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第37届中国控制会议

作者： Chao Gan Weihua Cao Kangzhi Liu School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Department of Electrical and Electronic Engineering Chiba University

Operational drilling parameters optimization is necessary for increase drilling efficiency in complex geological drilling process. There are four key objectives to evaluate the drilling efficiency, including drilling cost, rate of penetration（ROP）, drill bit life, and drill bit’s specific energy. In this paper, we proposed a multi-objective optimization method to improve drilling efficiency in the complex geological drilling process considering all the four key objectives. Firstly, the characteristics of complex geological drilling process and optimization problems are analyzed to find the vital process parameters and problems for drilling efficiency optimization. Then, a multi-objective optimization model which combines the four key objectives is ***, an improved nondominated sorting genetic algorithm（improved NSGA-II） is used to optimize the operational drilling parameters include weight on bit（WOB） and rotational speed（RS） to make drilling efficiency improved. The real case results demonstrate that our method increases the drilling efficiency in four key objectives and saves the simulation time. The proposed method provides the foundation for intelligent optimization control in complex geological drilling process.

关键词： Operational Drilling Parameters Multi-objective Optimization Complex Geological Drilling Process Drilling Efficiency Improved NSGA-II

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Supervisory Robust Model Predictive control for Linear systems Subject to Parametric Uncertainty and Application to Level control Process

Supervisory Robust Model Predictive Control for Linear Syste...

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International Conference on electrical engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON

作者： Pongsorn Keadtipod David Banjerdpongchai Dept. of Electrical Engineering Chulalongkorn University Intelligent Control Automation of Process Systems (iCAPS) Research Unit Bangkok Thailand

ISBN: (纸本)9781538635568;9781538635551

This paper presents supervisory robust model predictive control (RMPC) for linear systems in the presence of parametric uncertainty. The control structure is cascade control where the outer loop controller is RMPC and the inner loop controller as proportional-integral (PI) controller. The parametric uncertainty appears in the dynamic matrix and input matrix and satisfies norm-bounded condition. First, we derive inner closed-loop model and use this model to design RMPC. The design of RMPC involves a min-max optimization problem which aims to find the optimal solution minimizing the worst case performance. We derive an upper bound of the worst case performance and apply Linear Matrix Inequality to determine the control input. We conduct the step tests on the level control loop and obtain 4 dynamic models. Numerical results reveal that supervisory RMPC can ensure reference tracking regardless of uncertain dynamics.

关键词： Process control Uncertainty Level control Computational modeling Linear systems Telecommunications Information technology

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Foundation models and intelligent decision-making: Progress, challenges, and perspectives

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Innovation 2025年第6期6卷 100948页

作者： Huang, Jincai Xu, Yongjun Wang, Qi Wang, Qi [Cheems] Liang, Xingxing Wang, Fei Zhang, Zhao Wei, Wei Zhang, Boxuan Huang, Libo Chang, Jingru Ma, Liantao Ma, Ting Liang, Yuxuan Zhang, Jie Guo, Jian Jiang, Xuhui Fan, Xinxin An, Zhulin Li, Tingting Li, Xuefei Shao, Zezhi Qian, Tangwen Sun, Tao Diao, Boyu Yang, Chuanguang Yu, Chenqing Wu, Yiqing Li, Mengxian Zhang, Haifeng Zeng, Yongcheng Zhang, Zhicheng Zhu, Zhengqiu Lv, Yiqin Li, Aming Chen, Xu An, Bo Xiao, Wei Bai, Chenguang Mao, Yuxing Yin, Zhigang Gui, Sheng Su, Wentao Zhu, Yinghao Gao, Junyi He, Xinyu Li, Yizhou Jin, Guangyin Ao, Xiang Zhai, Xuehao Tan, Haoran Yun, Lijun Shi, Hongquan Li, Jun Fan, Changjun Huang, Kuihua Harrison, Ewen Leung, Victor C.M. Qiu, Sihang Dong, Yanjie Zheng, Xiaolong Wang, Gang Zheng, Yu Wang, Yuanzhuo Guo, Jiafeng Wang, Lizhe Cheng, Xueqi Wang, Yaonan Yang, Shanlin Fu, Mengyin Fei, Aiguo Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Department of Automation Tsinghua University Beijing100084 China Huazhong University of Science and Technology Wuhan430074 China School of Automation Beijing Institute of Technology Beijing100081 China School of Information Science and Engineering Dalian Polytechnic University Dalian116034 China National Engineering Research Center for Software Engineering Peking University Beijing100871 China Department of Oral Implantology Peking University School and Hospital of Stomatology Beijing100081 China Guangzhou511453 China College of Information and Electrical Engineering China Agricultural University Beijing100083 China IDEA Research International Digital Economy Academy Shenzhen518057 China Institute of Automation Chinese Academy of Sciences Beijing100190 China The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China College of Science National University of Defense Technology Changsha410073 China Center for Systems and Control College of Engineering Peking University Beijing100871 China Gaoling School of Artificial Intelligence Renmin University of China Beijing100872 China College of Systems Engineering National University of Defense Technology Changsha410073 China Laboratory for Big Data and Decision National University of Defense Technology Changsha410073 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore School of Electrical Engineering Chongqing University Chongqing400044 China Department of Mathematics The University of Hong Kong Hong Kong SAR999077 China School of Food Science and Technology Dalian Polytechnic University Dalian116034 China Centre for Medical Informatics University of Edinburgh EdinburghEH16 4UX United Kingdom Department of Stomatology Peking Union Medical College Hospital Chinese Academ

