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arXiv 2024年

作者： He, Jiabao Rojas, Cristian R. Hjalmarsson, Håkan Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm100 44 Sweden

Subspace identification methods (SIMs) have proven very powerful for estimating linear state-space models. To overcome the deficiencies of classical SIMs, a significant number of algorithms has appeared over the last two decades, where most of them involve a common intermediate step, that is to estimate the range space of the extended observability matrix. In this contribution, an optimized version of the parallel and parsimonious SIM (PARSIM), PARSIMopt, is proposed by using weighted least-squares. It not only inherits all the benefits of PARSIM but also attains the best linear unbiased estimator for the above intermediate step. Furthermore, inspired by SIMs based on the predictor form, consistent estimates of the optimal weighting matrix for weighted least-squares are derived. Essential similarities, differences and simulated comparisons of some key SIMs related to our method are also presented. Copyright © 2024, The Authors. All rights reserved.

关键词： State space methods

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On final opinions of the Friedkin-Johnsen model over random graphs with partially stubborn community

arXiv

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arXiv 2024年

作者： Wang, Lingfei Xing, Yu Johansson, Karl H. Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology with Digital Futures Stockholm Sweden

This paper studies the formation of final opinions for the Friedkin-Johnsen (FJ) model with a community of partially stubborn agents. The underlying network of the FJ model is symmetric and generated from a random graph model, in which each link is added independently from a Bernoulli distribution. It is shown that the final opinions of the FJ model will concentrate around those of an FJ model over the expected graph as the network size grows, on the condition that the stubborn agents are well connected to other agents. Probability bounds are proposed for the distance between these two final opinion vectors, respectively for the cases where there exist non-stubborn agents or not. Numerical experiments are provided to illustrate the theoretical findings. The simulation shows that, in presence of non-stubborn agents, the link probability between the stubborn and the non-stubborn communities affect the distance between the two final opinion vectors significantly. Additionally, if all agents are stubborn, the opinion distance decreases with the agent stubbornness. Copyright © 2024, The Authors. All rights reserved.

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On final opinions of the Friedkin-Johnsen model over random graphs with partially stubborn community

On final opinions of the Friedkin-Johnsen model over random ...

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IEEE Conference on decision and control

作者： Lingfei Wang Yu Xing Karl H. Johansson Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology and with Digital Futures Stockholm Sweden

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

关键词： Vectors Numerical models

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Switching control for Identification Deception in Cyber-Physical systems

Switching Control for Identification Deception in Cyber-Phys...

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IEEE Conference on decision and control

作者： Christos Mavridis Aris Kanellopoulos Henrik Sandberg Karl Henrik Johansson Division of Decision and Control School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

We investigate the problem of deceiving a malicious agent employing an identification method to estimate the closed-loop dynamics of a cyber-physical system. In particular, we propose a moving target defense mechanism that utilizes stochastic switching between linear closed-loop dynamics to drive a linear system identification process of a potential adversary to sub-optimal solutions with non-vanishing error. We provide a statistical analysis of the induced identification error and show that it is not possible for any linear system identification method to reconstruct the average dynamics of a stochastic switched linear system. Finally, we utilize the theory of Markov jump linear systems to guarantee asymptotic stability of the switching system, and formulate the switching control problem as an optimization problem that guarantees stability while taking into account the trade-off between security and switching effort. Simulation results showcase the efficacy of the proposed approach in inducing identification error for the adversary using minimal switching.

关键词： Linear systems Asymptotic stability Switching systems Statistical analysis Simulation Switches Cyber-physical systems control systems Stability analysis Security

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State-Space Piece-Wise Affine System Identification with Online Deterministic Annealing

State-Space Piece-Wise Affine System Identification with Onl...

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European control Conference (ECC)

作者： Christos N. Mavridis Aris Kanellopoulos John S. Baras Karl Henrik Johansson Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Department of Electrical and Computer Engineering and the Institute for Systems Research University of Maryland College Park USA

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

We propose an online identification scheme for discrete-time piece-wise affine state-space models based on a system of adaptive algorithms running in two timescales. A stochastic approximation algorithm implements an online deterministic annealing scheme at a slow timescale, estimating the partition of the augmented state-input space that defines the switching signal. At the same time, an adaptive identification algorithm, running at a higher timescale, updates the parameters of the local models based on the estimate of the switching signal. Identifiability conditions for the switched system are discussed and convergence results are given based on the theory of two-timescale stochastic approximation. In contrast to standard identification algorithms for piece-wise affine systems, the proposed approach progressively estimates the number of modes needed and is appropriate for online system identification using sequential data acquisition. This progressive nature of the algorithm improves computational efficiency and provides real-time control over the performance-complexity trade-off, desired in practical applications. Experimental results validate the efficacy of the proposed methodology.

关键词： Switched systems Annealing Stochastic processes Switches Approximation algorithms Real-time systems System identification

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Rollout-Based Interactive Motion Planning for Automated Vehicles *

Rollout-Based Interactive Motion Planning for Automated Vehi...

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International Conference on Intelligent Transportation

作者： Ning Wang Xiao Chen Jonas Mårtensson Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

Longitudinal and lateral motion planning poses a significant challenge to achieving full autonomy in automated vehicles. This work focuses on studying the motion planning problem for automated vehicles specifically in a highwaymerging scenario. The problem is modeled as an infinite horizon optimal control problem, taking into account finite control sets for the ego agents and uncontrolled state components of surrounding traffic. For this type of control problem, obtaining a real-time solution that meets both high safety and efficiency requirements can be difficult. In this study, we employ the rollout approach, which involves online optimization following the simulation of a known baseline policy instead of relying solely on extensive offline training. We compare the performance of one and multistep lookahead rollout algorithms against several state-of-the-art benchmark policies in simulation. The simulation results indicate that the rollout algorithm significantly enhances safety while simultaneously maintaining a high average speed within the merging scenario. Furthermore, we conduct simulation studies to assess the rollout methods in adapting to varying behaviors of surrounding vehicles. Additionally, we investigate the impact of different horizon settings and the introduction of terminal cost approximation.

