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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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Stability and bifurcation analysis of delayed tri-trophic food chain model in poisoned environment with fear effect and additional food

Stability and bifurcation analysis of delayed tri-trophic fo...

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作者： Ramasamy, Sivasamy Banjerdpongchai, David Park, PooGyeon Center of Excellence in Intelligent Control Automation of Process Systems Department of Electrical Engineering Faculty of Engineering Chulalongkorn University Bangkok Thailand Department of Electrical Engineering Pohang University of Science and Technology Pohang Korea Republic of

One of the most important goals of theoretical ecologists is to find a strategy for controlling the chaos in ecological models to maintain healthy ecosystems. We investigate the influence of fear and the supply of additional food to predators in a delayed tri-trophic food chain model, where interference among the species is determined by Holling type-II functional response. The proposed model incorporates the fact that the growth rates of prey and middle predators are decreased due to the fear of middle and special predators, respectively. The additional food is supplied to both predators by either nature or external agencies. Furthermore, the poisoned prey in the predator's environment is taken into account. In the proposed model, we use two time delays: one for the growth term of the middle predator and another for the gestation delay of the special predator. We determine the conditions for the existence of ecologically feasible equilibrium points and their local and global stability. In addition, we establish the conditions for the existence of Hopf bifurcation around interior equilibrium to seek periodic behavior in nondelayed and delayed models. Numerical investigations are performed to justify the proposed theoretical findings through phase portraits, time series of solutions, and bifurcation diagrams. We observe that the chaotic dynamics of a tri-trophic food chain model can be controlled by the proper choice of the fear effect and by supplying additional food parameters. As a result, our findings provide more ecological insights into the dynamics of the delayed tri-trophic food chain model with the fear effect and additional food supply. © 2023 John Wiley & Sons, Ltd.

关键词： Hopf bifurcation

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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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Containment control of Multiple Quadrotor Unmanned Aerial Vehicles Based on Model Transformation 13

Containment Control of Multiple Quadrotor Unmanned Aerial Ve...

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13th IEEE Data Driven control and Learning Systems Conference, DDCLS 2024

作者： Chen, Pengfei Yang, Chunxi Zhang, Faxiang Kunming University of Science and Technology Faculty of Mechanical and Electrical Engineering Yunnan650500 China Yunnan Key Laboratory of Intelligent Control and Application Kunming650500 China

ISBN: (纸本)9798350361674

This paper investigates the containment control problem of a class of quadrotor unmanned aerial vehicle (QUAV) systems under directed topologies. Firstly, an integral chain model with unknown control directions is constructed by iteratively differentiating the system output. At the same time, a low-pass filter is introduced to transform the original system into a new system, avoiding the problem of unknown control direction. Then, a distributed containment control law is designed by using the dynamic surface control method, which ensures that the trajectories of the followers converge within the convex spanned by the leaders. The closed-loop system is also proved to be uniformly ultimately bounded (UUB) based on Lyapunov stability theory. Finally, simulation results show the effectiveness of the proposed control scheme. © 2024 IEEE.

关键词： Topology

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Event-Triggered Neural Tracking control of Cable-Driven Lower-Limb Exoskeleton Robots 13

Event-Triggered Neural Tracking Control of Cable-Driven Lowe...

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13th IEEE Data Driven control and Learning Systems Conference, DDCLS 2024

作者： Zhang, Hongqiang Zhang, Faxiang Na, Jing Gao, Guanbin Kunming University of Science and Technology Faculty of Mechanical and Electrical Engineering Kunming650500 China Yunnan Key Laboratory of Intelligent Control and Application Kunming650500 China

ISBN: (纸本)9798350361674

This paper gives an event-triggered neural tracking control strategy for cable-driven lower-limb exoskeleton robots. To reduce the number of triggers and prevent the jitter of the control signal, a switching threshold is used to drive the event trigger, which is different from the traditional trigger cases of fixed and relative thresholds. Firstly, a neural network (NN) is adopted to approximate the unknown nonlinear function, with the extracted estimation error being new leakage terms developed to update the weight of NN. Then an adaptive controller is designed to achieve the uniformly ultimately bounded(UUB) performance of the closed-loop system, and at the same time both transient and steady-state performance of the tracking error is guaranteed via a predefined performance function. Finally, the effectiveness of the proposed method is verified by simulation. © 2024 IEEE.

