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On Approximate Opacity of Stochastic control systems

IEEE Transactions on Automatic control 2024年第6期70卷 3846-3861页

作者： Liu, Siyuan Yin, Xiang Dimarogonas, Dimos V. Zamani, Majid Kth Royal Institute of Technology Division of Decision and Control Systems Stockholm Sweden Shanghai Jiao Tong University and Key Lab of System Control & Information Processing Ministry of Education Department of Automation Shanghai China University of Colorado Boulder Computer Science Department CO80309 United States Ludwig Maximilian University of Munich Computer Science Department Germany

This paper investigates an important class of information-flow security property called opacity for stochastic control systems. Opacity captures whether a system's secret behavior (a subset of the system's behavior that is considered to be critical) can be kept from outside observers. Existing works on opacity for control systems only provide a binary characterization of the system's security level by determining whether the system is opaque or not. In this work, we introduce a quantifiable measure of opacity that considers the likelihood of satisfying opacity for stochastic control systems modeled as general Markov decision processes (gMDPs). We also propose verification methods tailored to the new notions of opacity for finite gMDPs by using value iteration techniques. Then, a new notion called approximate opacity-preserving stochastic simulation relation is proposed, which captures the distance between two systems' behaviors in terms of preserving opacity. Based on this new system relation, we show that one can verify opacity for stochastic control systems using their abstractions (modeled as finite gMDPs). We also discuss how to construct such abstractions for a class of gMDPs under certain stability conditions. © 1963-2012 IEEE.

关键词： Stochastic control systems

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On convergence of covariance matrix of empirical Bayes hyper-parameter estimator

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control Theory and Technology 2024年第2期22卷 149-162页

作者： Yue Ju Biqiang Mu Tianshi Chen School of Data Science and Shenzhen Research Institute of Big Data The Chinese University of Hong KongShenzhenShenzhen518172GuangdongChina Division of Decision and Control Systems School of Electrical Engineering and Computer ScienceKTH Royal Institute of Technology10044StockholmSweden Key Laboratory of Systems and Control Institute of Systems ScienceAcademy of Mathematics and System ScienceChinese Academy of SciencesBeijing100190China

Regularized system identification has become the research frontier of system identification in the past *** related core subject is to study the convergence properties of various hyper-parameter estimators as the sample size goes to *** this paper,we consider one commonly used hyper-parameter estimator,the empirical Bayes(EB).Its convergence in distribution has been studied,and the explicit expression of the covariance matrix of its limiting distribution has been ***,what we are truly interested in are factors contained in the covariance matrix of the EB hyper-parameter estimator,and then,the convergence of its covariance matrix to that of its limiting distribution is *** general,the convergence in distribution of a sequence of random variables does not necessarily guarantee the convergence of its covariance ***,the derivation of such convergence is a necessary complement to our theoretical analysis about factors that influence the convergence properties of the EB hyper-parameter *** this paper,we consider the regularized finite impulse response(FIR)model estimation with deterministic inputs,and show that the covariance matrix of the EB hyper-parameter estimator converges to that of its limiting ***,we run numerical simulations to demonstrate the efficacy of ourtheoretical results.

关键词： Regularized system identification Hyper-parameter estimator Empirical Bayes Convergence of covariance matrix

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Secure-by-Construction Synthesis for control systems

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IEEE Transactions on Automatic control 2025年第6期70卷 4170-4177页

作者： Zhong, Bingzhuo Liu, Siyuan Caccamo, Marco Zamani, Majid Thrust of Artificial Intelligence Information Hub The Thrust of Intelligent Transportation System Hub China KTH Royal Institute of Technology Division of Decision and Control Systems Stockholm Sweden Technical University of Munich TUM School of Engineering and Design Germany University of Colorado Boulder Department of Computer Science United States Ludwig Maximilian University Department of Computer Science Munich Germany

In this note, we present the synthesis of secure-by-construction controllers that address safety and security properties simultaneously in cyber-physical systems. Our focus is on studying a specific security property called opacity, which characterizes the system's ability to maintain plausible deniability of its secret behavior in the presence of an intruder. These controllers are synthesized based on a concept of so-called (augmented) control barrier functions. We propose conditions that facilitate the construction of the desired (augmented) control barrier functions and their corresponding secure-by-construction controllers. To compute these functions, we propose an iterative scheme that leverages iterative sum-of-square programming techniques. This approach enables efficient computation of these functions, particularly for polynomial systems. Finally, we validate the effectiveness of our results through a case study of a vehicle. © 1963-2012 IEEE.

