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63rd IEEE Conference on Decision and control, CDC 2024

作者： Guan, Yuxiang Salizzoni, Giulio Kamgarpour, Maryam Summers, Tyler H. University of Texas at Dallas Control Optimization and Networks Lab United States Systems Control and Multi-Agent Optimization Research Lab Epfl Switzerland

ISBN: (纸本)9798350316339

We present a policy iteration algorithm for the infinite-horizon N -player general-sum deterministic linear quadratic dynamic games and compare it to policy gradient methods. We demonstrate that the proposed policy iteration algorithm is distinct from the Gauss-Newton policy gradient method in the N -player game setting, in contrast to the single-player setting where under suitable choice of step size they are equivalent. We illustrate in numerical experiments that the convergence rate of the proposed policy iteration algorithm significantly surpasses that of the Gauss-Newton policy gradient method and other policy gradient variations. Furthermore, our numerical results indicate that, compared to policy gradient methods, the convergence performance of the proposed policy iteration algorithm is less sensitive to the initial policy and changes in the number of players. © 2024 IEEE.

关键词： Numerical methods

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A Distributed Algorithm for Large-Scale Multi-Agent MINLPs 63

A Distributed Algorithm for Large-Scale Multi-Agent MINLPs

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Dong, Sheng Xia, Weiguo Dalian University of Technology Key Lab. of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education School of Control Science and Engineering China

ISBN: (纸本)9798350316339

In this paper, we focus on the optimization of large-scale multi-agent systems, where agents collaboratively optimize the sum of local objective functions through their own continuous and/or discrete decision variables, subject to global coupling constraints and local constraints. The resulting Mixed-Integer Nonlinear Programmings (MINLPs) are NP-hard, non-convex, and large-scale. Therefore, this paper aims to design distributed algorithms to find feasible suboptimal solutions with a guaranteed bound. To this end, considering dual decomposition as an effective method to decompose large-scale constraint-coupled optimization problems, we first show, based on the convexification effects of large-scale MINLPs, that the primal solutions from the dual are near-optimal under certain conditions. This expands recent results in Mixed-Integer Linear Programmings (MILPs) to the nonlinear case but requires additional efforts on the proof. Utilizing this result to tighten the coupling constraints, we develop a fully distributed algorithm for the tightened problem, based on dual decomposition and consensus protocols. The algorithm is guaranteed to provide feasible solutions for the original MINLP. Moreover, asymptotic suboptimality bounds are established for the obtained solution. Finally, the efficacy of the method is verified through numerical simulations. © 2024 IEEE.

关键词： Mixed-integer linear programming

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A Policy Iteration Algorithm for N-player General-Sum Linear Quadratic Dynamic Games

A Policy Iteration Algorithm for N-player General-Sum Linear...

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IEEE Conference on Decision and control

作者： Yuxiang Guan Giulio Salizzoni Maryam Kamgarpour Tyler H. Summers Control Optimization and Networks Lab University of Texas at Dallas Systems Control and Multi-Agent Optimization Research Lab EPFL

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

关键词： Gradient methods Heuristic algorithms Games Nash equilibrium Convergence

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Mechanistic data-driven combined modelling and predictive control method for coal-fired power unit 5

Mechanistic data-driven combined modelling and predictive co...

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5th International Conference on Industrial Artificial Intelligence, IAI 2023

作者： Hu, Jingyu Han, Zhongyang Zhao, Jun Wang, Wei Dalian University of Technology Key Lab. of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education School of Control Science and Engineering Liaoning 116024 China

ISBN: (纸本)9798350325294

With the increase of the proportion of new energy sources connected to the grid, challenges are also emerged for modeling and control of the conventional coal-fired power units to increase their operational flexibility of deep peak-regulation. Traditional data-driven modelling (DDM) requires a large amount of training data containing a variety of operating conditions, which are hard to be fully satisfied in engineering practice. This paper investigates the mechanics and dynamic characteristics of boiler-turbine systems in the coal-fired power unit, and proposes a mechanistic data-driven combined modelling (M-DDM) method. It not only requires less amount of data comparing with the DDM based method, but also covers a wide range of operating conditions by considering the dynamic characteristics of the coal-fired power unit. A model predictive control (MPC) based method was subsequently designed as the control vehicle. In order to improve the computing efficiency and reduce the probability of model mismatch considering the involved ordinary differential equations (ODEs) and nonlinearities. reinforcement learning is embedded into the MPC. Finally, simulations are conducted for demonstrating the superior performance of the proposed methods comparing with other commonly deployed modeling and control methods. © 2023 IEEE.

