Article
Computer Science, Artificial Intelligence
Shiqi Zheng, Peng Shi, Shuoyu Wang, Yan Shi
Summary: This article studies the adaptive neural controller design for uncertain multiagent systems, utilizing neural networks to approximate unknown nonlinearities, constructing new Lyapunov functions to guarantee the conditions of NNs, and proposing new adaptive neural PI-type controllers. Illustrative examples demonstrate the advantages of the obtained results.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2021)
Article
Sport Sciences
Mark Connor, Marco Beato, Michael O'Neill
Summary: The planning and control of team sport training activities are crucial for athletic development and team performance. This research introduces a novel system that utilizes control system theory and artificial intelligence techniques to construct optimal future training plans, addressing unexpected disturbances and deviations from the training plan goal. The study demonstrates that an intelligent feedback controller outperforms random and proportional controllers in reducing deviations from the training plan goal.
JOURNAL OF SCIENCE AND MEDICINE IN SPORT
(2022)
Article
Computer Science, Artificial Intelligence
Dapeng Li, Hong-Gui Han, Jun-Fei Qiao
Summary: This study develops an adaptive neural controller for nonlinear strict-feedback systems subject to state-dependent constraint boundaries. The controller employs nonlinear state-dependent mapping and a radial basis function neural network to estimate unknown system dynamics. The Nussbaum gain technique is integrated into the controller design to remove the effect of unknown control direction. Based on Lyapunov analysis, the developed control strategy ensures bounded closed-loop signals and achieves constraints on system states and tracking error.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Xinglan Liu, Bin Xu, Yingxin Shou, Quan-Yong Fan, Yingxue Chen
Summary: This article focuses on event-based collaborative design for strict-feedback systems with uncertain nonlinearities, proposing a controller design method based on neural network weight adaptive law and updating the controller and NN weights adaptive law at triggering instants determined by a novel composite triggering threshold. By integrating state-model error, the requirements of system information and allowable range of event-triggering error are relaxed, reducing the number of triggering instants significantly while maintaining system performance. The stability of the closed-loop system is proven using the Lyapunov method at time intervals and sampling instants, with simulation results demonstrating the effectiveness of the proposed scheme.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Automation & Control Systems
Miroslav B. Milovanovic, Dragan S. Antic, Marko T. Milojkovic, Miodrag D. Spasic
Summary: A new intelligent hybrid controller based on orthogonal endocrine neural network and orthogonal endocrine ANFIS is proposed in this article. Experimental results show that the proposed controller outperforms other control algorithms in terms of online control performance.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Computer Science, Artificial Intelligence
Miaomiao Gao, Lijian Ding, Xiaozheng Jin
Summary: This article addresses the problem of fast fixed-time tracking control for robotic manipulator systems subject to model uncertainties and disturbances. A novel faster nonsingular fixed-time sliding mode (FNFTSM) surface is developed to ensure faster convergence rate, and an extreme learning machine (ELM) algorithm is utilized to suppress the negative influence of system uncertainties and disturbances. The proposed control scheme combines fixed-time stable theory and the ELM learning technique, enabling adaptive fixed-time sliding mode control without knowing any information of system parameters.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Information Systems
K. U. Jaseena, Binsu C. Kovoor
Summary: Weather forecasting is the practice of predicting the state of the atmosphere based on different weather parameters. Accurate weather forecasts are crucial in various fields. With the advancement of atmospheric observing systems and the increasing volume of weather data, deep learning techniques are being used to improve weather prediction. This paper provides a comprehensive review of weather forecasting approaches and discusses potential future research directions.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Engineering, Electrical & Electronic
Zixiao Ma, Zhaoyu Wang, Yifei Guo, Yuxuan Yuan, Hao Chen
Summary: This article introduces a model-free secondary voltage control method for microgrids using nonlinear multiple models adaptive control. It combines a linear robust adaptive controller and a nonlinear adaptive controller with a switching mechanism to ensure closed-loop stability and accurate voltage tracking, showing good robustness and ease of deployment.
IEEE TRANSACTIONS ON SMART GRID
(2021)
Article
Computer Science, Artificial Intelligence
Hao Fu, Xin Chen, Wei Wang, Min Wu
Summary: This article focuses on the optimal synchronization problem for discrete-time nonlinear heterogeneous multiagent systems with an active leader. The author proposes an observer-based adaptive synchronization control approach to overcome the difficulty in deriving optimal control protocols for these systems. Through convergence analysis, the effectiveness of the approach is demonstrated and verified using a numerical example.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Rohollah Moghadam, Sarangapani Jagannathan
Summary: This article introduces an actor-critic neural network-based online optimal adaptive regulation method for a class of nonlinear continuous-time systems. The method considers known state and input delays, as well as uncertain system dynamics. The temporal difference error (TDE) dependent on state and input delays is derived using actual and estimated value function and reinforcement learning. The critic neural network (NN) weights are tuned at each sampling instant based on the instantaneous integral TDE. A novel identifier is used to estimate the control coefficient matrices and obtain the estimated control policy. The boundedness of various components in the system is proved through Lyapunov analysis, and simulation results demonstrate the effectiveness of the proposed approach.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Information Systems
Jiahui Wang, Yabin Gao, Yifan Liu, Jianxing Liu, Guanghui Sun, Ligang Wu
Summary: This paper explores the dynamic practical-sliding-mode control and estimation of unknown functions for singular Markovian jump systems with system perturbations. It proposes an intelligent dynamic practical sliding mode control method based on ellipsoidal-type interval type-2 fuzzy neural networks, and proves its stability and performance.
