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Relevant bibliographies by topics / Model based adaptive controller

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Academic literature on the topic 'Model based adaptive controller'

Author: Grafiati

Published: 5 June 2025

Last updated: 15 July 2025

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Journal articles on the topic "Model based adaptive controller"

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Hassan, Aliaa Adnan Flaih, Ekhlas Hameed Karam, and Muaayed F. Al-Rawi. "Model based adaptive controller with grasshopper optimization algorithm for upper-limb rehabilitation robot." Indonesian Journal of Electrical Engineering and Computer Science 26, no. 2 (2022): 723–31. https://doi.org/10.11591/ijeecs.v26.i2.pp723-731.

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Model based adaptive controllers (MBACs) are considered one of the most common adaptive controllers that are used with robotic systems due to their ensuring nonlinear robust scheme with global asymptotic stability for controlling nonlinear systems. However, this controller requires precise mathematical models of the controlled systems. In this paper, an optimal model-based adaptive controller (OMBAC) is suggested for controlling a two-link upper limb rehabilitation robot. This controller, in the presence of model uncertainties, can guarantee the robustness of the rehabilitation robot. Although

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Adnan, Aliaa, Ekhlas H. Karam, and Muaayed F. Al-Rawi. "Model based adaptive controller with grasshopper optimization algorithm for upper-limb rehabilitation robot." Indonesian Journal of Electrical Engineering and Computer Science 26, no. 2 (2022): 723. http://dx.doi.org/10.11591/ijeecs.v26.i2.pp723-731.

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<span>Model based adaptive controllers (MBACs) are considered one of the most common adaptive controllers that are used with robotic systems due to their ensuring nonlinear robust scheme with global asymptotic stability for controlling nonlinear systems. However, this controller requires precise mathematical models of the controlled systems. In this paper, an optimal model-based adaptive controller (OMBAC) is suggested for controlling a two-link upper limb rehabilitation robot. This controller, in the presence of model uncertainties, can guarantee the robustness of the rehabilitation rob

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Yang, Xinhao, and Ze Li. "Congestion Control Based on Multiple Model Adaptive Control." Mathematical Problems in Engineering 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/714320.

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The congestion controller based on the multiple model adaptive control is designed for the network congestion in TCP/AQM network. As the conventional congestion control is sensitive to the variable network condition, the adaptive control method is adopted in our congestion control. The multiple model adaptive control is introduced in this paper based on the weight calculation instead of the parameter estimation in past adaptive control. The model set is composed by the dynamic model based on the fluid flow. And three “local” congestion controllers are nonlinear output feedback controller based

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Zuo, Yuefei, Shushu Zhu, Yebing Cui, Chuang Liu, and Xiaogang Lin. "Adaptive PI Controller for Speed Control of Electric Drives Based on Model Reference Adaptive Identification." Electronics 13, no. 6 (2024): 1067. http://dx.doi.org/10.3390/electronics13061067.

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In this paper, to achieve auto-setting of PI controller gains when mechanical parameters are unknown, two adaptive PI controllers for speed control of electric drives are developed based on model reference adaptive identification. The adaptive linear neuron (ADALINE) neural network is used to interpret the proposed adaptive PI controller. The effect of the low-pass filter used for the feedback speed and the Coulomb friction torque on parameter identification is analysed, and a new motion equation using filtered speed is given. Additionally, a parameter identification method based on unipolar s

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LEE, T. H., J. X. XU, and M. WANG. "A model-based adaptive sliding controller." International Journal of Systems Science 27, no. 1 (1996): 129–40. http://dx.doi.org/10.1080/00207729608929195.

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Mirwald, Jonas, Johannes Ultsch, Ricardo de Castro, and Jonathan Brembeck. "Learning-Based Cooperative Adaptive Cruise Control." Actuators 10, no. 11 (2021): 286. http://dx.doi.org/10.3390/act10110286.

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Traffic congestion and the occurrence of traffic accidents are problems that can be mitigated by applying cooperative adaptive cruise control (CACC). In this work, we used deep reinforcement learning for CACC and assessed its potential to outperform model-based methods. The trade-off between distance-error minimization and energy consumption minimization whilst still ensuring operational safety was investigated. Alongside a string stability condition, robustness against burst errors in communication also was incorporated, and the effect of preview information was assessed. The controllers were

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Li, Xian-Sheng, Yuan-Yuan Ren, and Xue-Lian Zheng. "Model-Free Adaptive Control for Tank Truck Rollover Stabilization." Mathematical Problems in Engineering 2021 (August 20, 2021): 1–16. http://dx.doi.org/10.1155/2021/8417071.

