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ieee symposium Series on computational intelligence (SSCI)

作者： Gordon Frost Marta Vallejo The School of Engineering and Physical Sciences Heriot-Watt University Edinburgh UK

ISBN: (数字)9781728125473

ISBN: (纸本)9781728125480

Reinforcement learning problems are often discretised, use linear function approximation, or perform batch updates. However, many applications that can benefit from reinforcement learning contain continuous variables and are inherently non-linear, for example, the control of aerospace or maritime robotic vehicles. Recent work has brought focus onto online temporal difference methods, specifically for using non-linear function approximation. In this paper, we evaluate the Forward Actor-Critic against the regular Actor-Critic, and Continuous Actor-Critic Learning automation. We also propose and evaluate a new algorithm called True Online Continuous Learning automation (TOCLA) which combines these two approaches. The chosen benchmark problem was the Mountain Car Continuous-vO environment from OpenAI Gym, which represents a further step in complexity over the benchmark used to test the Forward Actor Critic in previous works. Our results demonstrate the superiority of TOCLA in terms of its sensitivity to hyper-parameter selection compared with the Forward Actor Critic, Continuous Actor Critic Learning automation, and Actor Critic algorithms.

关键词： Approximation algorithms Benchmark testing Automobiles Genetic algorithms Learning automata Optimization Mathematical model

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A Minimum-Cost Modeling Method for Nonlinear Industrial Process Based on Multimodel Migration and Bayesian Model Averaging Method

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ieee TRANSACTIONS ON automation SCIENCE AND ENGINEERING 2020年第2期17卷 947-956页

作者： Chu, Fei Dai, Bangwu Ma, Xiaoping Wang, Fuli Ye, Bin China Univ Min & Rechnol Engn Res Ctr Intelligent Control Underground Spac Minist Educ Xuzhou 221116 Jiangsu Peoples R China State Key Lab Proc Automat Min & Met Beijing 100160 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100080 Peoples R China China Univ Min & Technol Natl Engn Res Ctr Coal Preparat & Purificat Xuzhou 221116 Jiangsu Peoples R China Xuzhou Key Lab Artificial Intelligence & Big Data Xuzhou 221116 Jiangsu Peoples R China Northeastern Univ Coll Informat Sci & Engn Shenyang 110004 Peoples R China China Univ Min & Technol Sch Informat & Control Engn Xuzhou 221116 Jiangsu Peoples R China Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China

With increasing drastic market competition, establishing an accurate and reliable performance prediction model for control and optimization at a minimum cost is a growing trend in industrial production. This article proposes a minimum-cost modeling method to develop the performance prediction model of a new nonlinear industrial process. The core idea of this approach is to migrate the useful information on multiple old and similar processes to develop a new process model. A multimodel migration strategy is proposed to migrate the useful information by combining the existing nonlinear process models and take full advantage of minimum data from the new nonlinear process. In order to obtain a set of optimal weights for combining the multiple old and similar process models, the Bayesian model averaging method is employed to estimate the contributions of each available old nonlinear process model to the new nonlinear process model. Moreover, a further experiment used nested Latin hypercube design (NLHD) to gather the necessary minimum data on the new nonlinear process for model migration. Finally, we apply the proposed minimum-cost modeling method to the new multistage centrifugal compressor in the combined cycle power plant, and the results show that the proposed method can develop an accurate compressor model at a minimal cost in terms of the amount of new process data. Note to Practitioners-Process optimal control and condition monitoring are vital for the stability and economic operation of industrial processes, and the basis of them is to quickly establish an accurate and reliable process performance prediction model. Traditional methods for developing process performance prediction models often require a large amount of complex calculations and rich process data, which is time- and cost-consuming. In particular, these methods focus only on the current process to be modeled, while ignoring the existing and similar process information, wasting process information. Th

关键词： Predictive models Data models Adaptation models Process control computational modeling Integrated circuit modeling Bayes methods Bayesian model averaging (BMA) minimum cost multimodel migration nonlinear performance prediction

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Neural-Physical Fusion Computation for Container Terminal Handling Systems by computational Logistics and Deep Learning*

Neural-Physical Fusion Computation for Container Terminal Ha...