Intelligent decision-making (IDM) is a cornerstone of artificial intelligence (AI) designed to automate or augment decision processes. Modern IDM paradigms integrate advanced frameworks to enable intelligent agents to make effective and adaptive choices and decompose complex tasks into manageable steps, such as AI agents and high-level reinforcement learning. Recent advances in multimodal foundation-based approaches unify diverse input modalities—such as vision, language, and sensory data—into a cohesive decision-making process. Foundation models (FMs) have become pivotal in science and industry, transforming decision-making and research capabilities. Their large-scale, multimodal data-processing abilities foster adaptability and interdisciplinary breakthroughs across fields such as healthcare, life sciences, and education. This survey examines IDM's evolution, advanced paradigms with FMs and their transformative impact on decision-making across diverse scientific and industrial domains, highlighting the challenges and opportunities in building efficient, adaptive, and ethical decision systems. © 2025 The Authors

关键词： Reinforcement learning

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Stability Analysis of Discrete T-S Fuzzy systems with Time-Varying Delay Using an Extended Reciprocally Convex Matrix Inequality

Stability Analysis of Discrete T-S Fuzzy Systems with Time-V...

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第37届中国控制会议

作者： Bi-Ying Chen Fei Long Chuan-Ke Zhang Yong He Hui-Qin Xiao School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems College of Electrical and Information Engineering Hunan University of Technology

This paper concerns the stability analysis of discrete Takagi-Sugeno fuzzy systems with time-varying delays.A suitable Lyapunov-Krasovskii functional is constructed,and an extended reciprocally convex matrix inequality is used to estimate the forward difference of the *** a result,a less conservative stability criterion is ***,a numerical example is given to demonstrate the advantage of the proposed method.

关键词： Discrete T-S fuzzy systems time-varying delay stability analysis extended reciprocally convex matrix inequality

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Closed Loop Fault Detectability Based on a Gap-Metric 2

Closed Loop Fault Detectability Based on a Gap-Metric

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2nd IEEE Conference on control Technology and Applications, CCTA 2018

作者： Sekunda, Andre Ding, Steven Kjolstad Poulsen, Niels Niemann, Henrik Department of Electrical Engineering Automation and Control Tech. University of Denmark Kgs. LyngbyDk-2800 Denmark Dept. of Automatic Control and Complex Systems University of Duisburg-Essen Duisburg47057 Germany Dept. of Applied Mathematics and Computer Science Tech. University of Denmark Kgs. LyngbyDk-2800 Denmark

ISBN: (纸本)9781538676981

systems are often controlled using feedback loops. Fault diagnosis schemes are usually designed assuming that there is no feedback loop. Therefore fault diagnosis methods need to accommodate for the feedback loop. One such method is active fault diagnosis based on a fault signature system. This method is derived using the YJBK parametrisation, named after Youla, Jabr, Bongiorno and Kucera. It uses the derived fault signature system to determine the detectability of possible faults in the system. Deriving the fault signature system requires knowledge about the controller. This paper demonstrates the possibility of estimating the detectability of faults in the fault signature system using a gap-metric. The gap-metric has the advantage of only requiring knowledge about the plant. By using the gap-metric it is possible to estimate the detectability of faults without using information about the controller. © 2018 IEEE.

关键词： Fault detection

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Adaptive-Learning-Based H∞ Optimal Tracking control Design for a Class of Constrained Nonlinear systems

Adaptive-Learning-Based H∞ Optimal Tracking Control Design ...

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第37届中国控制会议

作者： Jiaxu Hou Ding Wang Derong Liu School of Automation and Electrical Engineering University of Science and Technology Beijing The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences School of Computer and Control Engineering University of Chinese Academy of Sciences School of Automation Guangdong University of Technology

This paper, considering constrained inputs, focuses on designing the optimal controller for discrete-time H∞ tracking control problems. The neural-network-based iterative learning algorithm is deemed to be an excellent method to work. The iterative heuristic dynamic programming algorithm is employed for developing the formulated regulation of the tracking error. Strict convergence guarantees are given to support the optimality of the adaptive algorithm. A neural-network-based training scheme is adopted to implement the learning algorithm and stabilize the system in the optimal manner. Two numerical examples are provided to illustrate the applicability and good performance of the designed tracking control scheme.

关键词： Adaptive dynamic programming H∞ tracking control constraints convergence proof

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ADP-Based Model Reference Adaptive control Design for Unknown Discrete-Time Nonlinear systems

ADP-Based Model Reference Adaptive Control Design for Unknow...