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Influenced by lateral liquid sloshing in partially filled tanks, tank vehicles are apt to encounter with rollover accidents. Due to its strong nonlinearity and loading state uncertainty, it has great challenges in tank vehicle active control. Based on the model-free adaptive control (MFAC) theory, the roll stability control problem of tank trucks with different tank shapes and liquid fill percentages is explored. First, tank trucks equipped with cylinder or elliptical cylinder tanks are modelled, and vehicle dynamics is analyzed. This dynamic model is used to provide I/O data in the controlled

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Chou, Sidney. "Controller Tuning Based on Stochastic Control Theory." Journal of Dynamic Systems, Measurement, and Control 110, no. 1 (1988): 100–104. http://dx.doi.org/10.1115/1.3152638.

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A practical controller tuning method is proposed for selecting controller gains in the face of design difficulties such as poor repeatability, long delay, nonlinearity, conflicting control objectives, model inaccuracy, and system complexity. Unlike many adaptive schemes striving to acquire knowledge about the system being controlled, the proposed approach is aimed at designing nonadaptive, or at best, gain scheduling controllers in a quantitative, systematic way while meeting design specifications.

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Humaidi, Amjad, and Mustafa Hameed. "Development of a New Adaptive Backstepping Control Design for a Non-Strict and Under-Actuated System Based on a PSO Tuner." Information 10, no. 2 (2019): 38. http://dx.doi.org/10.3390/info10020038.

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In this work, a new adaptive block-backstepping control design algorithm was developed for an under-actuated model (represented by a ball–arc system) to enhance the transient and steady-state behaviors and to improve the robustness characteristics of the controlled system against parameter variation (load change and model uncertainty). For this system, the main mission of the proposed controller is to simultaneously hold the ball at the top of the arc and retain the cart at the required position. The stability of a controlled system based on the proposed adaptive controller was analyzed, and i

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Kasparian, Vicken, and Celal Batur. "Model reference based neural network adaptive controller." ISA Transactions 37, no. 1 (1998): 21–39. http://dx.doi.org/10.1016/s0019-0578(98)00002-0.

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Dissertations / Theses on the topic "Model based adaptive controller"

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Wu, Yue. "The design and application of a new type of adaptive fuzzy model-based controller." Thesis, University of Oxford, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.432574.

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Couch, Jeremy Robert. "An ECMS-Based Controller for the Electrical System of a Passenger Vehicle." The Ohio State University, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=osu1366076350.

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Shamsudin, Syariful Syafiq. "The Development of Neural Network Based System Identification and Adaptive Flight Control for an AutonomousHelicopter System." Thesis, University of Canterbury. Mechanical Engineering Department, 2013. http://hdl.handle.net/10092/8803.

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This thesis presents the development of self adaptive flight controller for an unmanned helicopter system under hovering manoeuvre. The neural network (NN) based model predictive control (MPC) approach is utilised in this work. We use this controller due to its ability to handle system constraints and the time varying nature of the helicopter dynamics. The non-linear NN based MPC controller is known to produce slow solution convergence due to high computation demand in the optimisation process. To solve this problem, the automatic flight controller system is designed using the NN based approxi

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Sudakar, Madhavan. "Novel control techniques for a quadrotor based on the Sliding Mode Controller." University of Cincinnati / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1613746605628363.

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Yung, K. L. "Microprocessor based non-linear adaptive controller." Thesis, University of Plymouth, 1985. http://hdl.handle.net/10026.1/2492.

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The advent of microprocessors has created the possibility of developing low cost adaptive controllers for small process plants which in the past badly needed but could not afford such controllers. To examine the practicality of developing advanced low cost microprocessor based controller, this thesis describes the development of a non-linear adaptive controller for a nylon crimping plant which is a typical example of small process plants. In order to test the algorithm on site, an algorithm development/implement device basing on a novel multi-tasking concept was developed. This novel microproc

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Griesebner, Klaus. "Model-based Controller Development." Thesis, Högskolan i Halmstad, Akademin för informationsteknologi, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-34929.

