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7th Chinese Conference on Swarm Intelligence and Cooperative control, CCSICC 2023

作者： Li, Zheng Ma, Shuai Chen, Bin Hu, Qinglei Zheng, Jianying Tianmushan Laboratory Hangzhou310023 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Beijing Institute of Control Engineering Beijing100090 China

ISBN: (纸本)9789819733279

Dual-super satellite is a new type of spacecraft structure that separates the satellite platform and payload, and blocks the transmission of micro-vibrations from the platform through mechanical isolation to provide an ultra-precise and ultra-stable payload environment. However, the cable used for energy supply and communication of the payload has an impact on the pointing performance of the payload. In order to solve the uncertainty of the payload dynamics model brought by the flexible cable and the low-frequency bounded interference problem, this paper proposes a sliding mode adaptive control method for the payload under the influence of cables. A feedforward compensation algorithm based on partial state observer is designed for bounded disturbance of satellite platform. The control method presented in this paper ensures the pointing performance of the payload during agile maneuvers, and demonstrates robustness against model uncertainty and low-frequency disturbances caused by cables through simulation studies. © The Author(s) Hangzhou 310023, under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Satellites

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Dissolved oxygen concentration control in wastewater treatment process based on reinforcement learning

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Science China(Technological Sciences) 2023年第9期66卷 2549-2560页

作者： DU ShengLi CHEN PeiXi HAN HongGui QIAO JunFei School of Artificial Intelligence and Automation Beijing University of TechnologyBeijing 100124China Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of EducationBeijing 100124China Beijing Laboratory of Smart Environmental Protection Beijing 100124China

In this article, the dissolved oxygen(DO) concentration control problem in wastewater treatment process(WWTP) is *** existing control strategies that control DO concentration at a fixed value, here we develop a different control *** the proposed control framework, an intelligent control method of DO concentration based on reinforcement learning(RL)algorithm is presented to resolve the DO concentration control problem. By using the deep deterministic policy gradient(DDPG)algorithm, the DO concentration of the fifth tank in the activated sludge reactor can be adjusted dynamically. In addition, by designing two different reward functions and by analysing the relationships among effluent quality, energy consumption, and DO concentration, the target of energy-saving and emission-reducing is achieved. The simulation results indicate that the designed control method can reduce energy consumption while ensuring that the effluent quality meet the specified standards.

关键词： dissolved oxygen concentration wastewater treatment process intelligent control reinforcement learning energy-saving and emission-reducing

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Receding horizon control for continuous-time mean-field systems

Receding horizon control for continuous-time mean-field syst...

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作者： Gao, Rong Liu, Junzhe Zhang, Fang Xu, Juanjuan Wang, Hongxia School of Mathematics and Statistics Science Ludong University Yantai China School of Control Science and Engineering Shandong University Jinan China College of Electrical and Automation Engineering Shandong University of Science and Technology Qingdao China

In this paper, we study the receding horizon control (RHC) stabilization of continuous-time mean-field system. An explicit RHC controller is obtained by introducing a cost function with two terminal matrices and the conditions that make the system stable are also obtained;that is, when two coupled Lyapunov inequalities are satisfied, the system controlled by RHC is stabilized. Finally, a simulation example is given to verify the effectiveness of the proposed strategy. © 2023 Chinese Automatic control Society and John Wiley & Sons Australia, Ltd.

关键词： Stabilization

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Thermal constraint control of reversible solid oxide cell for optimal system efficiency

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International Journal of Hydrogen Energy 2025年 134卷 198-211页

作者： Xu, Yuanwu Yin, Jie Wang, Jingjing Wu, Xiao-long Peng, Jingxuan Tang, Ruifeng Zou, Suhang Fu, Xiaowei Li, Xi School of Artificial Intelligence and Automation Wuhan University of Science and Technology Hubei Wuhan430081 China School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control of Education Ministry Huazhong University of Science and Technology Hubei Wuhan430074 China School of Information Engineering Nanchang University Jiangxi Nanchang330031 China School of Electrical and Electronic Engineering Nanyang Technological University 639798 Singapore China-Poland Belt and Road Joint Laboratory on Measurement and Control Technology Huazhong University of Science and Technology Hubei Wuhan430074 China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen518055 China