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第37届中国控制会议

作者： Wei Wang Xin Chen Fang Wang Hao Fu School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Electrical and Information Engineering Zhengzhou University of Light Industry

In engineering applications, it is very useful to make a nonlinear system behave like a linear stable system. However,how to design a model reference adaptive control（MRAC） as the nonlinear dynamics is unknown is the key problem. In this study,an MRAC with adaptive dynamic programming（ADP） algorithm is presented for discrete-time nonlinear unknown dynamic systems, in which a multi-layer neural network（NN） model is utilized to describe the nonlinear system’s dynamics. Then based on this approximate model, a feedforward neuro-controller is developed as the desired control corresponding to the reference input. Third, the iterative ADP algorithm, in the form of actor-critic framework, employs two NNs to estimate the action value function, and generates the feedback control which, together with the feedforward neuro-controller, makes the state of the system track the reference model. Finally the feasibility of the new approach is verified by two numerical experiments.

关键词： Model reference adaptive control Adaptive dynamic programming Nonlinear system Q-function

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DC-DC Buck-Boost Converters with Unknown CPL: An Adaptive PBC

DC-DC Buck-Boost Converters with Unknown CPL: An Adaptive PB...

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2018 Annual American control Conference, ACC 2018

作者： He, Wei Abraham Soriano-Rangel, Carlos Ortega, Romeo Astolfi, Alessandro Mancilla-David, Fernando Li, Shihua Ministry of Education School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Nanjing210096 China University of Colorado Denver Department of Electrical Engineering Denver United States Laboratoire des Signaux et Systemes Supelec Plateau du Moulon Gif-sur-Yvette91192 France Department of Electrical and Electronic Engineering Imperial College London LondonSW72AZ United Kingdom

ISBN: (纸本)9781538654286

We develop in this paper an adaptive passivity-based controller for output voltage regulation of DC-DC buck-boost converter with unknown constant power load. This control problem is theoretically challenging since the average model of the converter is a bilinear second order system that, due to the presence of the constant power load, is non-minimum phase with respect to both states. A solution to the problem was recently reported in [9], however, the resulting control law is extremely complicated to be of practical interest. The aim of the paper is to design a novel, significantly simpler, controller that can be easily implemented in applications. The key modifications introduced in the new design are the use of a change of coordinates and a partial linearization that transform this system into a cascade form, to which an adaptive energy-shaping controller is applied. Another merit of the designed control law, besides its simplicity, lies in that it is amenable for the addition of an outer-loop PI that improves its transient and disturbance rejection performances. Simulations and experimental results are provided to assess the improved performance of the proposed controller. © 2018 AACC.

关键词： Mathematical model Load modeling Asymptotic stability Voltage control Adaptation models Adaptive systems Stability analysis"

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Peak regulation strategy of regional power systems for large-scale renewable generation accommodation 2

Peak regulation strategy of regional power systems for large...

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2nd IEEE International Conference on Energy Internet, ICEI 2018

作者： Meng, Tianhui Qian, Minhui Zhao, Dawei Chen, Ning Wang, Ningbo Zhang, Wei State Key Laboratory of Operation and Control of Renewable Energy and Storage Systems China EPRI Nanjing210003 China School of Electrical Engineering and Automation Harbin Institute of Technology Harbin150001 China Wind Generation Technology Center State Grid Gansu Electric Power Company Lanzhou730050 China

ISBN: (纸本)9781538641316

The integration of large-scale renewable energy has brought great challenges for the control and operation of power systems. In order to accommodate the renewable power as much as possible, the system operators should properly coordinate the conventional generators such hydro and thermal units in the system. In this paper, a peak regulation strategy is proposed for the control of multiple heterogeneous generators to maximize the renewable energy usage ratio in a regional power system. To this end, first the hydro generators is utilized to alleviate the stress for peak regulation due to dramatic fluctuation of the renewable power. Then an optimization model is established with objective function being maximizing the peak regulation capability of the system in a weekly period. The obtained optimal solution of the model can ensure the peak regulation requirement of the system during the whole planning-period while avoiding the frequent switch of on-off operation for the thermal units within the system. © 2018 IEEE.

关键词： Renewable energy resources

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Discrete-time Optimal Zero-sum Games for Nonlinear systems via Adaptive Dynamic Programming 6

Discrete-time Optimal Zero-sum Games for Nonlinear Systems v...

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2017 IEEE 6th Data Driven control and Learning systems Conference (DDCLS’17)

作者： Qinglai Wei Ruizhuo Song Yancai Xu Derong Liu Qiao Lin The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences School of Automation and Electrical Engineering University of Science and Technology Beijing

ISBN: (纸本)9781509054626

In this paper, a novel discrete-time iterative zero-sum adaptive dynamic programming(ADP) algorithm is developed for solving the optimal control problems of nonlinear systems. Two iteration processes, which are lower and upper iterations, are employed to solve the lower and upper value functions, respectively. Arbitrary positive semi-definite functions are acceptable to initialize the upper and lower iterations of the iterative zero-sum ADP algorithm. It is proven that the upper and lower value functions converge to the optimal performance index function if the optimal performance index function exists, where the existence criterion of the optimal performance index function is unnecessary. Simulation examples are given to illustrate the effective performance of the present method.

关键词： Approximate dynamic programming adaptive dynamic programming zero-sum game optimal control

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