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Model-based design is a powerful design technique for embedded system development. The technique enables virtual prototyping to develop and debug controllers before touching real hardware. There are many tools available covering the distinct steps of the design cycle including modeling, simulation, and implementation. Unfortunately, none of them covers all three steps. This thesis proposes a formalism coupling the model and the implementation of a controller for equation-based simulation tools. The resulting formalism translates defined controller models to platform specific code using a defin

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Patkar, Abhishek. "Adaptive neural controller based on convex parametrization." Thesis, Massachusetts Institute of Technology, 2020. https://hdl.handle.net/1721.1/128972.

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Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, September, 2020<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 65-67).<br>The problem of control of a class of nonlinear plants has been addressed by using neural networks together with sliding mode control to lead to global boundedness. We revisit this problem in this thesis and suggest a specific class of neural networks that employ convex activation functions. By using the algorithms that have been proposed previously for adaptive control in th

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Marchant, Andrew Nicholas. "An adaptive knowledge based controller for refrigerated potato stores." Thesis, University of Exeter, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.393347.

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Rapley, Veronica Elizabeth. "Model-based adaptive cluster sampling." Thesis, University of Southampton, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.433939.

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Logan, Beth Teresa. "Adaptive model-based speech enhancement." Thesis, University of Cambridge, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.625004.

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Books on the topic "Model based adaptive controller"

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J, Merhav Shmuel, and Ames Research Center, eds. Performance characteristics of an adaptive controller based on least-mean-square filters. National Aeronautics and Space Administration, Ames Research Center, 1987.

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Zhen-Lei, Zhou, and Goddard Space Flight Center, eds. Position control of redundant manipulators using an adaptive error-based control scheme. Catholic University of America, Dept. of Electrical Engineering, 1990.

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Zhen-Lei, Zhou, and Goddard Space Flight Center, eds. Position control of redundant manipulators using an adaptive error-based control scheme. Catholic University of America, Dept. of Electrical Engineering, 1990.

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Vedage, Vishwanath. A personal computer based adaptive controller for heat exchangers. University of East London, 1997.

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Basten, Twan, Roelof Hamberg, Frans Reckers, and Jacques Verriet, eds. Model-Based Design of Adaptive Embedded Systems. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-4821-1.

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Zourntos, Takis. Nonlinear adaptive control based on the related model. National Library of Canada, 1996.

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Melvin, James E. AUV fault detection using model based observer residuals. Naval Postgraduate School, 1998.

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MacDonald, Gordon S. Model based design and verification of a rapid dive controller for an Autonomous Underwater Vehicle. Naval Postgraduate School, 1989.

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Guerquin, Jan. The development of an IC engine model-based fuel injection controller with fuel film compensation. National Library of Canada, 2003.

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United States. National Aeronautics and Space Administration., ed. Command operator tracker based direct model reference adaptive control of a Puma 560 manipulator. Center for Intelligent Robotic Systems for Space Exploration, Rensselaer Polytechnic Institute, 1992.

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Book chapters on the topic "Model based adaptive controller"

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Graham, Bruce, and Robert Newell. "An adaptive fuzzy model-based controller." In Fuzzy Logic and Fuzzy Control. Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/3-540-58279-7_19.

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Liu, Xia, Li Li, and Qiyan Yan. "Research on PID Controller Based on Adaptive Internal Model Control." In Lecture Notes in Electrical Engineering. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-3648-5_155.

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Sun, Jiayue, Shun Xu, Yang Liu, and Huaguang Zhang. "Neural Networks-Based Immune Optimization Regulation Using Adaptive Dynamic Programming." In Adaptive Dynamic Programming. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-5929-7_2.

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AbstractThis chapter investigates optimal regulation scheme between tumor and immune cells based on ADP approach. The therapeutic goal is to inhibit the growth of tumor cells to allowable injury degree, and maximize the number of immune cells in the meantime. The reliable controller is derived through the ADP approach to make the number of cells achieve the specific ideal states. Firstly, the main objective is to weaken the negative effect caused by chemotherapy and immunotherapy, which means that minimal dose of chemotherapeutic and immunotherapeutic drugs can be operational in the treatment process. Secondly, according to nonlinear dynamical mathematical model of tumor cells, chemotherapy and immunotherapeutic drugs can act as powerful regulatory measures, which is a closed-loop control behavior. Finally, states of the system and critic weight errors are proved to be ultimately uniformly bounded with the appropriate optimization control strategy and the simulation results are shown to demonstrate effectiveness of the cybernetics methodology.