Reversible solid oxide cell (RSOC) demonstrates considerable promise in multimodal energy conversion architectures, particularly as a technology for hydrogen-based energy storage systems. Nevertheless, under dynamic operating regimes with time-varying current inputs, realizing time-efficient and reliable transitions to steady-state operation remains difficult. This difficulty arises from the intricate coupling of thermal, electrical, and gaseous processes within the RSOC system. This study aims to address these challenges by developing a 5 kW mathematical model of the RSOC system. The proposed model enables systematic investigation of thermal safety issues arising during current switching operations while introducing a thermal constraint control strategy to enhance both system efficiency and thermal safety performance. This efficiency-driven study employs the Grid Search algorithm to identify optimal steady-state operating points for the RSOC system, ensuring strict adherence to safety constraints across varying current inputs. Furthermore, by integrating the pre-identified optimal operating points as feedforward control with Model Predictive control (MPC)-based feedback correction, this hybrid strategy effectively mitigates stack temperature gradient overshoot while synchronously ensuring optimal system efficiency throughout current transition processes. Under simulated variable current inputs, the proposed RSOC system model and control strategy demonstrate the capability to constrain stack temperature gradient overshoot within 2.3K while enhancing transient efficiency by 5 %–8 %, achieving a peak system efficiency of 85 %. It provides a novel methodology for safe and efficient working with intermittent renewable energy. © 2025 Hydrogen Energy Publications LLC

关键词： Thermal variables control

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ADRC-Based controller with Linkage Compensation for Interleaved Parallel Converters 6

ADRC-Based Controller with Linkage Compensation for Interlea...

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6th International Conference on Energy, Electrical and Power engineering, CEEPE 2023

作者： Wang, Bo Zhou, Xuesong Zhao, Chenyang Tao, Long Ma, Youjie Gao, Zhiqiang School of Electrical Engineering and Automation Tianjin University of Technology Tianjin China School of Control Science and Engineering Tiangong University Tianjin China School of Electrical and Information Engineering Tianjin University Tianjin China

ISBN: (纸本)9798350348279

The microgrid is the key development direction of distributed energy. However, the performance indicators of its low-voltage side interface are often affected under complex operating conditions. To address this problem in a targeted manner, a linkage compensatory active disturbance rejection controller (LC-ADRC) that considers both the higher-order perturbation model and object model information is proposed. In this work, the extended state observer (ESO) and the feedback control law of the LC-ADRC are reconstructed according to the interleaved parallel bi-directional converter (IBC) characteristics, and the theoretical analysis shows that the dynamic response and immunity to disturbances of the IBC under typical conditions can be improved simultaneously. Finally, the experimental curves and test indexes obtained from the 300-watt test platform designed for these operating conditions verified the accuracy of the theoretical analysis. © 2023 IEEE.

关键词： controllers

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Enhancing Human-AI Trust by Describing AI Decision-Making Behavior

Enhancing Human-AI Trust by Describing AI Decision-Making Be...

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2023 IEEE International Conference on Unmanned Systems, ICUS 2023

作者： Lou, Zhouwei Wei, Haikun School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China

ISBN: (纸本)9798350316308

Air combat game is a highly complex and dynamic decision-making problem that is crucial for ensuring national security and improving combat efficiency. In recent years, artificial intelligence (AI) technologies such as deep reinforcement learning have made significant progress in the air combat game field, surpassing human experts' capabilities. However, the decision-making process of AI algorithms often lacks transparency and interpretability, resulting in low trust in them, which limits their promotion and application in practical scenarios. To enhance human-AI trust, this paper proposes a decision explanation method based on natural language generation. As the most direct means of information transmission, natural language can help people quickly understand the behavior and intent of AI algorithms. Taking a one-on-one air combat game as an experimental scenario, this paper constructs a combat dataset mapping temporal states to behavioral explanations and designs an attention-based Encoder-decoder architecture (AED) capable of generating natural language descriptions of current AI decision-making behavior based on a period of combat data. Experimental results show that AED can accurately describe the decision-making behavior of AI algorithms and help improve the level of human-AI trust. © 2023 IEEE.

关键词： Deep learning

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Model-free adaptive predictive control of nonlinear sandwich systems with input constraints 13

Model-free adaptive predictive control of nonlinear sandwich...

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

作者： Dong, Jianguo Liu, Jun Jiang, Xiaoqing Wang, Ziyu Luo, Huarui Shi, Zhao School of Automation Southeast University Nanjing210000 China School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 China College of Information Science and Engineering Northeastern University Shenyang China

ISBN: (纸本)9798350361674

Since nonlinear sandwich systems with input constraints are difficult to establish their model accurately, a model-free adaptive predictive control (MFAPC) based on compact format dynamic linearization is proposed. The criterion function of MFAPC algorithm is applied to build a quadratic programming problem with input constraints. The optimal control input signal can be obtained by solving the quadratic programming problem. In order to eliminate the adverse effect of random measurement noise on solving the optimal input signal and the stability of tracking control results, the tracking differentiator is used to estimate the output measurement signal. The stability of the control algorithm is also analyzed and proved. Finally, the merit of the proposed control algorithm is verified by example test. © 2024 IEEE.

