2025

最新文章

• Fully Distributed Consensus of Multiple Euler-Lagrange Systems With Time-Varying Asymmetric Full-State Constraints AI翻译
  作者：Yang, Yanhua; Mei, Jie; Shi, Xiongtao; Ma, Guangfu
  作者单位：Harbin Institute of Technology; Harbin Institute of Technology
  摘要：In this article, the consensus problem of multiple Euler-Lagrange (EL) systems with time-varying asymmetric full-state constraints under a directed graph is investigated in a fully distributed way, where the global information dependence is removed. First, to prevent the violation of the full-state constraints of each EL system, a nonlinear state-dependent transformation is adopted for both the leader and followers, where the constraints will not be violated as long as the transformed states r...
• A Robust Adaptive Control Approach to Leader-Following Consensus of Multiple Uncertain Euler-Lagrange Systems Over Switching Networks AI翻译
  作者：Lin, Haoyan; Huang, Jie
  作者单位：Chinese University of Hong Kong
  摘要：The leader-following consensus problem for multiple uncertain Euler-Lagrange (EL) systems has been extensively studied by two approaches: robust control approach and adaptive control approach. The adaptive control approach has the advantage that it does not require the bounds of unknown parameters be known but has the shortcoming that the control law is of high dimension as every unknown parameter has to be estimated by the local control law of every follower. On the other hand, the robust con...
• Distributed Adaptive Time-Varying Optimization With Global Asymptotic Convergence AI翻译
  作者：Jiang, Liangze; Wu, Zheng-Guang; Wang, Lei
  作者单位：Zhejiang University
  摘要：In this note, we study distributed time-varying optimization for a multiagent system. We first focus on a class of time-varying quadratic cost functions, and develop a new distributed algorithm that integrates an average estimator and an adaptive optimizer, with both bridged by a Dead Zone Algorithm. Based on a composite Lyapunov function and finite escape-time analysis, we prove the closed-loop global asymptotic convergence to the optimal solution under mild assumptions. Particularly, the int...
• A Nonparametric Learning Framework for Nonlinear Robust Output Regulation AI翻译
  作者：Wang, Shimin; Guay, Martin; Chen, Zhiyong; Braatz, Richard D.
  作者单位：Massachusetts Institute of Technology (MIT); Queens University - Canada; University of Newcastle
  摘要：A nonparametric learning solution framework is proposed for the global nonlinear robust output regulation problem. We first extend the assumption that the steady-state generator is linear in the exogenous signal to the more relaxed assumption that it is polynomial in the exogenous signal. In addition, a nonparametric learning framework is proposed to eliminate the construction of an explicit regressor, as required in the adaptive method, which can potentially simplify the implementation and re...
• Safety Control of Uncertain MIMO Systems Using Dynamic Output Feedback Barrier Pairs AI翻译
  作者：He, Binghan; Tanaka, Takashi
  作者单位：University of Texas System; University of Texas Austin; University of California System; University of California Berkeley; University of Texas System; University of Texas Austin
  摘要：Safety control of dynamical systems using barrier functions relies on knowing the full state information. This article introduces a novel approach for safety control in uncertain multiple-input-muiltiple-output (MIMO) systems with partial state information. The proposed method combines the synthesis of a vector norm barrier function and a dynamic output feedback safety controller to ensure robust safety enforcement. The safety controller guarantees the invariance of the barrier function under ...
• Second Moment Polytopic Systems: Generalization of Uncertain Stochastic Linear Dynamics AI翻译
  作者：Ito, Yuji; Fujimoto, Kenji
  作者单位：Toyota Central R&D Labs Inc; Kyoto University
  摘要：This article presents a new paradigm to stabilize uncertain stochastic linear systems. Herein, second moment polytopic (SMP) systems are proposed that generalize systems with both uncertainty and randomness. The SMP systems are characterized by second moments of the stochastic system matrices and the uncertain parameters. Further, a fundamental theory for guaranteeing stability of the SMP systems is established. It is challenging to analyze the SMP systems owing to both the uncertainty and ran...
• Bayesian Formal Synthesis of Unknown Systems via Robust Simulation Relations AI翻译
  作者：Schoen, Oliver; van Huijgevoort, Birgit; Haesaert, Sofie; Soudjani, Sadegh
  作者单位：Newcastle University - UK; Max Planck Society
  摘要：This article addresses the problem of data-driven computation of controllers that are correct by design for safety-critical systems and can provably satisfy (complex) functional requirements. With a focus on continuous-space stochastic systems with parametric uncertainty, we propose a two-stage approach that decomposes the problem into a learning stage and a robust formal controller synthesis stage. The first stage utilizes available Bayesian regression results to compute robust credible sets ...
• On Recursive Feasibility and Stability of Constrained Output Regulation AI翻译
  作者：Liu, Jinhao; Yang, Jun; Yan, Yunda; Tan, Yuan; Wang, Xiangyu; Li, Shihua
  作者单位：Southeast University - China; Southeast University - China; Loughborough University; University of London; University College London
  摘要：The constrained output regulation problems of discrete-time linear systems are studied under the model predictive control framework. The unique feature of the proposed control approach lies in addressing the technical challenges related to recursive feasibility of the optimization problem and stability of the closed-loop system posed by exogenous signal estimation and estimation errors. First, the original system is transformed into a new dynamic system by means of regulator equations and dist...
• A Uniform Framework of Yau-Yau Algorithm Based on Deep Learning With the Capability of Overcoming the Curse of Dimensionality AI翻译
  作者：Chen, Xiuqiong; Sun, Zeju; Tao, Yangtianze; Yau, Stephen S. -T.
  作者单位：Renmin University of China; Tsinghua University; Tsinghua University
  摘要：In numerous application areas, high-dimensional nonlinear filtering is still a challenging problem. The introduction of deep learning and neural networks has improved the efficiency of classical algorithms and they perform well in many practical tasks. However, a theoretical interpretation of their feasibility is still lacking. In this article, we exploit the representational ability of recurrent neural networks (RNNs) and provide a computationally efficient and optimal framework for nonlinear...
• A Generalized Passivity Theory Over Abstract Time Domains AI翻译
  作者：Moreschini, Alessio; Bin, Michelangelo; Astolfi, Alessandro; Parisini, Thomas
  作者单位：Imperial College London; University of Bologna; University of Cyprus; University of Rome Tor Vergata; Aalborg University; University of Trieste
  摘要：Passivity is a well-established concept for continuous-time systems. Yet, its application to discrete time, delay, or other classes of systems is somewhat limited, leading to inconsistencies and disparities. In this article, we study a new notion, $\varrho$-passivity, which reduces to standard passivity in the continuous-time case but addresses some of the aforementioned limitations when applied to other classes of systems. In particular, in an abstract input-output setting, we show that $\var...