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Putratama, Muhammad Andy, Rémy Rigo-Mariani, Vincent Debusschere, and Yvon Bésanger. "Uncertainties Impact and Mitigation with an Adaptive Model-Based Voltage Controller." In Lecture Notes in Electrical Engineering. Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-55696-8_12.

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Seong, Junyeong, Sungjun Park, and Kunsoo Huh. "Robust Lane Keeping Control with Estimation of Cornering Stiffness and Model Uncertainty." In Lecture Notes in Mechanical Engineering. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-70392-8_39.

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AbstractThis paper introduces an adaptive lane-keeping control strategy that adapts to varying cornering stiffness while ensuring robustness against uncertainties. The system consists of three blocks: an Interacting Multiple Model (IMM) cornering stiffness estimator, a cornering stiffness uncertainty estimator, and a Robust Model Predictive Controller (RMPC). Improvements in estimation accuracy are achieved through a novel IMM probability derivation method, and the uncertainty estimator utilizes the IMM probability matrix to obtain reliable uncertainty boundaries. Real-time cornering stiffness estimations are integrated into the RMPC for adaptive model predictions. Uncertainty boundaries provide robustness against estimation error in the RMPC by constraint tightening and smoothing techniques. The performance of the estimator and controller is validated in simulations, where the overall control performance is compared to that of the Model Predictive Control (MPC) based on static cornering stiffness.

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Yu, Li, Xiaoxiong Liu, Weiguo Zhang, and Ming Ruichen. "Robust Flight Controller Design Based on Adaptive Nonlinear Dynamic Inverse with Reference Model." In Lecture Notes in Electrical Engineering. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8155-7_81.

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Mohapatra, Gayatri, and Manoj Kumar Debnath. "A Novel Fuzzy-Based Model Predictive Adaptive Controller for a PMSG Wind Turbine." In Lecture Notes in Electrical Engineering. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-7076-3_7.

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Xu, Zhen, Mingchu Xu, and Qingwei Chen. "Adaptive Sliding Model Controller Design of Carlike Robot Speed and Steering Angle Based on Characteristic Model." In Proceedings of the 11th International Conference on Modelling, Identification and Control (ICMIC2019). Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0474-7_80.

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Sathyamangalam Imran, Mohammed Irshadh Ismaaeel, Satyesh Shanker Awasthi, Michael Khayyat, Stefano Arrigoni, and Francesco Braghin. "A Rule-Defined Adaptive MPC Based Motion Planner for Autonomous Driving Applications." In Lecture Notes in Mechanical Engineering. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-70392-8_77.

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AbstractIn autonomous driving systems, motion planning to reach a given destination while avoiding obstacles becomes a task entirely managed by the on-board unit. In this work, we present a rule-defined motion planning algorithm for autonomous driving applications based on an adaptive Model Predictive Controller (MPC) framework. The motion planning task is first formulated as an Optimal Control Problem (OCP) subject to time-varying Control Barrier Function (CBF) constraints. It is then integrated within an MPC framework with adaptive weights settings, enabling the algorithm to dynamically adjust the MPC weights according to the rule-defined driving scenarios. The developed motion planner generates optimized trajectories for a high-fidelity Autonomous Vehicle (AV) model within IPG CarMaker software. Simulations performed showed that the developed motion planner adeptly facilitates successful overtaking, following, and stopping of the AV behind the Obstacle Vehicle (OV) based on rule-defined scenarios perceived by the AV.

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Gómez, Josué, Chidentree Treesatayapun, and América Morales. "Free Model Task Space Controller Based on Adaptive Gain for Robot Manipulator Using Jacobian Estimation." In Advances in Computational Intelligence. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-04497-8_22.