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ieee International Conference on Systems, Man and Cybernetics

作者： Bin Li Yuqing He Wenfeng Li School of Mechanical & Automotive Engineering Fujian University of Technology Fuzhou Fujian Province China School of Transportation Fujian University of Technology Fuzhou Fujian Province China School of Logistics Engineering Wuhan University of Technology Wuhan Hubei Province China

ISBN: (纸本)9781665442084

The emerging information technology and the rising computational thinking make it possible to establish new theoretical methods and engineering practices for planning, scheduling, control and decision-making of complex logistics systems. Consequently, the complex logistics systems oriented neural-physical fusion computation (CLSO-NPFC) is proposed initially to discuss and explore the decision issues of complex logistics systems at all the strategic, tactical and operational levels. By CLSO-NPFC, the container terminal oriented logistics generalized computing mechanization, automation and intelligence are constructed uniformly and tentatively, and the typical deep learning model neural computing architecture (DLM-NCA) in CLSO-NPFC is designed for the prediction of calling liner handling volume that is measured by container units rather than twenty-feet equivalent unit. A typical regional container terminal along the coast of China is selected to implement, execute and evaluate the DLM-NCA, and the DLM-NCA shows the agile, efficient and robust performance for forecasting with the low and stable computation consumption. It demonstrates the feasibility, credibility and practicality of the abstract principles, design paradigms and computing architecture in CLSO-NPFC preliminarily.

关键词： Deep learning Solid modeling automation Processor scheduling Volume measurement Computer architecture Containers

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Tight Formulations of Unit Commitment with General Start-Up Ramp Rate Constraints

Tight Formulations of Unit Commitment with General Start-Up ...

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Sustainable Power and Energy Conference (iSPEC)

作者： Yijie Gao Xuan Zhang Yaowen Yu Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Hitachi ABB Power Grid San Jose California US

ISBN: (纸本)9781665414401

Unit commitment (UC) is a central market clearing process in wholesale electricity markets. With the increasing size and complexity of market-clearing models, UC problems become more and more complicated. To improve the computation efficiency, this paper tightens UC formulations with the general start-up ramp rate. Through analyzing their parameters over three periods, units are categorized into several categories for constructing corresponding tight formulations, and three of the categories are beyond existing works. By applying a recent systematic formulation tightening approach on top of these categories, new tight constraints of the general start-up ramp rate have been obtained. Numeral testing results illustrate the tightening process and demonstrate the improvement of computational efficiency when using our tightened formulations.

关键词： Systematics computational modeling Conferences Electricity supply industry computational efficiency Complexity theory Testing

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The variant d-path Laplacian based consensus of networked lpv multi-agent systems 7

The variant d-path Laplacian based consensus of networked lp...

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7th International Conference on Information, Cybernetics, and computational Social Systems, ICCSS 2020

作者： Fang, Rong Wang, Xiaoling Su, Housheng Xiao, Min College of Automation Nanjing University of Posts and Telecommunications Nanjing210023 China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781728162461

In this paper, the consensus problem of networked linear parameter varying (LPV) multi-agent systems (MASs) with directed long-rang interactions is investigated, where the specific communication topology of the LPV MASs is defined by the variant d-path Laplacian. Edge coupling gains based adaptive strategy or the node coupling gains based adaptive strategy is introduced in the control algorithm design to ensure the completely distributed consensus of the LPV MASs, in order for the elimination of the dependence on the topology information. Finally, numerical simulations are given to verify the obtained theoretical results. © 2020 ieee.

关键词： Multi agent systems

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Modeling and control of a Two DOF Helicopter with Tail Rotor Disturbances

Modeling and Control of a Two DOF Helicopter with Tail Rotor...

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ieee International Conference on Mechatronics, Electronics and Automotive Engineering (ICMEAE)

作者： Sanchez-Meza, Victor G. Lozano-Hernandez, Yair Gutierrez-Frias, Oscar O. Inst Politecn Nacl Unidad Profes Interdisciplinaria Ingn & Tecnol Av Cdmx Mexico Inst Politecn Nacl Escuela Super Ingn Mecan & Elect Unidad Zacatenco Cdmx Mexico

ISBN: (纸本)9781728199047

The modelling and control of a two-degrees-of-freedom helicopter with perturbations in the tail rotor are presented. Numerical simulations are presented to analyze the behavior of the PID and GPI controllers when the angle of the tail rotor is modified. The controllers were designed based on the mathematical model of a prototype developed at UPIITA-IPN. Therefore, the mechanical parameters of the system were calculated and the motors parameters were characterized. The prototype is able to rotate freely on the z (psi) axis, whereas on the y (theta) axis the rotation is limited to a range of -0.925rad to 0.925 rad. The work was carried out using the following methodology: the mathematical model and the non-linear characteristics were analyzed, the physical parameters were experimentally measured and the controllers were designed based on the obtained model. Finally, the proposed controllers were simulated.

关键词： GPI controller non-linear system rotary wing tail rotor disturbance two degrees of freedom helicopter

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Adaptive Consensus Tracking control for Nonlinear Multi-Agent Systems with Input Saturation and Dead-Zone

Adaptive Consensus Tracking Control for Nonlinear Multi-Agen...

