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Inverse learning of black-box aggregator for robust Nash equilibrium

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arXiv 2024年

作者： Chen, Guanpu Xu, Gehui He, Fengxiang Tao, Dacheng Parisini, Thomas Johansson, Karl Henrik Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm100 44 Sweden Digital Futures Stockholm100 44 Sweden Artificial Intelligence and its Applications Institute School of Informatics University of Edinburgh EdinburghEH8 9AB United Kingdom College of Computing & Data Science Nanyang Technological University Singapore639798 Singapore Department of Electrical and Electronic Engineering Imperial College London LondonSW7 2AZ United Kingdom Department of Engineering and Architecture University of Trieste Trieste34127 Italy

In this note, we investigate the robustness of Nash equilibria (NE) in multi-player aggregative games with coupling constraints. There are many algorithms for computing an NE of an aggregative game given a known aggregator. When the coupling parameters are affected by uncertainty, robust NE need to be computed. We consider a scenario where players' weight in the aggregator is unknown, making the aggregator kind of "a black box". We pursue a suitable learning approach to estimate the unknown aggregator by proposing an inverse variational inequality-based relationship. We then utilize the counterpart to reconstruct the game and obtain first-order conditions for robust NE in the worst case. Furthermore, we characterize the generalization property of the learning methodology via an upper bound on the violation probability. Simulation experiments show the effectiveness of the proposed inverse learning approach. Copyright © 2024, The Authors. All rights reserved.

关键词： Inverse problems

Nonlinear parameter-varying state-feedback design for a gyroscope using virtual control contraction metrics

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arXiv 2021年

作者： Wang, Ruigang Koelwijn, Patrick J.W. Manchester, Ian R. Tóth, Roland Australian Centre for Field Robotics Sydney Institute for Robotics and Intelligent Systems University of Sydney SydneyNSW2006 Australia Department of Electrical Engineering Eindhoven University of Technology Eindhoven Netherlands Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

In this paper, we present a virtual control contraction metric (VCCM) based nonlinear parameter-varying (NPV) approach to design a state-feedback controller for a control moment gyroscope (CMG) to track a user-defined trajectory set. This VCCM based nonlinear stabilization and performance synthesis approach, which is similar to linear parameter-varying (LPV) control approaches, allows to achieve exact guarantees of exponential stability and L2-gain performance on nonlinear systems with respect to all trajectories from the predetermined set, which is not the case with the conventional LPV methods. Simulation and experimental studies conducted in both fully- and under-actuated operating modes of the CMG show effectiveness of this approach compared to standard LPV control methods. © 2021, CC BY.

关键词： State feedback

Deep Learning-Based Prediction of Subsurface Oil Reservoir Pressure Using Spatio-Temporal Data

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Deep Learning-Based Prediction of Subsurface Oil Reservoir P...

Annual Conference of Industrial Electronics Society

作者： Haibo Cheng Yunpeng He Peng Zeng Shichao Li Valeriy Vyatkin State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China Department of Computer Science Electrical and Space Engineering Luleå University of Technology Luleå Sweden Department of Electrical Engineering and Automation Aalto University Helsinki Finland

Prediction of subsurface oil reservoir pressure are critical to hydrocarbon production. However, the accurate pressure estimation faces great challenges due to the complexity and uncertainty of reservoir. The underground seepage flow and petrophysical parameters (permeability and porosity) are important but difficult to measure in oilfield. Deep learning methods have been successfully used in reservoir engineering and oil & gas production process. In this study, the effective but inaccessible subsurface seepage fields are not used, only the spatial coordinates and temporal information are selected as model input to predict reservoir pressure. A stacked GRU-based deep learning model is proposed to map the relationship between spatio-temporal data and reservoir pressure. The proposed deep learning method is verified by using a three-dimensional reservoir model, and compared with commonly-used methods. The results show that the stacked GRU model has a better performance and higher accuracy than other deep learning or machine learning methods in pressure prediction.

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Stabilization of switched systems with all unstable modes:application to the aircraft team problem

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Journal of systems engineering and Electronics 2019年第4期30卷 792-798页

作者： TAVAKOLIFAR Dariush KHALOOZADEH Hamid AMJADIFARD Roya Department of Electrical Engineering Science and Research BranchIslamic Azad UniversityTehran ***Iran Department of Systems and Control K.N.Toosi University of TechnologyTehran ***Iran Department of Electrical&Computer Engineering Kharazmi UniversityTehran ***Iran

The method of stabilizing switched systems based on the optimal control is applied,with all modes unstable,for a typical example of the multi-agent ***,the optimal control method for stabilizing switched systems is *** this purpose,a switching table rule procedure is *** procedure is inspired by the optimal control that identifies the optimal trajectory for the switched *** the next step,the stability of a multi-agent system is studied,considering different unstable connection ***,the optimal control method is successfully applied to an aircraft team,as an example of the multi-agent *** results evaluate and confirm the successful application of this method in the aircraft team example.

关键词： switched system multi-agent system stabilization optimal control unstable modes aircraft team

Data-driven System Identification of Thermal systems using Machine Learning

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IFAC-PapersOnLine 2021年第7期54卷 162-167页

作者： Ştefan-Cristian Nechita Roland Tóth Koos van Berkel Department of Electrical Engineering Eindhoven University of Technology Eindhoven University of Technology Eindhoven The Netherlands Systems and Control Laboratory Institute for Computer Science and Control Kende u. 13-17 H-1111 Budapest Hungary Research Department ASML Netherlands B.V. 5504 DR Veldhoven The Netherlands

The paper addresses the identification of spatial-temporal mirror surface deformations as a result of laser-based heat load within the lithography process of integrated circuit production. The thermal diffusion and surface deformation are modeled by separation of the spatial-temporal effects using data-driven orthogonal decomposition. A novel tree adjoining grammar (TAG) and sparsity enhanced symbolic-regression-based learning methods are deployed to discover temporal dynamics that connect the spatial variation. The resulting data-driven procedure is applied to automatically synthetise a compact model representation of synthetic thermal effects induced mirror surface deformations.

