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Visual feedback vibration control of flexible hinged plate system based on reinforcement learning algorithm

MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2025年 224卷

作者： Qiu, Zhi-cheng Liu, Yi-hong South China Univ Technol Sch Mech & Automot Engn Guangzhou 510641 Peoples R China

In order to simulate the bending and torsional coupled vibration of solar panels under external disturbances, an experimental system of a fixed flexible hinged plate is established. The dynamic model is established by finite element method (FEM), and the model parameters are accurately identified by wavelet transform and simulated annealing (SA) optimization method. Two laser displacement sensors and binocular vision are combined to detect the bending and torsional vibrations and used for feedback control. A combined piezoelectric (PZT) control scheme is designed to suppress the bending and torsional vibrations of the flexible hinged plate. A vibration control method based on divergence augmented policy optimization (DAPO) reinforcement learning (RL) for the fixed flexible hinged plate is developed to train the modal controllers. Simulation and experiments are carried out to verify the effectiveness of the applied vibration control scheme and algorithm. The simulation and experimental results show that DAPO RL modal controllers have better vibration suppression effect than large gain proportional derivative (PD) controllers, especially for small amplitude residual vibration.

关键词： Flexible hinged plate Visual detection Vibration suppression Model identification Divergence-augmented policy optimization (DAPO) reinforcement learning algorithm

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Observer-Based Adaptive Optimized Control for Uncertain Cyclic Switched Nonlinear Systems: reinforcement learning algorithm Approach

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2024年第5期71卷 2203-2216页

作者： Yan, Chengyuan Xia, Jianwei Park, Ju H. Sun, Wei Xie, Xiangpeng Liaocheng Univ Sch Math Sci Liaocheng 252000 Shandong Peoples R China Yeungnam Univ Dept Elect Engn Gyongsan 38541 South Korea Nanjing Univ Posts & Telecommun Inst Adv Technol Nanjing 210023 Peoples R China

This article addresses adaptive optimized tracking control problem for strict-feedback cyclic switched nonlinear output constrained systems with average cyclic dwell time (ACDT). Different from most of the existing results on optimized control of switched nonlinear systems, a new mode-dependent reinforcement learning (RL) algorithm of identifier-critic-actor architecture is designed. Technically, with the help of neural networks (NNs) switched state observer, the virtual and actual optimal controllers are developed by solving the Hamilton-Jacobi-Bellman (HJB) equation. Meanwhile, to reduce the impact of systems switching on the overall optimization control, the information of the switching signal is considered into the optimal performance index functions. In an attempt to settle the output constraints, a nonlinear output-dependent time-varying function is used to ensure that the system output never transgresses the prescribed regions. More importantly, by combining the improved ACDT method and Lyapunov stability theorem, a novel adaptive optimized control scheme is put forward which ensures that the boundedness of all signals in the closed-loop system. Finally, the effectiveness of the proposed optimized control algorithm is verified by numerical as well as practical simulations.

关键词： Neural networks switched nonlinear systems adaptive optimized control reinforcement learning algorithm switched state observer

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RESEARCH ON ELEVATOR GROUP SCHEDULING STRATEGY AND SIMULATION BASED ON reinforcement learning algorithm

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MECHATRONIC SYSTEMS AND CONTROL 2024年第4期52卷 251-259页

作者： Tian, Rui Gao, Weimin Guangdong Construction Polytech Sch Mech & Elect Engn Guangzhou 510440 Guangdong Peoples R China Hunan Univ Med Sch Med Humanities & Informat Management Huaihua 418000 Hunan Peoples R China

The multi-objective elevator group optimisation problem has attracted widespread attention due to its high practical significance. The elevator group control system (EGCS) is a typical multi- objective system designed to increase passenger service and reducing costs, such as energy consumption (EC). Based on the characteristics of elevator group scheduling problem, this paper treats EGCS as multiple reinforcement learning agents that cooperate with each other. Each agent controls the operation of an elevator according to the average reward reinforcement learning algorithm (RLA), aiming to minimise the average waiting time (AWT) of passengers. Additionally, a neural network is employed to store and update the state behaviour value functions while dynamically classifying the state of unknown environments. The simulation results show that the reinforcement learning based-scheduling algorithm has better scheduling effects than the static partition scheduling algorithm (SPSA) and cultural algorithm (CA).

关键词： Elevator group control systems adaptive multi-objective optimisa- tion reinforcement learning algorithm

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New Trust Management Scheme Based on Blockchain and KNN reinforcement learning algorithm

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2024年第4期15卷 1158-1176页

作者： Aljohani, Ahdab Hulayyil Al-Shammri, Abdulaziz Imam Muhammed ibn Saud Islamic Univ IMISIU Comp Sci Informat Secur Dept Riyadh Saudi Arabia

There has been a continual rise in the quantity of smart and autonomous automobiles in recent decades. the effectiveness of communication among vehicles in Vehicular Ad-hoc Networks (VANET) is critical for ensuring the safety of drivers' lives. the primary objective of VANET is to share critical information regarding life-threatening events, such as traffic jams and accident alerts in a timely and accurate manner. Nevertheless, typical VANETs encounter several security issues involving threats to confidentiality, integrity, and availability. This paper proposes a new decentralized and tamper-resistant scheme for privacy preservation. We designed a new trust management system that utilizes blockchain technology. We strive to establish trust between vehicles and infrastructure and preserve privacy by guaranteeing the authenticity and integrity of the information exchanged in VANETS. Our proposal adopts the principles of reinforcement learning to dynamically evaluate and allocate trust scores to vehicles and infrastructure based on their behavior. The scheme's performance has been evaluated based on key metrics. The results show that our new system provides an effective behavior management technique while preserving vehicle privacy.

