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Youth Academic Annual Conference of Chinese Association of automation (YAC)

作者： Renzhi Liu Hongru Ren Hui Ma Hongyi Li Renquan Lu School of Automation Guangdong-Hong Kong Joint Laboratory for Intelligent Decision and Cooperative Control and Guangdong Provincial Key Laboratory for Intelligent Decision and Cooperative Control Guangdong University of Technology Guangzhou China School of Mathematics and Statistics Guangdong University of Technology Guangzhou China School of Electronic and Information Engineering and Chongqing Key Laboratory of Generic Technology and System of Service Robots Southwest University Chongqing China

ISBN: (数字)9798350379228

ISBN: (纸本)9798350390780

In this paper, a new reinforcement learning-based model-free adaptive control algorithm is introduced for discrete-time nonlinear multi-agent systems with unknown dynamics, while the equivalent dynamic linearization algorithm is applied to design the optimal controller. The strategy for Q-Learning and the actor-critic neural network are specifically redesigned to achieve consensus control in multi-agent systems. The proposed reinforcement learning algorithm can adjust the dynamic linearization parameters in real-time only based on input and output data. The stability of the closed-loop system is proven by Lyapunov theorem. Furthermore, the method’s effectiveness is verified by a numerical simulation.

关键词： Adaptation models Target tracking Q-learning Heuristic algorithms Numerical simulation Stability analysis Real-time systems

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On the Gaussian Filtering for Nonlinear Dynamic systems Using Variational Inference

On the Gaussian Filtering for Nonlinear Dynamic Systems Usin...

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International Conference on Information Fusion

作者： Yi Liu Xi Li Le Yang Lyudmila Mihaylova Ji Li Department of Electrical and Computer Engineering University of Canterbury Christchurch NZ State Key Lab of Complex Electromagnetic Environment Effects on Electronics and Information Systems National University of Defense Technology Changsha China Department of Automatic Control and System Engineering University of Sheffield Sheffield UK Locaris Electronic Technology Co. Ltd Zhengzhou China

ISBN: (数字)9781737749769

ISBN: (纸本)9798350371420

This paper introduces a new variational Gaussian filtering approach for estimating the state of a nonlinear dynamic system. We first assume that the predictive distribution of the state is Gaussian and derive an iterative method for updating the state posterior in the natural parameter space through KullbackLeibler divergence minimization. The obtained update rule is the same as that of the conjugate-computation variational inference technique in Bayesian learning. The derivation here is simpler and more insightful. We then impose a Wishart prior on the inverse of the state prediction covariance to take into account the impact of approximating the state predictive distribution using a Gaussian density on the state posterior estimation. The prediction covariance is identified jointly with the state using variational inference and the established state posterior update rule to achieve the desired Gaussian filtering. Simulation study examines the performance of the proposed filtering framework in target tracking based on bearing and range measurements.

关键词： Target tracking Smoothing methods Filtering Estimation Filtering algorithms Prediction algorithms Minimization Nonlinear dynamical systems Bayes methods Iterative methods

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Local to Global: A Distributed Quantum Approximate Optimization Algorithm for Pseudo-Boolean Optimization Problems

arXiv

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arXiv 2023年

作者： Yue, Bo Xue, Shibei Pan, Yu Jiang, Min Dong, Daoyi The Department of Automation Shanghai Jiao Tong University the Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Institute of Cyber-Systems and Control College of Control Science and Engineering Zhejiang University Hangzhou310027 China School of Electronics and Information Engineering Soochow University Suzhou215006 China The School of Engineering Australian National University CanberraACT2601 Australia

With the rapid advancement of quantum computing, Quantum Approximate Optimization Algorithm (QAOA) is considered as a promising candidate to demonstrate quantum supremacy, which exponentially solves a class of Quadratic Unconstrained Binary Optimization (QUBO) problems. However, limited qubit availability and restricted coherence time challenge QAOA to solve large-scale pseudo-Boolean problems on currently available Near-term Intermediate Scale Quantum (NISQ) devices. In this paper, we propose a distributed QAOA which can solve a general pseudo-Boolean problem by converting it to a simplified Ising model. Different from existing distributed QAOAs’ assuming that local solutions are part of a global one, which is not often the case, we introduce community detection using Louvian algorithm to partition the graph where subgraphs are further compressed by community representation and merged into a higher level subgraph. Recursively and backwards, local solutions of lower level subgraphs are updated by heuristics from solutions of higher level subgraphs. Compared with existing methods, our algorithm incorporates global heuristics into local solutions such that our algorithm is proven to achieve a higher approximation ratio and outperforms across different graph configurations. Also, ablation studies validate the effectiveness of each component in our method. Copyright © 2023, The Authors. All rights reserved.

