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2nd International Conference on Frontiers of intelligent Manufacturing and Automation, CFIMA 2024

作者： Li, Siyan Xing, Yashan Na, Jing Li, Qi Faculty of Mechanical and Electrical Engineering Yunnan Key Laboratory of Intelligent Control and Application Kunming University of Science and Technology Yunnan China

ISBN: (纸本)9798400710681

In this paper, a robust fixed-time composite control scheme based on an Unknown System Dynamics Estimator (USDE) is proposed. The proposed control method guarantees the tracking error convergence in a fixed-time for nonlinear robotic manipulators with unknown dynamics and disturbance. To address the existence of uncertainties, first, a simple structure of USDE by a low-pass filtering operation is constructed without using acceleration signals, Which is adjusted for only one filter coefficient. Then, the USDE compensate the estimated value into the robust fixed-time composite controller. The composite controller contains a fixed time control term, a robust control term, and a computed torque control term. By the fixed-time control term, the upper bound on the convergence time is independent of the initial conditions of the system, which ensures fast-tracking tasks for the robot manipulator. And the robust control term is used to reduce the impact of USDE estimation errors. The stability of the closed-loop system is demonstrated through Lyapunov criteria. The validity of the proposed method is verified by simulation. © 2024 Copyright held by the owner/author(s).

关键词： Stability criteria

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Optimal Adaptive Parameter Estimation With Online Varying Learning Gain

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IEEE Transactions on Automatic control 2025年第5期70卷 3518-3525页

作者： Chen, Siyu Na, Jing Huang, Yingbo Xing, Yashan Zhao, Jing Wong, Pak Kin Kunming University of Science and Technology Faculty of Mechanical and Electrical Engineering Yunnan Key Laboratory of Intelligent Control and Application Kunming650500 China University of Macau Department of Electromechanical Engineering 999078 China

It has been well known that the learning gain plays a crucial role in the adaptive parameter estimation (APE) for guaranteeing fast convergence and robustness. However, the tuning of learning gains in the existing methods is generally empirical and time-consuming. To address this issue, this article presents a novel APE approach to explore the optimality principle in the design of adaptive laws to obtain an online updated optimal learning gain, which is derived to minimize a cost function of estimation error with two weighting matrices. For this purpose, the estimation error is first reconstructed by using filter operations on the system and introducing several auxiliary variables. Then, two constructive APE algorithms driven by the extracted estimation error are developed. Finally, inspired by the duality of control and estimation, the estimation error dynamics are reformulated as a closed-loop system with an analogous control action to minimize the cost function. By using the Hamiltonian function, a differential Riccati equation is derived to find an optimal solution of learning gain to construct an optimal adaptive law. The robustness of the proposed adaptive laws subjected to disturbances is also analyzed. Comparative numerical simulations are given to illustrate the superiority of the proposed method. © 1963-2012 IEEE.

关键词： Cost functions

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Kinematic Parameter Identification of Industrial Robots Based on an Improved Zebra Algorithm 16th

Kinematic Parameter Identification of Industrial Robots Base...

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16th International Conference on Modelling, Identification and control, ICMIC 2024

作者： Zhou, Houchen Gao, Guanbin Wang, Xi Faculty of Mechanical and Electrical Engineering Kunming University of Science and Technology Kunming650500 China Yunnan Key Laboratory of Intelligent Control and Application Kunming650500 China

ISBN: (纸本)9789819617760

Kinematic calibration is one of the main methods to improve the absolute positioning accuracy of industrial robots. Parameter identification is the primary step in determining the calibration effect. However, due to the large number of kinematic parameters of industrial robots and the complex coupling between parameters, the existing parameter identification methods easily fall into local optimal solutions, and it is difficult to obtain accurate kinematic parameters. Therefore, this paper proposes a Particle Swarm Somersault Foraging Zebra Optimization Algorithm (PSSFZOA) for kinematic parameter identification of industrial robots. The proposed PSSFZOA introduces the foraging strategy into the Zebra Optimization Algorithm (ZOA) and linearizes the kinematic error model so that it does not fall into the local optimum in the later stage of optimization, and improves the population diversity in the early stage. In the first stage of the algorithm, the particle swarm strategy is used to provide local optimal particles, which ensures that the algorithm achieves a balance between global search and local search. It has both the ability of deep search and the diversity of breadth search better to find the best in the complex search space. Simulations are carried out to verify the effectiveness of the method. The results show that compared with the existing Grey Wolf Optimization (GWO) and ZOA, the PSSFZOA can avoid falling into local optimum prematurely, accelerate the convergence speed of the algorithm, and improve the absolute positioning accuracy of the industrial robot after parameter identification. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Industrial robots

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Adaptive Parameter Estimation with Directional Forgetting for Nonlinear Systems 16th

Adaptive Parameter Estimation with Directional Forgetting f...