关键词： Exoskeleton (Robotics)

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Consensus Tracking Data-Driven control of Multi-Agent Systems based on Matrix S-Lemma under Noisy Data 13

Consensus Tracking Data-Driven Control of Multi-Agent System...

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13th IEEE Data Driven control and Learning Systems Conference, DDCLS 2024

作者： Tan, Shuli Zhang, Xiufeng Yang, Chunxi Kunming University of Science and Technology Faculty of Mechanical and Electrical Engineering Kunming China Yunnan Key Laboratory of Intelligent Control and Application Yunnan Kunming650504 China

ISBN: (纸本)9798350361674

In this paper, the problem of consensus tracking for multi-agent systems in the presence of noise inter-ference is investigated. Unlike the traditional model-based approach, this paper assumes that the dynamics of all agents are unknown and only a portion of the input-state data is available. The offline data contaminated with noise interference is utilized. By introducing the matrix S-lemma, a necessary and sufficient conditions for directly designing consensus tracking controllers from bounded noisy input-state data is given. This method can be applied to large datasets and effectively addresses computational challenges associated with extensive data. Simulation result illustrates the effectiveness of the theoretical analysis. © 2024 IEEE.

关键词： Multi agent systems

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Multi-Task Learning for Alzheimer's Disease Diagnosis and Mini-Mental State Examination Score Prediction

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Big Data Mining and Analytics 2024年第3期7卷 828-842页

作者： Jin Liu Xu Tian Hanhe Lin Hong-Dong Li Yi Pan Hunan Provincial Key Lab on Bioinformatics School of Computer Science and EngineeringCentral South UniversityChangsha 410083China Xinjiang Engineering Research Center of Big Data and Intelligent Software School of SoftwareXinjiang UniversityUrumqi 830091China School of Science and Engineering University of DundeeDundeeDD14HNUK Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China

Accurately diagnosing Alzheimer's disease is essential for improving elderly ***,accurate prediction of the mini-mental state examination score also can measure cognition impairment and track the progression of Alzheimer's ***,most of the existing methods perform Alzheimer's disease diagnosis and mini-mental state examination score prediction separately and ignore the relation between these two *** address this challenging problem,we propose a novel multi-task learning method,which uses feature interaction to explore the relationship between Alzheimer's disease diagnosis and minimental state examination score *** our proposed method,features from each task branch are firstly decoupled into candidate and non-candidate parts for ***,we propose feature sharing module to obtain shared features from candidate features and return shared features to task branches,which can promote the learning of each *** validate the effectiveness of our proposed method on multiple *** Alzheimer's disease neuroimaging initiative 1 dataset,the accuracy in diagnosis task and the root mean squared error in prediction task of our proposed method is 87.86%and 2.5,*** results show that our proposed method outperforms most state-of-the-art *** proposed method enables accurate Alzheimer's disease diagnosis and mini-mental state examination score ***,it can be used as a reference for the clinical diagnosis of Alzheimer's disease,and can also help doctors and patients track disease progression in a timely manner.

关键词： multi-task learning Alzheimer's disease diagnosis mini-mental state examination score prediction

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Underwater Object Detection Based on Image Enhancement and Multi-Branch Structure 43

Underwater Object Detection Based on Image Enhancement and M...