关键词： Ising model

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Experimental Study on the Operational Characteristics and Coupling Relationships of Burners, Heat Exchangers, and Reformers in a Kerosene Solid Oxide Fuel Cell

SSRN

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SSRN 2024年

作者： Liu, Yechang Ning, Zhi Wang, Lintao Zheng, Xuan Sun, Chunhua Wei, Yongqi Lv, Ming School of Mechanical Electronic and Control Engineering Beijing Jiaotong University Beijing100044 China Shanghai Marine Diesel Engine Research Institute Shanghai201108 China Electric Drive System Department of BAIC Research Institute Co. Ltd Beijing China

The burner, heat exchanger, and reformer are pivotal components of the solid oxide fuel cell (SOFC) power generation system, and their operational characteristics have a direct impact on the system's ability to achieve efficient and stable power output. This study has established an experimental platform for a kerosene SOFC system to investigate the operational characteristics and coupling relationships of the burner, heat exchanger, and reformer. The research findings indicate that the burner operates stably with an excess air ratio of 39.4, but experiences frequent extinguishing when the methane flow rate falls below 4 SLM. Reducing both methane and water flow rates leads to a more significant temperature decrease in the burner and combined heat exchanger compared to reducing methane flow alone. Increasing the kerosene flow rate by 15% causes a 2.5% change in the outlet temperature of the burner, a 2.15% change in the air heat exchanger's exhaust gas input temperature, and a 1.47% change in the flue gas inlet and outlet pressure drop. However, due to the presence of the heater, there is no significant variation in the input and exit temperatures of the reformer. By elevating the reformer temperature and steam-to-carbon ratio, it is possible to bring the hydrogen volume fraction after kerosene reforming closer to that after methane reforming, and even lower than that after methane reforming. The electric heater serves as a means of thermal compensation, mitigating the intricate coupling effects among the reformer, burner, and heat exchanger. © 2024, The Authors. All rights reserved.

关键词： Methane

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controller Synthesis of Signal Temporal Logical Tasks for Cyber-Physical Production systems via Acyclic Decomposition

Controller Synthesis of Signal Temporal Logical Tasks for Cy...

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IEEE Conference on Decision and control

作者： Shuling Wang Shanying Zhu Cailian Chen Liang Xu Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China Institute of Artificial Intelligence Shanghai University Shanghai China

Modularization facilitates the adaptability of cyber-physical production systems (CPPSs) with a variety of collaborative tasks. Various production rules can be captured by signal temporal logic (STL) specifications imposed on interconnected multi-agent systems (MASs). In this paper, we focus on the controller synthesis of STL tasks for interconnected MASs to accomplish collaborative tasks in modular CPPSs. Firstly, a class of STL specifications characterizing tasks by the combination of fixed-time reachability and finite-time persistence tasks is proposed, which encompasses a large class of production specifications for the MAS. Secondly, the acyclic decomposition of the global STL formula is constructed to enable conflict-free collaborative tasks and unidirectional couplings between subsystems. By establishing the equivalence between the proposition and the state set of the MAS, necessary and sufficient conditions are respectively proposed for the satisfaction of reachability and persistence tasks. In addition, an algorithm is presented to synthesize controllers for the MAS with the global STL specification based on local controllers of subsystems. An illustrative example is given to show the effectiveness of the proposed method.

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Study on the Improved Water Flow Prediction Based on Classification-Regression Approach for Amphibious Spherical Robots

Study on the Improved Water Flow Prediction Based on Classif...

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IEEE International Conference on Mechatronics and automation

作者： Jun Leng Shuxiang Guo Chunying Li Sheng Cao School of Control Science and Engineering Shandong University Jinan China The Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China The Aerospace Center Hospital School of Life Science and the Key Laboratory of Convergence Medical Engineering System and Healthcare Technology Ministry of Industry and Information Technology Beijing Institute of Technology Beijing China

ISBN: (数字)9798350388077

ISBN: (纸本)9798350388084

Inspired by the lateral line systems found in aquatic organisms, an artificial lateral line system (ALLS) for underwater robots was developed. This technology was integrated into Autonomous Underwater Vehicles (AUVs) to enhance their environmental awareness and navigation. This paper was focused on improving water flow prediction performance for a amphibious spherical robot (ASR) equipped with a ALLS comprising 12 pressure sensors. To address the challenge of the initial method relying on polynomial fitting based on data from a single sensor, which encounters difficulties when the flow direction falls between sensor angles. Two strategies were explored: increasing the quantity of sensors and implementing a Classification-Regression approach. Experimental simulations using Computational Fluid Dynamics (CFD) software were conducted to investigate the effectiveness of these strategies. Experimental results indicated that the classification-regression method significantly enhanced prediction accuracy compared to the original approach and offered a cost-effective alternative to increasing sensor quantity. This research contributed to the advancement of underwater robotics by enhancing perception capabilities, which were crucial for navigating challenging underwater environments efficiently.

