessible, because they don’t require auxiliary lubrication systems and don’t suffer mechanic wear as they work with no contact between rotor and bearing stator. An important part of magnetic bearings is the controller; which is needed to stabilize the system. This controller is generally a PID in which tuning and/or filters design can be complicated for not well known systems. This work presents results of the development of a neural network controller, which is potentially easier to implement, to control the position of a magnetically suspended rotor. The proposed controller is based in the identification of the system inverse model. This is achieved first by implementing a simple PID capable of levitating the rotor, and then some excitations are applied to the rotor in order to acquire data of the position of the rotor and current in the actuators. Current and position data is used to train the artificial neural network for the controller. The controller was implemented in a numerical model and also in an experimental system with a rotor of 1.06kg and 300mm in length. The implementation of SISO, MISO and MIMO neural controllers (both with offline and online training) and a conventional PID with neural network compensation are compared. Structures and architectures of networks are shown. Vibration responses to: a constant force; a controlled impact and a constant acceleration ramp between 0 and 12500rpm are compared. Results in both, numeric model and experimental system, show that neural network controllers are capable of hovering the rotor and control vibrations. Peak-Peak amplitudes vs. rpm plots are similar to a conventional PID. In most cases, the neural network controllers show amplitudes slightly lowers on low frequencies and slightly higher on higher frequencies, except the conventional PID with neural network compensation case, were the system responses as with higher damping. Finally, a discussion is made about future steps in research to improve implementation of a neural controller that is potentially simpler and faster in terms of tuning and with a comparable performance to a conventional magnetic bearing PID controller.
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Pal, Kumaresh, Ashok Kumar Akella, Kumari Namrata, and Subhendu Pati. "Face Detection Using Artificial Neural Network and Wavelet Neural Network." In 2022 International Conference on Intelligent Controller and Computing for Smart Power (ICICCSP). IEEE, 2022. http://dx.doi.org/10.1109/iciccsp53532.2022.9862349.

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Shiotsuka, Toshinari, Kazuo Yoshida, and Akio Nagamatsu. "Vibration Control With Neural Network Dynamic Compensator." In ASME 1995 Design Engineering Technical Conferences collocated with the ASME 1995 15th International Computers in Engineering Conference and the ASME 1995 9th Annual Engineering Database Symposium. American Society of Mechanical Engineers, 1995. http://dx.doi.org/10.1115/detc1995-0600.

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Abstract An approach is presented on designing the dynamic compensator-type controller, using two kinds of neural networks. One is used for identification of system dynamic characteristics of the control object. A time history of response under sine-sweep input is used as the teaching signal of this neural network. The other is used as the neural network controller. The control input is determined with this neural network in order that a performance index concerning the state variable and the input force takes the minimum value. These two neural networks are combined reciprocally in a cascade type in designing the controller. Validity and usefulness of the presented approach are verified by both an computer simulation and an experiment with an active suspension model.
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Wang, Jin, Fuli Wang, Jinliang Zhang, and Jin Zhang. "Intelligent controller using neural network." In International Conference on Intelligent Manufacturing, edited by Shuzi Yang, Ji Zhou, and Cheng-Gang Li. SPIE, 1995. http://dx.doi.org/10.1117/12.217462.

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Leung, T. P., Qi-Jie Zhou, and Hai-Long Pei. "A Robust Neural Network Controller." In 1992 American Control Conference. IEEE, 1992. http://dx.doi.org/10.23919/acc.1992.4792231.

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Mohamed, Benrabah, Kamel Kara, AitSahed Oussama, and Laid Hadjili. "Adaptive Neural Network PID Controller." In 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe). IEEE, 2019. http://dx.doi.org/10.1109/eeeic.2019.8783803.

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Reports on the topic "Neural network controller"

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Braganza, D., D. M. Dawson, I. D. Walker, and N. Nath. Neural Network Grasping Controller for Continuum Robots. Defense Technical Information Center, 2006. http://dx.doi.org/10.21236/ada462583.

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Patro, S., and W. J. Kolarik. Integrated evolutionary computation neural network quality controller for automated systems. Office of Scientific and Technical Information (OSTI), 1999. http://dx.doi.org/10.2172/350895.

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Vitela, J. E., U. R. Hanebutte, and J. Reifman. An artificial neural network controller for intelligent transportation systems applications. Office of Scientific and Technical Information (OSTI), 1996. http://dx.doi.org/10.2172/219376.

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Kirichek, Galina, Vladyslav Harkusha, Artur Timenko, and Nataliia Kulykovska. System for detecting network anomalies using a hybrid of an uncontrolled and controlled neural network. [б. в.], 2020. http://dx.doi.org/10.31812/123456789/3743.

