2期

最新文章

• Episodic Gaussian Process-Based Learning Control With Vanishing Tracking Errors AI翻译
  作者：Lederer, Armin; Umlauft, Jonas; Hirche, Sandra
  作者单位：National University of Singapore; Technical University of Munich
  摘要：Due to the increasing complexity of technical systems, accurate first principle models can often not be obtained. Supervised machine learning can mitigate this issue by inferring models from measurement data. Gaussian process (GP) regression is particularly well suited for this purpose due to its high data efficiency and its explicit uncertainty representation, which allows the derivation of prediction error bounds. These error bounds have been exploited to show tracking accuracy guarantees fo...
• A Unifying Perspective for Safety of Stochastic Systems: From Barrier Functions to Finite Abstractions AI翻译
  作者：Laurenti, Luca; Lahijanian, Morteza
  作者单位：Delft University of Technology; University of Colorado System; University of Colorado Boulder
  摘要：Providing safety guarantees for stochastic dynamical systems is a central problem in various fields, including control theory, machine learning, and robotics. Existing methods either employ stochastic barrier functions (SBFs) or rely on numerical approaches based on finite abstractions. SBFs, analogous to Lyapunov functions, are used to establish (probabilistic) set invariance, whereas abstraction-based approaches approximate the stochastic system with a finite model to compute safety probabil...
• An Input-Output Approach to Incremental Feedback Stability AI翻译
  作者：Su, Lanlan; Khong, Sei Zhen
  作者单位：University of Manchester; National Sun Yat Sen University
  摘要：The well-posedness and incremental stability of feedback interconnections of nonlinear systems satisfying complementary incremental hard integral-quadratic constraints (IQCs) are investigated. It is established that when these incremental IQCs are defined by indefinite static (i.e., matricial) multipliers, feedback well-posedness, which guarantees unique existence of causal solutions to the feedback equations, can be ascertained. We show that a well-posed feedback system is incrementally passi...
• New Conditions and Controllers for State-of-Charge Balancing in Battery Energy Storage Systems AI翻译
  作者：Qian, Yangyang; Qiu, Chenyang; Lin, Zongli; Shamash, Yacov A.
  作者单位：University of Virginia; State University of New York (SUNY) System; Stony Brook University
  摘要：In this article, we investigate the state-of-charge (SoC) balancing control problem for a battery energy storage system, which consists of multiple battery units. These battery units are allowed to have heterogeneous battery parameters and are connected in parallel to deliver a desired total power. Existing power allocating controllers have been developed to achieve SoC balancing without taking balancing speed into consideration. Motivated by this observation, we aim to design new power alloca...
• Explicit Solution of Tunable Input-to-State Safe-Based Controller Under High-Relative-Degree Constraints AI翻译
  作者：Wei, Yan; Feng, Yu; Ou, Linlin; Wang, Yueying; Yu, Xinyi
  作者单位：Zhejiang University of Technology; Shanghai University
  摘要：This article investigates the safety analysis and verification of nonlinear systems subject to high-relative-degree constraints and unknown disturbance. The closed-form solution of the high-order control barrier functions (HOCBF) optimization problem with and without a nominal controller is first provided, making it unnecessary to solve the quadratic program problem online and facilitating the analysis. Further, the tunable input-to-state safety (ISSf) framework is investigated to accommodate ...
• The Role of Communication Delays in the Optimal Control of Spatially Invariant Systems AI翻译
  作者：Ballotta, Luca; Arbelaiz, Juncal; Gupta, Vijay; Schenato, Luca; Jovanovic, Mihailo R.
  作者单位：Delft University of Technology; Princeton University; Purdue University System; Purdue University; University of Padua; University of Southern California
  摘要：We study optimal proportional feedback controllers for spatially invariant systems when the controller has access to delayed state measurements received from different spatial locations. We analyze how delays affect the spatial locality of the optimal feedback gain leveraging the problem decoupling in the spatial frequency domain. For the cases of expensive control and small delay, we provide exact expressions of the optimal controllers in the limit for infinite control weight and vanishing de...
• MR-ARL: Model Reference Adaptive Reinforcement Learning for Robustly Stable On-Policy Data-Driven LQR AI翻译
  作者：Borghesi, Marco; Bosso, Alessandro; Notarstefano, Giuseppe
  作者单位：University of Bologna
  摘要：This article introduces a novel framework for data-driven linear quadratic regulator (LQR) design. First, we introduce a reinforcement learning paradigm for on-policy data-driven LQR, where exploration and exploitation are simultaneously performed while guaranteeing robust stability of the whole closed-loop system encompassing the plant and the control/learning dynamics. Then, we propose model reference adaptive reinforcement learning (MR-ARL), a control architecture integrating tools from rei...
• Online Parameter Identification of Cost Functions in Generalized Nash Games AI翻译
  作者：Chen, Jianguo; Lei, Jinlong; Hong, Yiguang; Qi, Hongsheng
  作者单位：Chinese Academy of Sciences; Academy of Mathematics & System Sciences, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Tongji University; Tongji University; Tongji University; Tongji University
  摘要：This work studies the online parameter identification of cost functions in a generalized Nash game, where each player's cost function is influenced by an observable signal and some unknown parameters. A learner can sequentially observe the equilibria reached by the game system as the observable signal changes, and its goal is to identify the unknown parameters in the cost functions. We recast this problem as an online optimization and introduce a novel online parameter identification algorithm...
• HMM-Based Secure Control of Multiagent Systems Under DoS Attacks AI翻译
  作者：Cheng, Peng; He, Shuping; Xiao, Gaoxi; Zhang, Weidong
  作者单位：Anhui University; Anhui University; Nanyang Technological University; Shanghai Jiao Tong University
  摘要：This note presents an asynchronous distributed active secure control strategy with a safe mode to address the consensus issue for a leader-follower multiagent system (MAS) under denial-of-service (DoS) attacks. The communication network of the leader-follower MAS is exposed to stochastic DoS attacks, which result in different communication topologies. Based on whether the connectivity of the network topology is disrupted, different DoS attack modes are classified into two types: successful DoS...
• Zero-Trust-Based Privacy-Preserving Distributed Localization for Mobile Robot Networks AI翻译
  作者：Shi, Lei; Huang, Darong; Zong, Guangdeng; Zhou, Yi; Cheng, Yuhua
  作者单位：Henan University; Anhui University; Tiangong University; University of Electronic Science & Technology of China
  摘要：Localization is the foundation for achieving autonomous navigation of robots, and protecting the privacy of robots during the localization process is crucial. This article develops a zero-trust-based privacy-preserving distributed iteration localization (ZTPP-DILOC) algorithm for a robot network that moves freely within bounded areas. In this algorithm, based on the principle of never trust, always verify in the zero-trust security framework, real-time trust evaluation mechanisms are establish...