关键词： Autonomous underwater vehicles Analytical models Accuracy Navigation Computational fluid dynamics Fitting Robot sensing systems

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Experimental Study on the Operational Characteristics and Coupling Relationships of Burners, Heat Exchangers, and Reformers in a Kerosene Solid Oxide Fuel Cell

SSRN

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SSRN 2024年

关键词： Methane

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Survey on Large Language Model-Enhanced Reinforcement Learning: Concept, Taxonomy, and Methods

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IEEE Transactions on Neural Networks and Learning systems 2024年第6期36卷 9737-9757页

作者： Yuji Cao Huan Zhao Yuheng Cheng Ting Shu Yue Chen Guolong Liu Gaoqi Liang Junhua Zhao Jinyue Yan Yun Li Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong SAR China Department of Building Environment and Energy Engineering The Hong Kong Polytechnic University Hong Kong China School of Science and Engineering The Chinese University of Hong Kong Shenzhen China Center for Crowd Intelligence Shenzhen Institute of Artificial Intelligence and Robotics for Society (AIRS) Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China School of Electrical and Electronic Engineering Nanyang Technological University Jurong West Singapore School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen China i4AI Ltd. London U.K.

With extensive pretrained knowledge and high-level general capabilities, large language models (LLMs) emerge as a promising avenue to augment reinforcement learning (RL) in aspects, such as multitask learning, sample efficiency, and high-level task planning. In this survey, we provide a comprehensive review of the existing literature in LLM-enhanced RL and summarize its characteristics compared with conventional RL methods, aiming to clarify the research scope and directions for future studies. Utilizing the classical agent-environment interaction paradigm, we propose a structured taxonomy to systematically categorize LLMs’ functionalities in RL, including four roles: information processor, reward designer, decision-maker, and generator. For each role, we summarize the methodologies, analyze the specific RL challenges that are mitigated and provide insights into future directions. Finally, the comparative analysis of each role, potential applications, prospective opportunities, and challenges of the LLM-enhanced RL are discussed. By proposing this taxonomy, we aim to provide a framework for researchers to effectively leverage LLMs in the RL field, potentially accelerating RL applications in complex applications, such as robotics, autonomous driving, and energy systems.

关键词： Taxonomy Surveys Visualization Reviews Planning Reinforcement learning Games Transformers Natural language processing Large language models

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Variable Frequency Phase-Shift Modulation Technique for Single Stage Dual-Active-Bridge AC-DC Converter

Variable Frequency Phase-Shift Modulation Technique for Sing...

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International Conference on Power electronics (ICPE)

作者： Dongxin Guo Panbao Wang Chunguang Ren Josep M. Guerrero School of Electrical Engineering and Automation Harbin Institute of Technology Harbin China Shanxi Key Lab. of Power System Operation and Control Taiyuan University of Technology Taiyuan China Department of Energy Technology Aalborg University Aalborg Denmark

This article proposes a variable frequency phase-shift modulation technique for single stage dual-active-bridge (DAB) AC-DC converter. This isolated single-stage single-phase converter consists of a DAB and a full bridge (FB) inverter. The proposed strategy applies a composite control strategy combining frequency modulation and extended phase shift (EPS) method to overcome the drawback of the non-linear power control of traditional phase-shift control scheme, which significantly reduces the complexity of the controller algorithm. Moreover, the synchronous inverter control applied to the back-end FB inverter is designed, which can realize the energy conversions in a single-stage, and a high frequency (HF) filter capacitor can replace the electrolytic capacitor at the output side DC-link. Thus, the safety of system operation improves and the power density of the system increases. Finally, simulation results confirm the validity and practical feasibility of the proposed control strategy.

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Nonlinear vibration response characteristics of a dual-rotor-bearing system with squeeze film damper

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Chinese Journal of Aeronautics 2021年第10期34卷 128-147页

作者： Xinxing MA Hui MA Haiqin QIN Xumin GUO Chenguang ZHAO Mingyue YU School of Mechanical Engineering and Automation Northeastern UniversityShenyang J10819China Institute of Vibration Shock and NoiseShanghai Jiao Tong UniversityShanghai 200240China Key Laboratory of Vibration and Control of Aero-Propulsion System Ministry of Education Northeastern UniversityShenyang 110819China Department of Aeronautical Mechanics Naval Aeronautical and Astronautical University Qingdao CampusQingdao 266041China

Rolling bearing and Squeeze Film Damper(SFD)are used in rotor support structures,and most researches on the nonlinear rotor-bearing system are focused on the simple rotor-bearing *** work emphasizes the comparative analysis of the influence of SFD on the nonlinear dynamic behavior of the dual-rotor system supported by rolling ***,a reduced dynamic model is established by combining the Finite Element(FE)method and the freeinterface method of component mode *** proposed model is verified by comparing the natural characteristics obtained from an FE model with those from the ***,the steady-state vibration responses of the system with or without SFD are solved by the numerical integration *** influences of the ball bearing clearance,unbalance,centralizing spring stiffness and oil film clearance of SFD on the nonlinear steady-state vibration responses of the dual-rotor system are *** show that SFD can effectively suppress the amplitude jump of the dual rotor system sustaining two rotors unbalance *** the ball bearing clearance or unbalance increases,the amplitude jump phenomenon becomes more obvious,the resonance hysteresis phenomenon strengthens or weakens,the resonant peaks shift to the left or the right,*** with unreasonable parameters will aggravate the system vibration,the smaller the oil film clearance,the better the damping performance of the SFD,the larger the centralizing spring stiffness is,the larger resonance amplitudes are.

关键词： dual-rotor rolling bearing squeeze film damper finite element method vibration response

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