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arXiv 2024年

作者： Wang, Siyi Wang, Zifan Yi, Xinlei Zavlanos, Michael M. Johansson, Karl H. Hirche, Sandra Department of Electrical and Computer Engineering Technical University of Munich Munich80333 Germany Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm10044 Sweden Lab for Information & Decision Systems Massachusetts Institute of Technology CambridgeMA02139 United States Mechanical Engineering and Material Science Duke University DurhamNC27708 United States

Considering non-stationary environments in online optimization enables decision-maker to effectively adapt to changes and improve its performance over time. In such cases, it is favorable to adopt a strategy that minimizes the negative impact of change to avoid potentially risky situations. In this paper, we investigate risk-averse online optimization where the distribution of the random cost changes over time. We minimize risk-averse objective function using the Conditional Value at Risk (CVaR) as risk measure. Due to the difficulty in obtaining the exact CVaR gradient, we employ a zeroth-order optimization approach that queries the cost function values multiple times at each iteration and estimates the CVaR gradient using the sampled values. To facilitate the regret analysis, we use a variation metric based on Wasserstein distance to capture time-varying distributions. Given that the distribution variation is sub-linear in the total number of episodes, we show that our designed learning algorithm achieves sub-linear dynamic regret with high probability for both convex and strongly convex functions. Moreover, theoretical results suggest that increasing the number of samples leads to a reduction in the dynamic regret bounds until the sampling number reaches a specific limit. Finally, we provide numerical experiments of dynamic pricing in a parking lot to illustrate the efficacy of the designed algorithm. Copyright © 2024, The Authors. All rights reserved.

关键词： Learning algorithms

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Spiking neural networks: Towards bio-inspired multimodal perception in robotics

arXiv

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arXiv 2024年

作者： Oikonomou, Katerina Maria Balaska, Vasiliki Tsintotas, Konstantinos A. Mavridis, Christos N. Kansizoglou, Ioannis Gasteratos, Antonios The Department of Production and Management Engineering Laboratory of Robotics and Automation Democritus University of Thrace Xanthi Greece The Department of Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

Spiking neural networks (SNNs) have captured apparent interest over the recent years, stemming from neuroscience and reaching the field of artificial intelligence. However, due to their nature SNNs remain far behind in achieving the exceptional performance of deep neural networks (DNNs). As a result, many scholars are exploring ways to enhance SNNs by using learning techniques from DNNs. While this approach has been proven to achieve considerable improvements in SNN performance, we propose another perspective: enhancing the biological plausibility of the models to leverage the advantages of SNNs fully. Our approach aims to propose a brain-like combination of audio-visual signal processing for recognition tasks, intended to succeed in more bio-plausible human-robot interaction applications. © 2024, CC BY-NC-SA.

关键词： Adversarial machine learning

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Data-Driven Nonconvex Reachability Analysis using Exact Multiplication

arXiv

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arXiv 2025年

作者： Zhang, Zhen Niazi, M. Umar B Chong, Michelle S. Johansson, Karl H. Alanwar, Amr School of Computation Information and Technology Technical University of Munich Germany Division of Decision and Control Systems Digital Futures School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Department of Mechanical Engineering Eindhoven University of Technology Netherlands

This paper addresses a fundamental challenge in data-driven reachability analysis: accurately representing and propagating non-convex reachable sets. We propose a novel approach using constrained polynomial zonotopes to describe reachable sets for unknown LTI systems. Unlike constrained zonotopes commonly used in existing literature, constrained polynomial zonotopes are closed under multiplication with constrained matrix zonotopes. We leverage this property to develop an exact multiplication method that preserves the non-convex geometry of reachable sets without resorting to approximations. We demonstrate that our approach provides tighter over-approximations of reachable sets for LTI systems compared to conventional methods. © 2025, CC BY.