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Conference papers on the topic "Model based adaptive controller"

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Picardi, Giacomo, Lorenzo Pollini, Stefano Geluardi, Mario Olivari, Heinrich Buelthoff, and Mario Innocenti. "L1-based Model Following Control of an Identified Helicopter Model in Hover." In Vertical Flight Society 72nd Annual Forum & Technology Display. The Vertical Flight Society, 2016. http://dx.doi.org/10.4050/f-0072-2016-11466.

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The aim of this study is to augment the uncertain dynamics of the helicopter in order to resemble the dynamics of a new kind of vehicle, the so called Personal Aerial Vehicle. To achieve this goal a two step procedure is proposed. First, the helicopter model dynamics is augmented with a PID-based dynamic controller. Such controller implements a model following on the nominal helicopter model without uncertainties. Then, anL1 adaptive controller is designed to restore the nominal responses of the augmented helicopter when variations in the identified parameters are considered. The performance o

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Xu, Chengtao, Zhaowu Ping, Yuqian He, Yunzhi Huang, and Jun-Guo Lu. "An internal model based adaptive controller design for robot manipulator." In 2024 43rd Chinese Control Conference (CCC). IEEE, 2024. http://dx.doi.org/10.23919/ccc63176.2024.10662224.

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Chu, Kenny Sau Kang, KuewWai Chew, Yoong Choon Chang, Stella Morris, and Kein Huat Chua. "Adaptive Direct Current Motor Proportional Integral Derivative Controller based on Deep Learning Model." In 2024 3rd Asian Conference on Frontiers of Power and Energy (ACFPE). IEEE, 2024. https://doi.org/10.1109/acfpe63443.2024.10801042.

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Zeng, Fuchuan, Xuejian Zhang, Hang Li, and Xiaobing Hu. "Neural Network-Based Adaptive Optimal Terminal Sliding Model Controller for Robot Manipulator Trajectory Tracking." In 2024 8th International Conference on Automation, Control and Robots (ICACR). IEEE, 2024. https://doi.org/10.1109/icacr62205.2024.11053746.

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Ding-lei, Wang. "Model Reference Based Neural Network Adaptive Controller." In 2008 IEEE International Symposium on Knowledge Acquisition and Modeling Workshop (KAM 2008 Workshop). IEEE, 2008. http://dx.doi.org/10.1109/kamw.2008.4810600.

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Amini, Mohammad Reza, Mahdi Shahbakhti, Selina Pan, and J. Karl Hedrick. "Handling Model and Implementation Uncertainties via an Adaptive Discrete Sliding Mode Controller Design." In ASME 2016 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/dscc2016-9732.

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Analog-to-digital conversion (ADC) and uncertainties in modeling the plant dynamics are the main sources of imprecisions in the design cycle of model-based controllers. These implementation and model uncertainties should be addressed in the early stages of the controller design, otherwise they could lead to failure in the controller performance and consequently increase the time and cost required for completing the controller verification and validation (V&V) with more iterative loops. In this paper, a new control approach is developed based on a nonlinear discrete sliding mode controller

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Pham, Hoang Anh, and Dirk Söffker. "Modified Model-Free Adaptive Control Method Applied to Vibration Control of an Elastic Crane." In ASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/detc2019-97654.

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Abstract Model-free adaptive control (MFAC) is a data-driven control approach receiving increased attention in the last years. Different model-free-based control strategies are proposed to design adaptive controllers when mathematical models of the controlled systems should not be used or are not available. Using only measurements (I/O data) from the system, a feedback controller is generated without the need of any structural information about the controlled plant. In this contribution an improved MFAC is discussed for control of unknown multivariable flexible systems. The main improvement in

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Abdelhameed, Magdy Mohamed, Unat Pinson, and Sabri Cetinkunt. "Adaptive Learning Algorithm for Cerebellar Model Articulation Controller: Neural Network Based Hybrid-Type Controller—Part II." In ASME 2000 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2000. http://dx.doi.org/10.1115/imece2000-2201.

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Abstract Cerebellar model articulation controller (CMAC) is a useful neural network learning technique. It was developed two decades ago but yet lacks adequate learning algorithm especially when it is used in a hybrid-type controller. Part I of this work was devoted to introduce a new CMAC adaptive learning algorithm. Part II will be directed to experimental application of new learning algorithm of a CMAC based hybrid-type real time controller. The proposed controller is applied for the trajectory tracking of a piezoelectric actuated tool post. It has been proven that the piezoelectric actuate

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Abdelrauf, Ahmed A., M. Abdel-Geliel, and E. Zakzouk. "Adaptive PID controller based on model predictive control." In 2016 European Control Conference (ECC). IEEE, 2016. http://dx.doi.org/10.1109/ecc.2016.7810378.