关键词： Model predictive control

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Preclinical Assessment of Upper Limb Tremor in Parkinson's Disease with Deep Learning and Wearable Technology

Preclinical Assessment of Upper Limb Tremor in Parkinson's D...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Lin, Fang Wang, Zhelong Qiu, Sen Zhao, Hongyu School of Control Science and Engineering Dalian University of Technology Key Lab. of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Liaoning Dalian China

ISBN: (纸本)9798350337020

Tremors are typically experienced by patients at the beginning of Parkinson's disease (PD). Clinicians evaluate clinical symptoms based on scale and experience, but mild tremors do not have significant characteristics and are difficult to observe with the naked eye. Implementing an intelligent and objective method to identify PD patients with early tremor symptoms and healthy controls (HC) is necessary. This study used wearable sensors to collect 9-axis inertial signals and 2-channel sEMG signals at the wrists of 13 HC and 24 PD patients from Dalian Municipal Central Hospital. Based on a Long Short-Term Memory Network (LSTM), an attention mechanism, and a Fully Convolutional Network (FCN), we develop a model to classify and recognize data from PD patients. Compared the proposed method with several classification methods, the results showed that the proposed method achieved higher classification accuracy (91.78%), precision (100%), recall (87.50%), and F1-score (93.33%) of PD class than Support Vector Machine, FCN, and LSTM. The computing time of the proposed method is approximately 1 second. The proposed method identifies early PD patients by pre-clinical assessment of mild upper limb tremors, which is valuable for early treatment and rehabilitation. © 2023 IEEE.

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BEHAVIORAL BASED DECISION-MAKING SCHEME FOR COOPERATIVE CONNECTED AUTOMATED MOBILITY 9

BEHAVIORAL BASED DECISION-MAKING SCHEME FOR COOPERATIVE CONN...

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9th International Conference on Big Data Analytics, Data Mining and Computational Intelligence, BigDaCI 2024, the 10th International Conference on Connected Smart Cities, CSC 2024 and the 16th International Conference on e-Health, EH 2024, Part of the 18th Multi Conference on Computer Science and Information Systems 2024, MCCSIS 2024

作者： Yagüe-Cuevas, David Marín-Plaza, Pablo Paz-Sesmero, María Sanchis, Araceli Intelligent Systems Lab. University Carlos III Madrid Spain Control Learning and Systems Optimization Group University Carlos III Madrid Spain

ISBN: (纸本)9789898704597

For some time now, the automotive industry has been undergoing major adjustments in recent years. Current technological solutions aimed to achieve an autonomous level 5 platform implement increasingly complex software and hardware systems. On the one hand, high-tech fields such as smart cities, the Internet of Things (IoT), and intelligent transportation systems (ITS) will benefit from connected and cooperative vehicles in the future. On the other hand, the more complex these systems are, the more knowledge, computing power and precision they require, and the more difficult they become to debug. There are still several challenges in the field of autonomous vehicles to address. From sensor perception to system localization, through arbitration and decision making. The goal of this paper is to address some of the problems regarding those latter fields by aggregating all the structured traffic knowledge extracted from the vehicle context. This knowledge will be used to deploy an effective decision-making system based on behaviors capable of choosing what to do when certain conditions are met. Actions such as changing its operation mode, selecting the most suitable trajectory or even adjusting the systems to the current situation for a better automated vehicle experience. © 2024 Big Data Analytics, Data Mining and Computational Intelligence. All Rights Reserved.

关键词： Automotive industry

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Scheduling optimization of Coal Washing Process Based on Two-Stage Fuzzy Heuristic Algorithm 13

Scheduling Optimization of Coal Washing Process Based on Two...

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

作者： Zhou, Hui Wang, Zhiyuan Han, Zhongyang Wang, Linqing Zhao, Jun Wang, Wei Dalian University of Technology Key Lab. of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian116024 China School of Control Science and Engineering Dalian University of Technology Dalian116024 China

ISBN: (纸本)9798350361674

Coal washing and sorting plays a vital role for coal production. Reasonable scheduling of washing and sorting equipment, crushers and various coal silos in the coal washing and sorting process can effectively increase the total value of product coal and reduce the total energy consumption of equipment. In this paper, a multi-constrained nonlinear mixed integer programming model considering the start and stop of each equipment in the washing process is established to realize the specific regulation of the washing process. In order to overcome the problem such as local convergence when solving high-dimensional space problems, a two-stage fuzzy heuristic algorithm is proposed in this paper. In the first stage, a variety of population genetic algorithms based on fuzzy adaptive strategy control are designed for global search to establish an elite population bank to preserve high-quality individuals produced by various groups. In the second stage, an improved simulated annealing algorithm is used to perform a local search with a backtracking process on specific neighbors of individuals in the elite population bank, which can improve the stability of the algorithm and reduce the situation of falling into local convergence. Then the real data of a Chinese coal mining company was used for experiments, where the proposed algorithm is compared with other commonly used methods. The experimental results demonstrate that the proposed algorithm can increase the total coal value of the washed product by at least 1.7% compared to other algorithms, and the average total number of starts and stops of the equipment is reduced by about 7.3%, which are effectively beneficial for the related enterprise. © 2024 IEEE.