INFORMATION SCIENCES
(2022)
Article
Automation & Control Systems
Praveen Kumar Muthusamy, Matthew Garratt, Hemanshu Pota, Rajkumar Muthusamy
Summary: A novel bidirectional fuzzy brain emotional learning controller is proposed for controlling quadcopter unmanned aerial vehicles. The controller achieves accurate trajectory tracking and real-time handling of payload uncertainties and disturbances through a simplified fuzzy neural network structure and adaptive bidirectional brain emotional learning algorithm.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2022)
Article
Engineering, Electrical & Electronic
Mohammad Babaie, Kamal Al-Haddad
Summary: This article explores the impact of intelligent predictive multiobjective control (IPMOC) on power quality, power ancillary services, efficiency, and reliability in grid-tied transformerless multilevel converters. The article also presents the development of the IPMOC concept in terms of training, autonomous power management, and real-time harmonic mitigation. The proposed IPMOC utilizes model predictive control (MPC) and artificial neural networks to handle the multiobjective task effectively.
IEEE TRANSACTIONS ON POWER ELECTRONICS
(2023)
Article
Chemistry, Multidisciplinary
Dani S. Assi, Hongli Huang, Vaithinathan Karthikeyan, Vaskuri C. S. Theja, Maria Merlyne de Souza, Ning Xi, Wen Jung Li, Vellaisamy A. L. Roy
Summary: Neuromorphic artificial intelligence systems are the future of ultrahigh-performance computing clusters. Quantum topological neuristors (QTN) with low energy consumption and higher switching speed are introduced to mimic the synapses of mammalian brains. With improved design, QTNs demonstrate top-notch neuromorphic behavior and can be interfaced with artificial neural networks for decision-making operations.
Article
Computer Science, Artificial Intelligence
Chengjie Huang, Zhi Liu, C. L. Philip Chen, Yun Zhang
Summary: In this article, the problem of adaptive fixed-time tracking control for multiagent systems (MASs) with mismatched uncertainty is considered. A new adaptive consensus control criterion is proposed, which includes the design of Lyapunov functions and tuning functions. The use of radial basis function neural networks and direct adaptive strategy improves the stability and performance of the MASs.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Engineering, Chemical
Majdi Al-Mahasneh, Fahad Alkoaik, Ahmed Khalil, Ahmad Al-Mahasneh, Ahmed El-Waziry, Ronnel Fulleros, Taha Rababah
JOURNAL OF FOOD PROCESS ENGINEERING
(2014)
Article
Automation & Control Systems
Ahmad Jobran Al-Mahasneh, Sreenatha G. Anavatti, Matthew A. Garratt, Mahardhika Pratama
Summary: The paper proposes a novel controller design combining generalized regression neural networks (GRNNs) and sliding mode control (SMC) for controlling nonlinear multi-input and multi-output (MIMO) dynamic systems. The design transforms GRNN into an online adaptive controller with low computational complexity, strong stability, and no need for pretraining. Performance is verified with various systems and compared with a standard PID controller, while stability is confirmed using the Lyapunov stability method.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2021)
Article
Automation & Control Systems
Ahmad Jobran Al-Mahasneh, Sreenatha G. Anavatti
Summary: In this article, a novel version of the GRNN called Imp_GRNN is developed for controlling MIMO nonlinear DT systems. The improvements include setting weights using recursive statistical means, introducing a new output layer and adaptable connections, and suggesting an interval-type smoothing parameter. Experimental results show that Imp_GRNN outperforms other methods in terms of control accuracy.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Computer Science, Information Systems
Ahmad Jobran Al-Mahasneh, Sreenatha G. Anavatti, Matthew A. Garratt
Summary: A new model-free Model-Actor (MA) reinforcement learning controller is developed for output feedback control of discrete-time systems with input saturation constraints. It consists of two neural networks, a model-network and an actor network, and can control the systems without prior knowledge of their dynamics.
Proceedings Paper
Computer Science, Artificial Intelligence
Ahmad Jobran Al-Mahasneh, Sreenatha G. Anavatti, Matthew A. Garratt, Mahardhika Pratama
2018 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (IEEE SSCI)
(2018)
Proceedings Paper
Computer Science, Artificial Intelligence
Ahmad Jobran Al-Mahasneh, Sreenatha G. Anavatti, Matthew A. Garratt
2017 INTERNATIONAL CONFERENCE ON ADVANCED MECHATRONICS, INTELLIGENT MANUFACTURE, AND INDUSTRIAL AUTOMATION (ICAMIMIA)
(2017)
Proceedings Paper
Computer Science, Artificial Intelligence
Ahmad Jobran Al-Mahasneh, Sreenatha G. Anavatti, Matthew A. Garratt
2017 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (SSCI)
(2017)
Proceedings Paper
Computer Science, Artificial Intelligence
Ahmad Jobran A-Mahasneh, S. G. Anavatti, M. Garratt
2017 NINTH INTERNATIONAL CONFERENCE ON ADVANCED COMPUTATIONAL INTELLIGENCE (ICACI)
(2017)