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Gadsden, S. Andrew. "An Adaptive PID Controller Based on Bayesian Theory." In ASME 2017 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/dscc2017-5340.

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One of the most popular trajectory-tracking controllers used in industry is the PID controller. The PID controller utilizes three types of gains and the tracking error in order to provide a control gain to a system. The PID gains may be tuned manually or using a number of different techniques. Under most operating conditions, only one set of PID gains are used. However, techniques exist to compensate for dynamic systems such as gain scheduling or basic time-varying functions. In this paper, an adaptive PID controller is presented based on Bayesian theory. The interacting multiple model (IMM) m

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Reports on the topic "Model based adaptive controller"

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Mayo, Jackson, Karla Morris Wright, Jon Aytac, et al. Demonstration of Model-Based Design for Digital Controller Using Formal Methods. Office of Scientific and Technical Information (OSTI), 2024. http://dx.doi.org/10.2172/2430067.

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Garris, Michael D. Component-based handprint segmentation using adaptive writing style model. National Institute of Standards and Technology, 1996. http://dx.doi.org/10.6028/nist.ir.5843.

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Basher, A. M. H. Development of a Robust Model-Based Water Level Controller for U-Tube Steam Generator. Office of Scientific and Technical Information (OSTI), 2001. http://dx.doi.org/10.2172/814425.

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Cui, Xiaohui, and Thomas E. Potok. Particle Swarm Social Adaptive Model for Multi-Agent Based Insurgency Warfare Simulation. Office of Scientific and Technical Information (OSTI), 2009. http://dx.doi.org/10.2172/984372.

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Rangaswamy, Muralidhar. Parametric and Model Based Adaptive Detection Algorithms for Non-Gaussian Interference Backgrounds. Defense Technical Information Center, 1999. http://dx.doi.org/10.21236/ada369457.

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Eguchi, Hiroaki, Takanori Fukao, and Koichi Osuka. Design Method of Reference Model for Active Steering Based on Nonlinear Adaptive D* Control. SAE International, 2005. http://dx.doi.org/10.4271/2005-08-0423.

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Hand, M. M. Variable-Speed Wind Turbine Controller Systematic Design Methodology: A Comparison of Non-Linear and Linear Model-Based Designs. Office of Scientific and Technical Information (OSTI), 1999. http://dx.doi.org/10.2172/12172.

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Osadchyi, Viacheslav V., Hanna Y. Chemerys, Kateryna P. Osadcha, Vladyslav S. Kruhlyk, Serhii L. Koniukhov, and Arnold E. Kiv. Conceptual model of learning based on the combined capabilities of augmented and virtual reality technologies with adaptive learning systems. [б. в.], 2020. http://dx.doi.org/10.31812/123456789/4417.

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The article is devoted to actual problem of using modern ICT tools to increase the level of efficiency of the educational process. The current state and relevance of the use of augmented reality (AR) and virtual reality (VR) technologies as an appropriate means of improving the educational process are considered. In particular, attention is paid to the potential of the combined capabilities of AR and VR technologies with adaptive learning systems. Insufficient elaboration of cross-use opportunities for achieving of efficiency of the educational process in state-of-the-art research has been ide

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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 capa

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Sikora, Yaroslava B., Olena Yu Usata, Oleksandr O. Mosiiuk, Dmytrii S. Verbivskyi, and Ekaterina O. Shmeltser. Approaches to the choice of tools for adaptive learning based on highlighted selection criteria. [б. в.], 2021. http://dx.doi.org/10.31812/123456789/4447.

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The article substantiates the relevance of adaptive learning of students in the modern information society, reveals the essence of such concepts as “adaptability” and “adaptive learning system”. It is determined that a necessary condition for adaptive education is the criterion of an adaptive learning environment that provides opportunities for advanced education, development of key competencies, formation of a flexible personality that is able to respond to different changes, effectively solve different problems and achieve results. The authors focus on the technical aspect of adaptive learni

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