关键词： Vehicular Ad hoc Networks (VANETs) Blockchain trust management reinforcement learning algorithm privacy preservation network security

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Optimization of river environmental management based on reinforcement learning algorithm: a case study of the Yellow River in China

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2023年第3期30卷 8170-8187页

作者： Chen, Minghong Li, Ying Zhang, Xue Liao, Renkuan Wang, Changxi Bi, Xiangshuai China Agr Univ Coll Water Resources & Civil Engn Beijing 100083 Peoples R China China Agr Univ Coll Land Sci & Technol Beijing 100083 Peoples R China

Generating scientific management strategy contributes to the sustainable development of river ecological environment. In this study, a multi-objective coupled water and sediment regulation model aiming at minimizing sedimentation and inundation loss as well as maximizing ecological value in the lower Yellow River has been developed. A reinforcement Q-learning algorithm was used to obtain optimized strategies from the multi-objective of sediment reduction, flood control and ecological restoration under different hydrological years. The results showed that the simulated channel sedimentation is very close to the measured value, which proves the applicability of the developed model. Under dry, normal and wet hydrological year, the effects of various regulation strategies on silt reduction, flood control and ecological restoration were obviously different. The regulation scheme of discharge at 3700 m(3)/s was verified to be suitable for dry and wet year, and that of discharge at 2600 m(3)/s was more suitable for normal year. Increasing the spacing of the beach area was better in normal year and wet year. Our findings suggested optimized strategies to address environmental challenges of the lower Yellow River in different hydrological years. This paper provides a reliable reference for improving the management of the lower Yellow River.

关键词： River siltation River scouring River dredging reinforcement learning algorithm Water ecological value

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Design of Human Resources Management Decision System Based on Multi-Agent System and reinforcement learning algorithm 24

Design of Human Resources Management Decision System Based o...

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3rd International Conference on Cyber Security, Artificial Intelligence and Digital Economy (CSAIDE)

作者： Yao, Wenyan Zhang, Tianbao Nanning Univ Nanning 530200 Guangxi Peoples R China Univ Sains Malaysia Sch Management George Town Malaysia Qinzhou Tobacco Monopoly Bur Qinzhou 535000 Guangxi Peoples R China

ISBN: (纸本)9798400718212

This study aims to address the lack of scientific and systematic decision systems in the field of Human Resources Management (HRM). By designing a HRM decision support system based on Multi-Agent systems and reinforcement learning algorithms, effective tools are provided to HR managers to assist them in making more scientific and systematic HRM decisions. The research analyzes the current issues in HRM practices and proposes comprehensive solutions. Through the optimization of Multi-Agent reinforcement learning algorithms, experiments validate the effectiveness of the system in supporting decision-making in HRM. The results demonstrate that the improved algorithms outperform traditional methods, confirming the efficacy of the system's design and optimization. This HRM decision support system, based on Multi-Agent systems and reinforcement learning algorithms, holds the potential to drive organizational development and enhance the efficiency of HRM. However, further research and practical application are needed to refine and optimize the system to adapt to the constantly evolving HRM environment.

关键词： Human Resources Management (HRM) Multi-Agent Systems reinforcement learning algorithm Decision Support System

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Intelligent Energy Management Strategy Based on an Improved reinforcement learning algorithm With Exploration Factor for a Plug-in PHEV

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2022年第7期23卷 8725-8735页

作者： Lin, Xinyou Zhou, Kuncheng Mo, Liping Li, Hailin Fuzhou Univ Coll Mech Engn & Automat Fuzhou 350108 Peoples R China West Virginia Univ Dept Mech & Aerosp Engn Morgantown WV 26506 USA

An intelligent energy management strategy (EMS) based on an improved reinforcement learning (RL) algorithm is developed to enhance the adaptability of the EMS and to further improve the fuel efficiency of a Plug-in Parallel Hybrid Electric Vehicle (PHEV). Both the numerical model and the energy management strategy of a plug-in PHEV are described. The improved RL with Q-learning algorithm is implemented to acquire the optimal control strategies for improving fuel economy. The Markov Chain is employed to calculate the Transition Probability Matrix of the required power. A Kullback-Leibler (KL) divergence rate is designed to activate the update of EMS, when a new corresponding driving cycle is expected. An Exploration Factor (FT) is proposed to overcome the disadvantages of the normal RL algorithm in convergence rate and reward cost evaluation. The diverse KL divergence rates are examined to seek optimal solutions. The normal-RI, strategy, rule-based strategy, and dynamic programming strategy are implemented as benchmark strategies to verify the effectiveness of the proposed strategy. The validation results indicate that the improved RL algorithm with FY makes it possible to promote the EMS capable of significantly improving the energy efficiency of a plug-in PHEV.