关键词： Iterative methods

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Learning Micro-Macro Models for Traffic control Using Microscopic Data

Learning Micro-Macro Models for Traffic Control Using Micros...

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2022 European control Conference, ECC 2022

作者： Krook, Jonathan Čičic, Mladen Johansson, Karl Henrik The Division of Decision and Control System KTH Royal Institute of Technology Sweden Univ. Grenoble Alpes CNRS Inria Grenoble INP GIPSA-lab France

ISBN: (纸本)9783907144077

Connected and Automated Vehicles (CAVs) are likely to have a large impact on the traffic in the near future. Assuming we are able to communicate some commands directly to them, it is of interest to know how CAVs can be used for traffic control. In order to achieve this, we need to understand how such controls affect the rest of the traffic. In this work, we study the influence of a CAV acting as a moving bottleneck, using the CAV's speed as a control input. We discuss the interpretation of the microscopic traffic data in the macroscopic framework, and propose nonparametric methods for learning the micro-macro model describing the interaction between the CAV and the surrounding traffic. We use only the local traffic data in the vicinity of the CAV, and design simple, targeted data collection experiments. This learned model is then used to predict the evolution of the traffic, and the predictions are compared with corresponding data from microscopic simulations. © 2022 EUCA.

关键词： Traffic control

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On the Equivalence Between Prescribed Performance control and control Barrier Functions

On the Equivalence Between Prescribed Performance Control an...

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American control Conference (ACC)

作者： Ryo Namerikawa Adrian Wiltz Farhad Mehdifar Toru Namerikawa Dimos V. Dimarogonas Department of System Design Engineering Keio University Kanagawa Japan Division of Decision and Control Systems KTH Royal Institute of Technology Stockholm Sweden

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

In this paper, we show that Prescribed Performance control (PPC) is a model-free control Barrier Function (CBF)-based control approach. Specifically, we establish that a function utilized in the PPC design is a Time-Varying Reciprocal control Barrier Function (TVRCBF). We demonstrate that PPC satisfies the same gradient condition that is well-known in the CBF literature, ensuring forward invariance. As a result, the control inputs generated by the PPC law belong to the input set characterized by the TVRCBF. Apart from assuming a certain controllability property, no further knowledge on the system dynamics is required. Our theoretical findings improve the understanding of the relationship between PPC and other CBF-based controllers. The theoretical results are validated through numerical simulations.

关键词： system dynamics Numerical simulation controllability Vectors

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Bayesian hypergame approach to equilibrium stability and robustness in moving target defense 63

Bayesian hypergame approach to equilibrium stability and rob...

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63rd IEEE Conference on decision and control, CDC 2024

作者： Zhang, Hanzheng Cheng, Zhaoyang Chen, Guanpu Johansson, Karl Henrik Shanghai Research Institute for Autonomous Intelligent Systems Tongji University Shanghai China University of Chinese Academy of Science Academy of Mathematics and System Science Chinese Academy of Science Beijing China Kth Royal Institute of Technology and the Digital Futures Division of Decision and Control Systems School of Electrical Engineering and Computer Science Stockholm Sweden

ISBN: (纸本)9798350316339

We investigate the equilibrium stability and robustness in a class of moving target defense problems, in which players have both incomplete information and asymmetric cognition. We first establish a Bayesian Stackelberg game model for incomplete information and then employ a hypergame reformulation to address asymmetric cognition. With the core concept of the hyper Bayesian Nash equilibrium (HBNE), a condition for achieving both the strategic and cognitive stability in equilibria can be realized by solving linear equations. Moreover, to deal with players' underlying perturbed knowledge, we study the equilibrium robustness by presenting a condition of robust HBNE under the given configuration. Experiments evaluate our theoretical results. © 2024 IEEE.