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In this article realization method of attacks and anomalies detection with the use of training of ordinary and attacking packages, respectively. The method that was used to teach an attack on is a combination of an uncontrollable and controlled neural network. In an uncontrolled network, attacks are classified in smaller categories, taking into account their features and using the self- organized map. To manage clusters, a neural network based on back-propagation method used. We use PyBrain as the main framework for designing, developing and learning perceptron data. This framework has a sufficient number of solutions and algorithms for training, designing and testing various types of neural networks. Software architecture is presented using a procedural-object approach. Because there is no need to save intermediate result of the program (after learning entire perceptron is stored in the file), all the progress of learning is stored in the normal files on hard disk.
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Pasupuleti, Murali Krishna. Neural Computation and Learning Theory: Expressivity, Dynamics, and Biologically Inspired AI. National Education Services, 2025. https://doi.org/10.62311/nesx/rriv425.

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Abstract: Neural computation and learning theory provide the foundational principles for understanding how artificial and biological neural networks encode, process, and learn from data. This research explores expressivity, computational dynamics, and biologically inspired AI, focusing on theoretical expressivity limits, infinite-width neural networks, recurrent and spiking neural networks, attractor models, and synaptic plasticity. The study investigates mathematical models of function approximation, kernel methods, dynamical systems, and stability properties to assess the generalization capabilities of deep learning architectures. Additionally, it explores biologically plausible learning mechanisms such as Hebbian learning, spike-timing-dependent plasticity (STDP), and neuromodulation, drawing insights from neuroscience and cognitive computing. The role of spiking neural networks (SNNs) and neuromorphic computing in low-power AI and real-time decision-making is also analyzed, with applications in robotics, brain-computer interfaces, edge AI, and cognitive computing. Case studies highlight the industrial adoption of biologically inspired AI, focusing on adaptive neural controllers, neuromorphic vision, and memory-based architectures. This research underscores the importance of integrating theoretical learning principles with biologically motivated AI models to develop more interpretable, generalizable, and scalable intelligent systems. Keywords Neural computation, learning theory, expressivity, deep learning, recurrent neural networks, spiking neural networks, biologically inspired AI, infinite-width networks, kernel methods, attractor networks, synaptic plasticity, STDP, neuromodulation, cognitive computing, dynamical systems, function approximation, generalization, AI stability, neuromorphic computing, robotics, brain-computer interfaces, edge AI, biologically plausible learning.
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Russell, Chris A., and Glenn F. Wilson. Application of Artificial Neural Networks for Air Traffic Controller Functional State Classification. Defense Technical Information Center, 2001. http://dx.doi.org/10.21236/ada404631.

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Blinov, D. O., A. I. Fomin, and A. A. Chibin. Neural network model for determining the values of the indicator of the effectiveness of the impact of controlled means on air objects. OFERNiO, 2021. http://dx.doi.org/10.12731/ofernio.2021.24804.

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Nikiforov, Vladimir. The use of composite materials in smart medical equipment, including with innovative laser systems, controlled and controlled complexes with elements of artificial intelligence and artificial neural networks. Intellectual Archive, 2019. http://dx.doi.org/10.32370/iaj.2133.

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O'Brien, Beth A., Chee Soon Tan, and Luca Onnis. Technology-based tools for teaching early literacy skills. National Institute of Education, Nanyang Technological University, Singapore, 2024. https://doi.org/10.32658/10497/27453.

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This project focuses on improving literacy development for young learners who are struggling with learning to read English by investigating the process of learning grapheme-phoneme correspondences (GPCs). Learning GPC is foundational to learning to read alphabetic languages, and is a core problem for struggling readers. In this project, two methods are used in two studies to understand the process of learning English GPCs as the crux of acquiring literacy. First, a machine learning neural network modelling approach is used to study the effect of sound-symbol grain size and consistency and training input on learning progression and outcomes. Second, a behavioural randomized controlled study is conducted to examine the effects of interventions with LSP students focused at different grain sizes. Between these two studies, information about the types of input that may yield most effective learning is corroborated.
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Panta, Manisha, Md Tamjidul Hoque, Kendall Niles, Joe Tom, Mahdi Abdelguerfi, and Maik Flanagin. Deep learning approach for accurate segmentation of sand boils in levee systems. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/49460.

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Sand boils can contribute to the liquefaction of a portion of the levee, leading to levee failure. Accurately detecting and segmenting sand boils is crucial for effectively monitoring and maintaining levee systems. This paper presents SandBoilNet, a fully convolutional neural network with skip connections designed for accurate pixel-level classification or semantic segmentation of sand boils from images in levee systems. In this study, we explore the use of transfer learning for fast training and detecting sand boils through semantic segmentation. By utilizing a pretrained CNN model with ResNet50V2 architecture, our algorithm effectively leverages learned features for precise detection. We hypothesize that controlled feature extraction using a deeper pretrained CNN model can selectively generate the most relevant feature maps adapting to the domain, thereby improving performance. Experimental results demonstrate that SandBoilNet outperforms state-of-the-art semantic segmentation methods in accurately detecting sand boils, achieving a Balanced Accuracy (BA) of 85.52%, Macro F1-score (MaF1) of 73.12%, and an Intersection over Union (IoU) of 57.43% specifically for sand boils. This proposed approach represents a novel and effective solution for accurately detecting and segmenting sand boils from levee images toward automating the monitoring and maintenance of levee infrastructure.
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