关键词： Hybrid electric vehicle energy management strategy reinforcement learning algorithm self-adaptive exploration factor

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Design and Application of English Oral Online Dialogue System Based on reinforcement learning algorithm

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Procedia Computer Science 2025年 261卷 716-723页

作者： Ting Chen Jiangxi Vocational College of Foreign Studies Nanchang 330099 Jiangxi China

In view of the lack of personalization and interactivity in current oral English learning, a dialogue system based on reinforcement learning is introduced, aiming to improve users’ oral expression ability by dynamically adjusting the learning path and feedback mechanism. The system adopts a deep Q-learning algorithm, optimizes the voice interaction effect through a reward mechanism, and realizes real-time adaptive adjustment of the dialogue. First, a dialogue framework based on reinforcement learning is designed, in which the user’s voice input is converted into text and used as the input of the system. Then, the system uses a deep Q-learning algorithm to conduct feedback learning based on the user’s voice performance and grammatical errors, and adjusts the dialogue strategy and vocabulary recommendation in real time to improve the interactivity and accuracy of learning. Finally, the system trains multiple rounds of dialogues in a simulated environment to continuously optimize the speech recognition and dialogue response strategies. The whole process uses a reward mechanism to adjust the system behavior based on the actual performance of the user to ensure gradual improvement in the learning process. Learners with a medium foundation (intermediate) have a slight improvement in satisfaction scores, reaching 4.3 points, and their oral test improvement is +15 points. Learners with a high foundation (advanced) have a satisfaction score of 4.5 points in the use of the system, and their oral test improvement is +18 points, thanks to the detailed and accurate feedback provided by the system. By introducing a reinforcement learning algorithm, the designed English oral online dialogue system can adjust learning strategies in real time according to user feedback and improve learning effects. Future research will further optimize the reward mechanism and enhance the system’s adaptive ability and intelligent recommendation function to better serve the needs of different learner

关键词： reinforcement learning algorithm online english speaking dialogue system learning efficiency oral test

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Smart City Distributed Trade Economy Dynamic Scheduling of E-Commerce Platform Based on reinforcement learning algorithm

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JOURNAL OF TESTING AND EVALUATION 2024年第3期52卷 1804-1816页

作者： Tang, Yimeng Chongqing Inst Foreign Studies Sch Int Business & Management 18 Longshi Rd Chongqing 401120 Peoples R China

In order to improve the reliability and economy of decentralized trade economy dynamic scheduling on e-Commerce platforms and shorten the running time of decentralized trade economy dynamic scheduling on e-Commerce platforms, a decentralized trade economy dynamic scheduling method based on the reinforcement learning algorithm is proposed. In this paper, we analyze the basic theory of the reinforcement learning algorithm, study the Q -learning algorithm, build a neural network to fit the value model, and initialize the reinforcement learning algorithm. With Markov decision process as the framework model, the optimal state behavior value function is updated by using the modeless discounted reward reinforcement learning algorithm Q-learning as the value iteration method. Gibbs distribution is used to construct exploratory random strategies to select behaviors with probability. Using the reinforcement learning algorithm and the three-layer feedforward neural network as the approximator of the state behavior value function, this paper studies the generalization of the value function faced by the decentralized trade economy dynamic scheduling of e-Commerce platforms and realizes the decentralized trade economy dynamic scheduling of e-Commerce platforms. The experimental results show that the proposed method can effectively improve the reliability and economy of the decentralized trade economy dynamic scheduling of e-Commerce platforms.

关键词： reinforcement learning algorithm Q-learning algorithm e-Commerce platform decentralized trade economy dynamic scheduling

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Research on Computer Aided learning System Based On reinforcement learning algorithm

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Procedia Computer Science 2024年 243卷 472-481页

作者： Haiyan Lu School of Education and Foreign Languages Wuhan Donghu University Wuhan 430212 Hubei China

In today's information-based education era, the computer-aided instruction system under the background of "Internet +" is ushering in unprecedented development opportunities. Based on VARK model, this paper discusses the design and implementation of computer-aided instruction system based on reinforcement learning algorithm. Through in-depth investigation of the application status of computer-aided instruction system and the characteristics of popular systems at home and abroad, combined with the principle and application of reinforcement learning algorithm, this study builds a system architecture with personalized education evaluation function. In the aspect of system design, with the help of neural network model and online test module, the study realizes intelligent analysis and evaluation of students' learning patterns, and improves the pertinence and effect of teaching. Through strict functional and performance tests, the stability and reliability of the system are verified, and the network and intelligent characteristics of the system are demonstrated in the learning process of students, which makes a positive contribution to the improvement of the level of education information and the optimization of teaching effect.

关键词： Computer-Aided Instruction reinforcement learning algorithm VARK Model Intelligent Teaching System

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