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ieee symposium Series on computational intelligence (SSCI)

作者： Li, Shubo Pan, Yingnan Liang, Hongjing Bohai Univ Coll Engn Jinzhou 121013 Peoples R China Southeast Univ Sch Math Nanjing 210096 Peoples R China

ISBN: (纸本)9781728124858

This paper investigates the adaptive consensus tracking control problem for multi-agent systems (MASs) with input saturation and dead-zone. Radial basis function neural networks (RBF NNs) are utilized to estimate the unknown nonlinear functions. An adaptive consensus tracking control scheme is proposed by the backstepping technique and Lyapunov stability theory. The proposed controller guarantees that all the closed-loop system signals are bounded and the outputs of followers can track the leader. Finally, numerical example demonstrates the effectiveness of the obtained results.

关键词： consensus tracking control dynamic surface control multi-agent systems input saturation and dead-zone

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Research on Temperature Error Calibration Method of Fluxgate Sensor 11th

Research on Temperature Error Calibration Method of Fluxga...

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11th International symposium on computational intelligence and Industrial Applications, ISCIIA 2024

作者： Yang, Ruiping Ge, Jian Luo, Wang Hu, Xiangyun She, Jinhua Chugo, Daisuke Dong, Haobin School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China Faculty of Engineering Kwansai Gakuin University Osaka669-1330 Japan School of Geophysics and Geomatics China University of Geosciences Wuhan430074 China Hubei Subsurface Multi-scale Imaging Key Laboratory Wuhan430074 China School of Engineering Tokyo University of Technology Hachioji Tokyo192-0982 Japan

ISBN: (纸本)9789819647521

The fluxgate sensor is the most widely used sensor in vector magnetic measurement. However, during long-term continuous observation, the fluxgate sensor will produce large measurement errors due to changes in ambient temperature. This paper proposes a temperature calibration method for the fluxgate sensor based on polynomial fitting to address the temperature error of fluxgate sensors. First, the effect of temperature on the performance indicators of the fluxgate sensor was analyzed. Second, according to the existing temperature-magnetic field data, a temperature calibration model of the fluxgate sensor was constructed. And compared it with other temperature calibration method, the result shows that the proposed temperature calibration method is relatively simple and can better achieve real-time calibration for sensor application scenarios. Finally, to verify the effectiveness of the proposed method, numerous laboratory experiments were implemented. The temperature drift was reduced from about 700 nT before calibration to about 1 nT, root mean square error RMSE=11.7189, indicating that the proposed method has a good temperature calibration effect on the data measured by the fluxgate sensor within the variable temperature background. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Curve Fitting Fluxgate Sensor Temperature Calibration

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The control of Magnetic Levitation System Based on Improved Q-network

The Control of Magnetic Levitation System Based on Improved ...

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ieee symposium Series on computational intelligence (SSCI)

作者： Huang, Tao Liang, Yue Ban, Xiaojun Zhang, Jian Huang, Xianlin Harbin Inst Technol Dept Control Theory & Engn Harbin 150001 Heilongjiang Peoples R China

ISBN: (纸本)9781728124858

The magnetic system is a typical nonlinear system with the problem of uncertain parameters. Q-network is a model-free reinforcement learning method, which takes reward function as feedback and finds the optimal strategy through iterative learning. In this paper, to solve the problem, we first design the control based on the Q-network which realizes the stable control of the system without depending on the system model, and more importantly, we propose the retraining algorithm to further improve the accuracy of controller. Finally, the effectiveness of the Q-network controller in the control of a magnetic levitation system is verified by numerical simulation. The simulation results show that the network retraining algorithm can effectively reduce the steady-state error of the Q-network controller.

关键词： magnetic levitation system Q-network reinforcement learning network retraining

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Passivity of Reaction-Diffusion Neural Networks Via Sampled-Data control

Passivity of Reaction-Diffusion Neural Networks Via Sampled-...

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ieee symposium Series on computational intelligence (SSCI)

作者： Wang, Zi-Peng Wu, Huai-Ning Chen, Wu-Hua Wang, Jin-Liang Univ Jinan Sch Elect Engn Jinan 250022 Peoples R China Beihang Univ Sch Automat Sci & Elect Engn Sci & Technol Aircraft Control Lab Beijing 100191 Peoples R China Guangxi Univ Coll Math & Informat Sci Nanning 530004 Peoples R China Tianjin Polytech Univ Sch Comp Sci & Software Engn Tianjin 300387 Peoples R China

ISBN: (纸本)9781728124858

A sampled-data (SD) control is introduced for passivity of reaction-diffusion neural networks (RDNNs). With an appropriate Lyapunov functional (LF), a SD control design is developed based on linear matrix inequalities to guarantee the passivity of closed-loop RDNNs. Lastly, we provide a numerical example to support the presented method.

关键词： passivity sampled-data control reaction-diffusion neural networks linear matrix inequalities

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