关键词： Hierarchical systems

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Delay-agnostic Asynchronous Coordinate Update Algorithm

arXiv

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arXiv 2023年

作者： Wu, Xuyang Liu, Changxin Magnússon, Sindri Johansson, Mikael Division of Decision and Control Systems Eecs Kth Royal Institute of Technology Stockholm Sweden Department of Computer and System Science Stockholm University Stockholm Sweden

We propose a delay-agnostic asynchronous coordinate update algorithm (DEGAS) for computing operator fixed points, with applications to asynchronous optimization. DEGAS includes novel asynchronous variants of ADMM and block-coordinate descent as special cases. We prove that DEGAS converges under both bounded and unbounded delays under delay-free parameter conditions. We also validate by theory and experiments that DEGAS adapts well to the actual delays. The effectiveness of DEGAS is demonstrated by numerical experiments on classification problems. © 2023, CC BY.

关键词： Computation theory

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Delay-Agnostic Asynchronous Distributed Optimization

Delay-Agnostic Asynchronous Distributed Optimization

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IEEE Conference on decision and control

作者： Xuyang Wu Changxin Liu Sindri Magnusson Mikael Johansson Division of Decision and Control Systems School of EECS KTH Royal Institute of Technology Stockholm Sweden Department of Computer and System Science Stockholm University Stockholm Sweden

Existing asynchronous distributed optimization algorithms often use diminishing step-sizes that cause slow practical convergence, or fixed step-sizes that depend on an assumed upper bound of delays. Not only is such a delay bound hard to obtain in advance, but it is also large and therefore results in unnecessarily slow convergence. This paper develops asynchronous versions of two distributed algorithms, DGD and DGD-ATC, for solving consensus optimization problems over undirected networks. In contrast to alternatives, our algorithms can converge to the fixed point set of their synchronous counterparts using step-sizes that are independent of the delays. We establish convergence guarantees under both partial and total asynchrony. The practical performance of our algorithms is demonstrated by numerical experiments.

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Asynchronous Distributed Optimization with Delay-free Parameters

arXiv

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arXiv 2023年

作者： Wu, Xuyang Liu, Changxin Magnússon, Sindri Johansson, Mikael Division of Decision and Control Systems School of EECS KTH Royal Institute of Technology StockholmSE-100 44 Sweden Department of Computer and System Science Stockholm University StockholmSE-164 07 Sweden

Existing asynchronous distributed optimization algorithms often use diminishing step-sizes that cause slow practical convergence, or use fixed step-sizes that depend on and decrease with an upper bound of the delays. Not only are such delay bounds hard to obtain in advance, but they also tend to be large and rarely attained, resulting in unnecessarily slow convergence. This paper develops asynchronous versions of two distributed algorithms, Prox-DGD and DGD-ATC, for solving consensus optimization problems over undirected networks. In contrast to alternatives, our algorithms can converge to the fixed point set of their synchronous counterparts using step-sizes that are independent of the delays. We establish convergence guarantees for convex and strongly convex problems under both partial and total asynchrony. We also show that the convergence speed of the two asynchronous methods adjusts to the actual level of asynchrony rather than being constrained by the worst-case. Numerical experiments demonstrate a strong practical performance of our asynchronous algorithms. © 2023, CC BY.

关键词： Consensus algorithm

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Delay-agnostic Asynchronous Distributed Optimization

arXiv

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arXiv 2023年

作者： Wu, Xuyang Liu, Changxin Magnússon, Sindri Johansson, Mikael The Division of Decision and Control Systems School of EECS KTH Royal Institute of Technology StockholmSE-100 44 Sweden The Department of Computer and System Science Stockholm University StockholmSE-164 07 Sweden

关键词： Optimization

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