关键词： Predictive control systems

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PSMFNet:Lightweight Partial Separation and Multiscale Fusion Network for Image Super-Resolution

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Computers, Materials & Continua 2024年第10期81卷 1491-1509页

作者： Shuai Cao Jianan Liang Yongjun Cao Jinglun Huang Zhishu Yang Institute of Intelligent Manufacturing GDASGuangdong Key Laboratory of Modern Control TechnologyGuangzhou510030China School of Mechanical&Automotive Engineering South China University of TechnologyGuangzhou511442China Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming650500China School of Faculty of Intelligent Manufacturing Wuyi UniversityJiangmen529020China

The employment of deep convolutional neural networks has recently contributed to significant progress in single image super-resolution(SISR)***,the high computational demands of most SR techniques hinder their applicability to edge devices,despite their satisfactory reconstruction *** methods commonly use standard convolutions,which increase the convolutional operation cost of the *** this paper,a lightweight Partial Separation and Multiscale Fusion Network(PSMFNet)is proposed to alleviate this ***,this paper introduces partial convolution(PConv),which reduces the redundant convolution operations throughout the model by separating some of the features of an image while retaining features useful for image ***,it is worth noting that the existing methods have not fully utilized the rich feature information,leading to information loss,which reduces the ability to learn feature *** by self-attention,this paper develops a multiscale feature fusion block(MFFB),which can better utilize the non-local features of an *** can learn long-range dependencies from the spatial dimension and extract features from the channel dimension,thereby obtaining more comprehensive and rich feature *** the role of the MFFB is to capture rich global features,this paper further introduces an efficient inverted residual block(EIRB)to supplement the local feature extraction ability of PSMFNet.A comprehensive analysis of the experimental results shows that PSMFNet maintains a better performance with fewer parameters than the state-of-the-art models.

关键词： Deep learning single image super-resolution lightweight network multiscale fusion

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Combined Model-Compensated and Quasi-Resonant Linear Active Disturbance Rejection control for PMSM Drives

Combined Model-Compensated and Quasi-Resonant Linear Active ...

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2023 China automation Congress, CAC 2023

作者： Chen, Songtao Mao, Xuefei Zhao, Lei Wang, Shuoyu Zhang, Jiyang School of Automation Beijing Institute of Technology Beijing100081 China Beijing Institute of Control Engineering Beijing100094 China

ISBN: (纸本)9798350303759

When operating at ultra-low speeds, Permanent Magnet Synchronous Motor (PMSM) is subject to disturbances such as non-linear frictional torque and periodic disturbance torque. These uncertain disturbances can cause fluctuations in speed, which deteriorate the speed loop performance. To address the problem, this paper proposed a linear active disturbance rejection control strategy (LADRC) that combined model-compensation and quasi-resonant control. The Stribeck model was chosen to estimate the non-linear frictional torque and input it into the linear expanding state observer (LESO) to reduce the estimation burden of LESO;The introduction of a quasi-resonant term (QR) in LESO improves the suppression of periodic harmonics in the mid-frequency band by linear active disturbance rejection control. Finally, the effectiveness of the proposed control strategy is verified by simulation experiments. © 2023 IEEE.

关键词： Friction

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Bézier-UKF Enhanced Incremental MPC Planner for Quadrotor Based Target Following 25

Bézier-UKF Enhanced Incremental MPC Planner for Quadrotor B...

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25th IEEE International Conference on Industrial Technology, ICIT 2024

作者： Zhang, Haodi Su, Jinya Li, Shihua School Of Automation Southeast University Nanjing China Southeast University Key Laboratory Of Measurement And Control Of Complex Systems Of Engineering Ministry Of Education Nanjing China

ISBN: (纸本)9798350340266

This paper presents a vision-based tracking control for a quadrotor to follow a moving target without assuming any quadrotor-target communication. Constant turn rate & acceleration model (CTRA) is first drawn to describe the target's motion, allowing to infer unmeasurable target motion variables while mitigating the adverse effects of vision sensor noises through an unscented Kalman filter (UKF). Then the target's future trajectory is firstly predicted by using a Bézier curve, which is further treated as virtual observation of the CTRA model within the prediction horizon so that a better target trajectory prediction is derived by using the proposed Bézier- UKF fusion predictor method. The outputs of Bézier- UKF predictor are input into an incremental model predictive control (IMPC) planner as the target reference, enabling the derivation of the desired position, velocity and acceleration signal for the quadrotor. These signals are implemented by the low-level SE(3) geometric controller. Finally, we conduct a comparative study of the proposed method against baseline planners such as MPC and IMPC planners in high-fidelity AirSim simulation environment, demonstrating better target following performance. © 2024 IEEE.

关键词： Model predictive control

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