关键词： Geometry

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Low-Complexity control for a Class of Uncertain MIMO Nonlinear systems under Generalized Time-Varying Output Constraints (extended version)

arXiv

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arXiv 2024年

作者： Mehdifar, Farhad Lindemann, Lars Bechlioulis, Charalampos P. Dimarogonas, Dimos V. Division of Decision and Control Systems KTH Royal Institute of Technology Stockholm Sweden Thomas Lord Department of Computer Science University of Southern California Los AngelesCA United States Division of Systems and Control Department of Electrical and Computer Engineering University of Patras Patra Greece

This paper introduces a novel control framework to address the satisfaction of multiple time-varying output constraints in uncertain high-order MIMO nonlinear control systems. Unlike existing methods, which often assume that the constraints are always decoupled and feasible, our approach can handle coupled time-varying constraints even in the presence of potential infeasibilities. First, it is shown that satisfying multiple constraints essentially boils down to ensuring the positivity of a scalar variable, representing the signed distance from the boundary of the time-varying output-constrained set. To achieve this, a single consolidating constraint is designed that, when satisfied, guarantees convergence to and invariance of the time-varying output-constrained set within a user-defined finite time. Next, a novel robust and low-complexity feedback controller is proposed to ensure the satisfaction of the consolidating constraint. Additionally, we provide a mechanism for online modification of the consolidating constraint to find a least violating solution when the constraints become mutually infeasible for some time. Finally, simulation examples of trajectory and region tracking for a mobile robot validate the proposed approach. Copyright © 2024, The Authors. All rights reserved.

关键词： Feedback control

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Locally Differentially Private Online Federated Learning With Correlated Noise

arXiv

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arXiv 2024年

作者： Zhang, Jiaojiao Zhu, Linglingzhi Fay, Dominik Johansson, Mikael Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology StockholmSE-100 44 Sweden H. Milton Stewart School of Industrial and Systems Engineering Georgia Institute of Technology AtlantaGA United States

We introduce a locally differentially private (LDP) algorithm for online federated learning that employs temporally correlated noise to improve utility while preserving privacy. To address challenges posed by the correlated noise and local updates with streaming non-IID data, we develop a perturbed iterate analysis that controls the impact of the noise on the utility. Moreover, we demonstrate how the drift errors from local updates can be effectively managed for several classes of nonconvex loss functions. Subject to an (ϵ,δ)-LDP budget, we establish a dynamic regret bound that quantifies the impact of key parameters and the intensity of changes in the dynamic environment on the learning performance. Numerical experiments confirm the efficacy of the proposed algorithm. © 2024, CC BY.

关键词： Federated learning

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Navigating A Mobile Robot Using Switching Distributed Sensor Networks

Navigating A Mobile Robot Using Switching Distributed Sensor...

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European control Conference (ECC)

作者： Xingkang He Ehsan Hashemi Karl H. Johansson Ericsson AB Sweden Department of Mechanical Engineering University of Alberta Canada Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden

This paper proposes a method to navigate a mobile robot by estimating its state over a number of distributed sensor networks (DSNs) such that it can successively accomplish a sequence of tasks, i.e., its state enters each targeted set and stays inside no less than the desired time, under a resource-aware, time-efficient, and computation-and communication-constrained setting. We propose a new robot state estimation and navigation architecture, which integrates an event-triggered task-switching feedback controller for the robot and a two-time-scale distributed state estimator for each sensor. With the controller, the robot is able to accomplish a task by following a reference trajectory and switch to the next task when an event-triggered condition is fulfilled. With the estimator, each active sensor is able to estimate the robot state. We provide conditions to ensure that the state estimation error and the trajectory tracking deviation are upper bounded by two time-varying sequences, respectively. Furthermore, we find a sufficient condition for accomplishing a task and provide an upper bound of running time for the task. Numerical simulations of an indoor robot’s localization and navigation are provided to validate the proposed architecture

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Algal Bloom Front Tracking Using an Unmanned Surface Vehicle: Numerical Experiments Based on Baltic Sea Data

Algal Bloom Front Tracking Using an Unmanned Surface Vehicle...

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OCEANS 2021: San Diego - Porto

作者： Fonseca, Joana Aguiar, Miguel Sousa, Joao Borges De Johansson, Karl H. KTH Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science and Digital Futures StockholmSE-10044 Sweden Portugal

ISBN: (纸本)9780692935590

We consider the problem of tracking moving algal bloom fronts using an unmanned surface vehicle (USV) equipped with a sensor that measures the concentration of chlorophyll a. Chlorophyll a is a green pigment found in plants, and its concentration is an indicator of phytoplankton abundance. Our algal bloom front tracking mission consists of three stages: deployment, data collection, and front tracking. At the deployment stage, a satellite collects an image of the sea from which the location of the front, the reference value for the concentration at this front and, consequently, the appropriate initial position for the USV are determined. At the data collection stage, the USV collects data points to estimate the local algal gradient as it crosses the front. Finally, at the front tracking stage, an adaptive algorithm based on recursive least squares fitting using recent past sensor measures is executed. We evaluate the performance of the algorithm and its sensitivity to measurement noise through MATLAB simulations. We also present an implementation of the algorithm on the DUNE onboard software platform for marine robots and validate it using simulations with satellite model forecasts from Baltic sea data. © 2021 MTS.