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43rd Chinese control Conference, CCC 2024

作者： Cui, Zheng Wang, Xian Duan, Hao Wang, Sen Yang, Chunxi Na, Jing Kunming University of Science and Technology Yunnan Key Laboratory of Intelligent Control and Application Kunming650500 China Kunming University of Science and Technology Faculty of Mechanical and Electrical Engineering Kunming650500 China

ISBN: (纸本)9789887581581

Underwater object detection plays an important role for underwater intelligent applications. However, optical-based underwater images often suffer from color bias, haze-like, small scale object and occlusions, resulting in lower detection accuracy for detectors. To address the above problems, an underwater object detection network based on image enhancement and multibranch structure is proposed. Concretely, a parameter-free color correction attention module is first presented to adjust the color cast of underwater images, providing detection-favorable features to the network. In addition, a backbone network and a neck network are constructed based on the attention mechanism and multi-branch structure to improve the detection ability of the network for small and occluded objects. The decoupled head is further introduced to implement the regression and classification tasks. Extensively qualitative and quantitative experiments show that the proposed method achieves a maximum AP of 53.2% at 280 FPS in the UODD dataset, outperforming other mainstream methods. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Underwater imaging

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Informer-LSTM-EWMA for Panax Notoginseng with Prediction of Early Intervention Time 13

Informer-LSTM-EWMA for Panax Notoginseng with Prediction of ...

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13th IEEE Data Driven control and Learning Systems Conference, DDCLS 2024

作者： Ye, Jiahui Yang, Chunxi Zhang, Xiufeng Zhang, Jin Kunming University of Science and Technology Faculty of Mechanical and Electrical Engineering Kunming650504 China Yunnan International Joint Laboratory of Intelligent Control and Application of Advanced Equipment China Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming650504 China

ISBN: (纸本)9798350361674

Panax notoginseng is one of economic crop with significant medicinal value in southwest China. However, traditional extensive fertilization and irrigation method usually damages the soil, making it difficult to achieve sustainable cultivation of Panax notoginseng. In order to assess the growth status of Panax notoginseng and take timely action, this paper proposed an Informer-LSTM-EWMA model to predict the growth trend of Panax notoginseng in cultivation fields. In the experimental greenhouse, we set up two experimental fields A and B to verify the feasibility of the proposed method. Firstly, wavelet denoising is utilized on the date collected by the sensor. Then, Informer and Long short-term Memory (LSTM) are used to train the denoised data to obtain data prediction. Further process the predicted data through Exponential Weighted Moving Average (EWMA) for trend prediction. Finally, the early intervention time is obtained by searching for the maximum value. The results indicate that the model can predict early intervention time and its predictive performance is far better than that of a single model. © 2024 IEEE.

关键词： Prediction models

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Adaptive Depth Estimation of Homography-Based Visual Servo with Finite-time Convergence 13

Adaptive Depth Estimation of Homography-Based Visual Servo w...

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13th IEEE Data Driven control and Learning Systems Conference, DDCLS 2024

作者： Fang, Qiwen Wang, Xian Huangl, Yingbo He, Haoran Kunming University of Science and Technology Faculty of Mechanical and Electrical Engineering Kunming650500 China Kunming University of Science and Technology Yunnan Key Laboratory of Intelligent Control and Application Kunming650500 China

ISBN: (纸本)9798350361674

Although the homography-based visual servo (HBVS) has been studied for many decades, how to obtain the depth information with fast and accurate convergence performance has not been fully studied. In this paper, a finite-time (FT) adaptive depth estimation method is suggested for the manipulator regulation of the eye-in-hand (EIH) visual servo system. To realize this objective, a linearized camera model including depth information is first established. To obtain the depth, a set of auxiliary variables are constructed to extract the depth estimation error with a series of simple algebraic calculation. Then, inspired by the sliding mode control, a FT adaptive depth estimation algorithm is developed, in which the predictor and/or observer error are not required, but the depth estimation error is employed to drive the estimation toward to its true value within finite time. With the estimated depth, the HBVS controller of the EIH system can regulate the robot arm to the desired position with fast response. Finally, comparative simulation results are given to show the efficiency of the proposed method. © 2024 IEEE.

关键词： Sliding mode control

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