关键词： Spatial-temporal System Identification Separation of Variables Machine Learning Genetic Programming MIMO System Indentification Tree Adjoining Grammar Equation Discovery Gaussian Proccesses

On the convergence of step decay step-size for stochastic optimization

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arXiv 2021年

作者： Wang, Xiaoyu Magnússon, Sindri Johansson, Mikael The Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology StockholmSE-100 44 Sweden Department of Computer and System Science Stockholm University Stockholm Sweden

The convergence of stochastic gradient descent is highly dependent on the step-size, especially on non-convex problems such as neural network training. Step decay step-size schedules (constant and then cut) are widely used in practice because of their excellent convergence and generalization qualities, but their theoretical properties are not yet well understood. We provide the convergence results for step decay in the non-convex regime, ensuring that the gradient norm vanishes at an (Formula presented) rate. We also provide the convergence guarantees for general (possibly non-smooth) convex problems, ensuring an (Formula presented) convergence rate. Finally, in the strongly convex case, we establish an (Formula presented) rate for smooth problems, which we also prove to be tight, and an (Formula presented) rate without the smoothness assumption. We illustrate the practical efficiency of the step decay step-size in several large scale deep neural network training tasks. Copyright © 2021, The Authors. All rights reserved.

关键词： Deep neural networks

Robust Data-Driven Predictive control using Reachability Analysis

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arXiv 2021年

作者： Alanwar, Amr Stürz, Yvonne Johansson, Karl Henrik Computer Science & Electrical Engineering Department Jacobs University Bremen Germany Division of Decision and Control Systems KTH Royal Institute of Technology Sweden Model Predictive Control Laboratory University of California Berkeley United States

We present a robust data-driven control scheme for an unknown linear system model with bounded process and measurement noise. Instead of depending on a system model in traditional predictive control, a controller utilizing data-driven reachable regions is proposed. The data-driven reachable regions are based on a matrix zonotope recursion and are computed based on only noisy input-output data of a trajectory of the system. We assume that measurement and process noise are contained in bounded sets. While we assume knowledge of these bounds, no knowledge about the statistical properties of the noise is assumed. In the noise-free case, we prove that the presented purely data-driven control scheme results in an equivalent closed-loop behavior to a nominal model predictive control scheme. In the case of measurement and process noise, our proposed scheme guarantees robust constraint satisfaction, which is essential in safety-critical applications. Numerical experiments show the effectiveness of the proposed data-driven controller in comparison to model-based control schemes. Copyright © 2021, The Authors. All rights reserved.

关键词： controllers

Actuation attacks on constrained linear systems: a set-theoretic analysis

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IFAC-PapersOnLine 2020年第2期53卷 6963-6968页

作者： P.A. Trodden J.M. Maestre H. Ishii Department of Automatic Control & Systems Engineering University of Sheffield Sheffield UK Department of Systems and Automation Engineering University of Seville Seville Spain Department of Computer Science Tokyo Institute of TechnologyYokohama Japan

This paper considers a constrained discrete-time linear system subject to actuation attacks. The attacks are modelled as false data injections to the system, such that the total input (control input plus injection) satisfies hard input constraints. We establish a sufficient condition under which it is not possible to maintain the states of the system within a compact state constraint set for all possible realizations of the actuation attack. The developed condition is a simple function of the spectral radius of the system, the relative sizes of the input and state constraint sets, and the proportion of the input constraint set allowed to the attacker.

关键词： control problems under conflict or uncertainties Constrained control Robustness

Real-time cross-fleet pareto-improving truck platoon coordination

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arXiv 2022年

作者： Johansson, Alexander Sun, Xiaotong Mårtensson, Jonas Yin, Yafeng Integrated Transport Research Lab and Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden StockholmSE-100 44 Sweden Department of Civil and Environmental Engineering University of Michigan 2350 Hayward St Ann ArborMI48109 United States

This paper studies a multi-fleet platoon coordination system in transport networks that deploy hubs to form trucks into platoons. The trucks belong to different fleets that are interested in increasing their profits by platooning across fleets. The profit of each fleet incorporates platooning rewards and costs for waiting at hubs. Each truck has a fixed route and a waiting time budget to spend at the hubs along its route. To ensure that all fleets are willing to participate in the system, we develop a cross-fleet Pareto-improving coordination strategy that guarantees higher fleet profits than a coordination strategy without cross-fleet platoons. By leveraging multiple hubs for platoon formation, the coordination strategy can be implemented in a real-time and distributed fashion while largely reducing the amount of travel information to be shared for system-wide coordination. We evaluate the proposed strategy in a simulation study over the Swedish transportation network. The cross-fleet platooning strategy significantly improves fleets' profits compared with single-fleet platooning, especially the profits from smaller fleets. The cross-fleet platooning strategy also shows strong competitiveness in terms of the system-wide profit compared to the case when a system planner optimizes all fleets' total profit. Copyright © 2022, The Authors. All rights reserved.

关键词： Profitability