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16th International Conference on Modelling, Identification and control, ICMIC 2024

作者： Wen, Guoli Xing, Yashan Na, Jing Gao, Guanbin Faculty of Mechanical and Electrical Engineering Kunming University of Science and Technology Kunming650500 China Yunnan Key Laboratory of Intelligent Control and Application Kunming University of Science and Technology Kunming650500 China

ISBN: (纸本)9789819617760

This paper proposes a novel adaptive parameter estimation (APE) with directional forgetting (DF) based on the parameter estimation error for nonlinear systems. Most of APE methods are designed to forget the information of the regressor matrix in all direction in order to update the system information. However, some directions of the regressor matrix for systems are insufficient such that forgetting in all directions may trigger windup phenomenon. To prevent this phenomenon, the proposed method in this paper is to divide the regressor matrix into a forgotten part and a retained part via oblique projection. Then, the constant forgetting factor is applied to the forgotten part such that forgetting the regressor matrix is along the direction that the new information comes. Furthermore, the adaptive law is designed based on the parameter estimation error and the exponential convergence of the proposed method can be guaranteed under the persistent excitation (PE) condition. Finally, comparative simulation related to the proposed method and the classic APE method driven by the parameter estimation error is provided. Simulation results show that the proposed APE with DF algorithm has a better estimation performance than the classic APE method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Errors

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A study of MySQL protocol-based database proxy approval system for fortress machine

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International Journal of Data Science 2025年第1期10卷 96-117页

作者： Zhang, Xian Luo, Xinhui Yin, Dong Qu, Taiguo Li, Hao Huaihua University Huaihua418000 China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Huaihua418000 China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Hunan Huaihua418000 China Huaihua Tourism School Hunan Huaihua418099 China

With the increase of enterprise informatisation, database security and compliance operation management have become increasingly important. Therefore, it is essential to design an efficient database proxy approval system. In this paper, we develop a database proxy approval system based on the MySQL protocol for fortress machines, which provides a real-time customised configuration scheme for high-risk commands, designs a real-time approval process for six types of high-risk commands, and creates a simple and efficient matching algorithm for high-risk commands. We designed a large number of experiments to test the system’s connection success rate, operation stability, response time, CPU resource consumption, matching algorithm performance, and other aspects. The experimental results show that this database proxy approval system has good configuration flexibility, high accuracy, and good time performance. This system has a wide range of applications in electric power, finance, petroleum, and other fields. © 2025 Inderscience Enterprises Ltd.

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USDE-Based Task-Space Bilateral Teleoperator control Design with Time Delay 16th

USDE-Based Task-Space Bilateral Teleoperator Control Design ...

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16th International Conference on Modelling, Identification and control, ICMIC 2024

作者： Luo, Xin Ma, Jun Wang, Xian Na, Jing Xing, Yashan Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Faculty of Mechanical and Electrical Engineering Kunming University of Science and Technology Kunming650500 China Yunnan Key Laboratory of Intelligent Control and Application Kunming University of Science and Technology Kunming650500 China

ISBN: (纸本)9789819617760

In this paper, we present a control strategy for bilateral teleoperators operated in the task space, which is designed to estimate the uncertain dynamics. To implement this proposed strategy, we develop a straightforward yet robust unknown system dynamics estimator (USDE) by incorporating a low-pass filtering operation. By using the USDE, we can eliminate the impact of unmodeled system dynamics without the need to measure the acceleration signal. Furthermore, the proposed USDE offers the advantages of requiring only one tuning parameter and being easily adjustable compared to conventional estimators. The convergence of the estimation error associated with the unmodeled system dynamics and the stability of the closed-loop control system are both rigorously demonstrated through Lyapunov’s theorem. Ultimately, the superior performance of our method, as evidenced by enhancing motion synchronization and stability, is confirmed through simulation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Time delay

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Infrared and visible image fusion with multi-resolution enhancement and dynamic weighted

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Optics and Laser Technology 2025年 188卷

作者： Wang, Zhongqiang Wang, Chengchao Pu, Yuanyuan Peng, Jinjiang Zhao, Zhengpeng Gu, Jinjing College of Information Science and Engineering Yunnan University Kunming650500 China The Universities Key Laboratory of Internet of Things Technology and Application in Yunnan Kunming650500 China Yunnan Key Laboratory of low-light Night Vision Technology and Intelligent Visual Navigation Kunming650500 China