关键词： Coal mines

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A Novel Point Cloud Deep Learning Method for Predicting Variable Airfoil Flow Fields 43

A Novel Point Cloud Deep Learning Method for Predicting Vari...

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43rd Chinese control Conference, CCC 2024

作者： Zhang, Xinyao Cao, Junzhe Dalian University of Technology Control Science and Engineering Dalian116024 China Dalian University of Technology Key Lab. of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian116024 China

ISBN: (纸本)9789887581581

Airfoil flow field is of great significance to the design optimization of aircraft shape. Convolutional Neural Networks (CNNs) have been applied in flow field prediction studies, which greatly reduce the cost of computation time in traditional Computational Fluid Dynamics (CFD) methods. However, such CNN-based approaches normally bring extra errors due to the procedure of interpolating irregular grid data into Cartesian grids, which may lead to underperforming prediction accuracy. In this paper, an improved PointNet++ point cloud deep learning method was proposed for the airfoil flow field prediction. The unstructured grid vertices in the CFD domain were treated as point cloud as well as the input of neural network, and only the original information of the data was adopted to avoid interpolation. Neighborhood relationship features were defined and spliced with neighborhood point features, and the features were integrated by simultaneously solving for the maximum, minimum, sum, and average values to extract the local or global features of the system to enhance the performance of deep network for extracting the information. Finally, the proposed network was trained and tested by using 549 sets of flow field data formed by 61 airfoils under 9 angle-of-attack conditions, and further predicted for an additional 32 datasets with different airfoils and conditions. The simulation results show that the method proposed in this study gets lower prediction errors in terms of pressure, Mach number, x-direction velocity, and y-direction velocity than the two other current point cloud deep networks, PointNet and PointNet++. At the same time, the computation speed of this method is about 14 times faster than the traditional CFD method. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Computational fluid dynamics

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A Policy Iteration Algorithm for N-player General-Sum Linear Quadratic Dynamic Games

arXiv

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arXiv 2024年

作者： Guan, Yuxiang Salizzoni, Giulio Kamgarpour, Maryam Summers, Tyler H. The Control Optimization and Networks Lab University of Texas at Dallas United States The Systems Control and Multi-Agent Optimization Research Lab EPFL Switzerland

We present a policy iteration algorithm for the infinite-horizon N-player general-sum deterministic linear quadratic dynamic games and compare it to policy gradient methods. We demonstrate that the proposed policy iteration algorithm is distinct from the Gauss-Newton policy gradient method in the N-player game setting, in contrast to the single-player setting where under suitable choice of step size they are equivalent. We illustrate in numerical experiments that the convergence rate of the proposed policy iteration algorithm significantly surpasses that of the Gauss-Newton policy gradient method and other policy gradient variations. Furthermore, our numerical results indicate that, compared to policy gradient methods, the convergence performance of the proposed policy iteration algorithm is less sensitive to the initial policy and changes in the number of players. Copyright © 2024, The Authors. All rights reserved.

关键词： Consensus algorithm

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Robust Performance Analysis of Wind Turbine control Systems

Robust Performance Analysis of Wind Turbine Control Systems

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2023 IEEE Conference on control Technology and Applications, CCTA 2023

作者： Farhat, Omar Jaoude, Dany Abou Northeastern University Department of Mechanical and Industrial Engineering BostonMA United States Control and Optimization Lab Department of Mechanical Engineering Lebanon

ISBN: (纸本)9798350335446

In this paper, a framework for robustness analysis of wind turbine control systems is proposed. This framework is employed to examine and compare the robust performance of H∞ and PI blade pitch controllers designed to maintain the turbine's generated power at its rated value in Region 3 of the turbine operation. In this framework, a turbulent wind model based on the Kaimal characterization of turbulence is added to the closed-loop wind turbine model obtained by linearizing the nonlinear model at different mean wind speeds. The framework utilizes the theory of Integral Quadratic Constraints (IQCs), whereby IQCs and signal IQCs are used to perform the analysis. The proposed framework results in an improved characterization of the system's performance. The analysis results are validated via extensive high-fidelity simulations. © 2023 IEEE.

关键词： Wind turbines

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