关键词： Unmanned surface vehicles

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Platoons Coordination Based on Decentralized Higher Order Barrier Certificates

Platoons Coordination Based on Decentralized Higher Order Ba...

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Mediterranean Conference on control and Automation (MED)

作者： Maryam Sharifi Dimos V. Dimarogonas ABB Corporate Research Västerås Sweden Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

This paper presents control strategies based on time-varying convergent higher order control barrier functions for the coordination of networks of platoons. This network could be modelled by a class of leader-follower multi-agent systems, where the leaders have knowledge on the associated tasks and control the performance of their platoon involved vehicles. The followers are not aware of the tasks, and do not have any control authority to reach them. They follow their platoon leader commands for the task satisfaction. Signal temporal logic (STL) tasks are defined for the platoons coordination. Robust solutions for the task satisfaction, based on the leader’s accessibility to the follower vehicles’ states are suggested. In addition, using the notion of higher order barrier functions, decentralized barrier certificates for each vehicle evolving in a formation dynamic structure are proposed. Our approach finds solutions to guarantee the satisfaction of STL tasks independent of the agents’ initial conditions.

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Optimal Input Design for Sparse System Identification

Optimal Input Design for Sparse System Identification

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European control Conference (ECC)

作者： Javad Parsa Cristian R. Rojas Hå kan Hjalmarsson Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

ISBN: (数字)9783907144077

ISBN: (纸本)9781665497336

In this contribution we consider sparse linear regression problems. It is well known that the mutual coherence, i.e. the maximum correlation of the regressors, is important for the ability of any algorithm to recover the sparsity pattern of an unknown parameter vector from data. A low mutual coherence improves the ability of recovery. In optimal experiment design this requirement may be in conflict with other objectives encoded by the desired Fisher matrix. In this contribution we alleviate this issue by combining optimal input design with a recently proposed approach to achieve low mutual coherence by way of a linear coordinate transformation. The resulting optimization problem is solved using cyclic minimization. Via simulations we demonstrate that the resulting algorithm is able to achieve a Fisher matrix which results in a performance close to the performance if the sparsity would have been known, while at the same time being able to recover the sparsity pattern.

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Finite Sample Analysis of Subspace Identification Methods

arXiv

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arXiv 2025年

作者： He, Jiabao Ziemann, Ingvar Rojas, Cristian R. Qin, S. Joe Hjalmarsson, Håkan Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm100 44 Sweden University of Pennsylvania PhiladelphiaPA19104 United States Institute of Data Science Lingnan University Hong Kong

As one of the mainstream approaches in system identification, subspace identification methods (SIMs) are known for their simple parameterization for MIMO systems and robust numerical properties. However, a comprehensive statistical analysis of SIMs remains an open problem. Amid renewed focus on identifying state-space models in the non-asymptotic regime, this work presents a finite sample analysis for a large class of open-loop SIMs. It establishes high-probability upper bounds for system matrices obtained via SIMs, and reveals that convergence rates for estimating Markov parameters and system matrices are O(1/√N) up to logarithmic terms, in line with classical asymptotic results. Following the key steps of SIMs, we arrive at the above results by a three-step procedure. In Step 1, we begin with a parsimonious SIM (PARSIM) that uses least-squares regression to estimate multiple high-order ARX models in parallel. Leveraging a recent analysis of an individual ARX model, we obtain a union error bound for a bank of ARX models. Step 2 involves model reduction via weighted singular value decomposition (SVD), where we consider different data-dependent weighting matrices and use robustness results for SVD to obtain error bounds on extended controllability and observability matrices, respectively. The final Step 3 focuses on deriving error bounds for system matrices, where two different realization algorithms, the MOESP type and the Larimore type, are considered. Although our study initially focuses on PARSIM, the methodologies apply broadly across many variants of SIMs. Copyright © 2025, The Authors. All rights reserved.

关键词： MIMO systems

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