Infrared and visible image fusion (IVIF) aims to generate fused images by combining intensity and detailed information from source images. Existing IVIF methods typically rely on manually defined loss function weights and lack dynamic adaptability to complex scenes. Additionally, single-resolution approaches are limited in capturing complementary information across multiple resolutions. In this study, we propose an adaptive loss-weights and multi-resolution enhancement framework for IVIF, improve adaptability to complex scenes by integrating fine-grained details and background textures. Our approach introduces a multi-resolution detail enhancement (MRDE) module that processes extracted features at three distinct scales to enhance texture details. We also present a holographic fusion strategy composed of two modules: the Structural Integration Module (SIM) and Multi-resolution Texture Fusion Modul (MTFM), which effectively integrate structural and texture information across resolutions. Moreover, we propose an adaptive weighting dynamic balancing loss function that adjusts the emphasis on texture and structure during training. Extensive experiments demonstrate that our method outperforms state-of-the-art IVIF techniques, achieving superior visual balance and improved quantitative performance. © 2025

关键词： Image fusion

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RISE-Based Prescribed Performance control for Multimotor Driving Systems

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IEEE Transactions on Industrial Informatics 2025年

作者： Wang, Shubo Chen, Qiang Kunming University of Science and Technology Faculty of Mechanical and Electrical Engineering Yunnan Key Laboratory of Intelligent Control and Application Kunming650500 China Zhejiang University of Technology College of Information Engineering Hangzhou310023 China

In this article, a novel approximation free control method was developed for multimotor driving systems with prescribed performance. The developed controller includes multimotor synchronization controller and the load tracking controller. First, the synchronous controller was designed by using the robust integral of the sign of the error to guarantee that all the motors states can converge their average value. Second, to achieve the load tracking control, an approximation free controller was developed based on the prescribed performance function. This method can avoid the function approximators and reduce the reduced computational burden and complexity. With the developed control strategy, the tracking error remains in a prescribed boundary, and the multimotor synchronous error achieves the asymptotic convergence. Finally, comparative experiments are conducted on a four-motor drive system to test the enhanced performance and robustness of the designed controller. © 2005-2012 IEEE.

关键词： Robust control

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Dynamic Modeling of Integrated Wind-Wave Offshore Floating Platform with Parameter Uncertainty

Dynamic Modeling of Integrated Wind-Wave Offshore Floating ...

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International Conference on Applied Nonlinear Dynamics, Vibration and control, ICANDVC 2024

作者： Yuan, Bozhong Huang, Yingbo Na, Jing Faculty of Mechanical and Electrical Engineering Kunming University of Science and Technology Kunming650500 China Yunnan Key Laboratory of Intelligent Control and Application Kunming University of Science and Technology Kunming650500 China

ISBN: (纸本)9789819633166

Advanced model-based control strategies can significantly improve energy conversion efficiency and equipment safety. However, current hybrid wind-wave energy system model can often accurately describe the system response but are complex in structure, and they are more suitable for performance analysis than controller design. In this paper, a dynamic model is proposed, which contains six degrees of freedom (DOFs), the surge, pitch and heave of the floating offshore wind turbine (FOWT), and one pitch for each of the three wave energy conversions (WECs). The model is derived from Newton’s second law so that it has the same physical significance as the real system, and an adaptive algorithm is used to estimate the unknown parameters in the model. The model is validated by AQWA/ANSYS and proved to accurately predict the motion response of the platform under different environmental conditions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Wave energy conversion

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Research on Early Warning of Drug Crime Behaviors Based on Data-driven Method 24

Research on Early Warning of Drug Crime Behaviors Based on D...

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Proceedings of the 2024 4th International Conference on Big Data, Artificial Intelligence and Risk Management

作者： Shaobing Wu Changmei Wang Institute of Information Security Key Laboratory of Intelligent Drug Control Yunnan Police College Kunming Yunnan China Education Ministry Key Laboratory of Advanced Technology and Preparation for Renewable Energy Materials Yunnan Normal University Kunming Yunnan China

ISBN: (纸本)9798400709753

The identification of target suspects in images and videos from multi-source heterogeneous data is a difficult task in criminal investigation. In criminal scene analysis during law enforcement, images and videos from surveillance applications and security systems play an important role in providing visual files for the scene. With the rise of big data, artificial intelligence, the Internet of Things, blockchain, and cyberspace security, it is particularly important to improve the application of computer technology in drug crime cases and achieve data-driven drug crime behavior prediction. This paper builds police subject-specific databases such as drug addicts, drug traffickers, key personnel, and fugitives, and achieves drug crime behavior prediction and warning based on identity information, facial recognition and storage and picture comparison using a combination of Python and OpenCV. It provides a useful exploration for combating Drug Crime.

关键词： data-driven

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