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Unknown Input Observers Breaking Confidentiality of controller States

Unknown Input Observers Breaking Confidentiality of Controll...

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IEEE Conference on decision and control

作者： Enno Breukelman Henrik Sandberg School of Electrical Engineering and Computer Science Division of Decision and Control Systems KTH Royal Institute of Technology Malvinas väg 10 Stockholm Sweden

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

Driven by ubiquitous digitalization and cyberattacks on critical infrastructure, there is a high interest in research on the security of cyber-physical systems. If an attacker gains access to protected and sensitive information, such as the internal states of a control system, this is considered a breach of confidentiality. Access to sensitive information can be the first step in a larger cyber-attack scheme, such as a stealthy false data injection attack. Considering process and measurement noise in the plant, existing research investigated when an attacker equipped with a Kalman filter can perfectly estimate the internal controller states if the attacker has access to plant measurements and all model parameters. For this estimate to converge, the controller is required to have stable poles. In this paper, we show that if the attacker has access to the control inputs instead of the plant measurements, the controller needs to have stable zeros. Additionally, we demonstrate that an attacker equipped with an Unknown Input Observer, using tools from delayed system inversion, can get a delayed yet perfect estimate of the controller states from the control inputs without knowledge of the plant’s parameters and noise characteristics. Lastly, we present simulation results from a three-tank system to showcase the differences in controller state estimation.

关键词： Simulation Noise Process control Observers Cyber-physical systems Critical infrastructure Noise measurement Security Kalman filters Cyberattack

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Invariant Safe Contingency Model Predictive control for Intersection Coordination of Mixed Traffic

Invariant Safe Contingency Model Predictive Control for Inte...

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International Conference on Intelligent Transportation

作者： Xiao Chen Jonas Mårtensson Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

This paper addresses the coordination challenge at intersections of mixed traffic involving both Human-Driven Vehicles (HDVs) and Connected and Autonomous Vehicles (CAVs). To strike a balance between coordination performance and safety guarantees, we propose an invariant safe Contingency Model Predictive control (CMPC) framework. The CMPC framework incorporates two parallel horizons for the ego vehicle: a nominal horizon optimized for performance based on the most likely prediction of the opponent HDV, and a contingency horizon designed to maintain an invariant safe backup plan for emergencies. In the contingency horizon, we consider the worst-case behavior of the human driver and formulate safety constraints using the forward reachable sets of the HDV within the planning horizon. These safety constraints are complemented by maximal invariant safe sets as terminal constraints. The two horizons are tied together by enforcing equality of the feedback inputs at the beginning of the horizons. We provide theoretical evidence supporting the recursive feasibility and persistent performance improvement of the invariant safe CMPC compared to our previously proposed nominal invariant safe Model Predictive control (MPC). Through simulation studies, we evaluate the proposed method. The simulation results demonstrate that the CMPC approach achieves enhanced performance by reducing conservatism while simultaneously preserving the invariant safety property.

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Self-Tuning Tube-based Model Predictive control

Self-Tuning Tube-based Model Predictive Control

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American control Conference (ACC)

作者： Damianos Tranos Alessio Russo Alexandre Proutiere Division of Decision and Control Systems School of Electrical Engineering and Computer Science Royal Institute of Technology (KTH) Stockholm Sweden

We present Self-Tuning Tube-based Model Predictive control (STT-MPC), an adaptive robust control algorithm for uncertain linear systems with additive disturbances based on the least-squares estimator and polytopic tubes. Our algorithm leverages concentration results to bound the system uncertainty set with prescribed confidence, and guarantees robust constraint satisfaction for this set, along with recursive feasibility and input-to-state stability. Persistence of excitation is ensured without compromising the algorithm’s asymptotic performance or increasing its computational complexity. We demonstrate the performance of our algorithm using numerical experiments.

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Distributed Prescribed-Time Observer for Nonlinear systems in Block-Triangular Form

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IEEE control systems Letters 2025年 9卷 222-227页

作者： Vincent de Heij M. Umar B. Niazi Karl H. Johansson Saeed Ahmed Jan C. Willems Center for Systems and Control and the Engineering and Technology Institute Groningen Faculty of Science and Engineering University of Groningen Groningen The Netherlands Division of Decision and Control Systems Digital Futures KTH Royal Institute of Technology Stockholm Sweden Department of Electrical Engineering and Computer Science Laboratory for Information and Decision Systems Massachusetts Institute of Technology Cambridge MA USA

This letter proposes a design of a distributed prescribed-time observer for nonlinear systems representable in a block-triangular observable canonical form. Using a weighted average of neighbor estimates exchanged over a strongly connected digraph, each observer estimates the system state despite the limited observability of local sensor measurements. The proposed design guarantees that distributed state estimation errors converge to zero at a user-specified convergence time, irrespective of observers’ initial conditions. To achieve this prescribed-time convergence, distributed observers implement time-varying local output injection gains that monotonically increase and approach infinity at the prescribed time. The theoretical convergence is rigorously proven and validated through numerical simulations, where some implementation issues due to increasing gains have also been clarified.

关键词： Observers Convergence Nonlinear systems Symmetric matrices Observability Directed graphs control systems Vectors Linear systems Electronic mail

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