2024

最新文章

• Distributed Control Design for Solving Linear Algebraic Equations via Adjustable Domains AI翻译
  作者：Li, Juntao; Liang, Cong; Meng, Deyuan
  作者单位：Henan Normal University; Beihang University; Beihang University
  摘要：This article aims to develop a general and designable distributed algorithm for solving linear algebraic equations (LAEs), which departs from the design framework based on orthogonal projection. The concept of adjustable domains for the parameter matrix is introduced, enabling the algorithm to derive flexible and variable updating rules for agents. By leveraging adjustable domains in control design, all agents can exponentially converge to a common (least squares) solution of (un)solvable LAEs...
• Pointwise Exponential Stability of State Consensus With Intermittent Communication AI翻译
  作者：Phillips, Sean; Sanfelice, Ricardo G.
  作者单位：United States Department of Defense; United States Air Force; US Air Force Research Laboratory; University of California System; University of California Santa Cruz
  摘要：In this article, we propose a solution to the problem of achieving global consensus of the states of scalar integrator systems over a directed graph when the network connecting the agents is available only at isolated (and possibly aperiodic) time instances. We propose decentralized consensus protocols that, using such intermittent information obtained at communication times, globally and asymptotically drives the values of their states to an agreement value, with stability and robustness to p...
• Robust Constrained Consensus and Inequality-Constrained Distributed Optimization With Guaranteed Differential Privacy and Accurate Convergence AI翻译
  作者：Wang, Yongqiang; Nedic, Angelia
  作者单位：Clemson University; Arizona State University; Arizona State University-Tempe
  摘要：We address differential privacy for fully distributed optimization subject to a shared inequality constraint. By co-designing the distributed optimization mechanism and the differential-privacy noise injection mechanism, we propose the first distributed constrained optimization algorithm that can ensure both provable convergence to a global optimal solution and rigorous $\epsilon$-differential privacy, even when the number of iterations tends to infinity. Our approach does not require the Lagr...
• Distributed Resource Allocation Over Multiple Interacting Coalitions: A Game-Theoretic Approach AI翻译
  作者：Zhou, Jialing; Wen, Guanghui; Lv, Yuezu; Yang, Tao; Chen, Guanrong
  作者单位：Beijing Institute of Technology; Southeast University - China; Northeastern University - China; City University of Hong Kong
  摘要：Despite the fact that many distributed resource allocation (DRA) algorithms have been reported in the literature, it is still unknown how to allocate resources optimally over multiple interacting coalitions. One major challenge in solving such a problem is that the relevance of one coalition's decision to other coalitions' benefits may lead to conflicts of interest among these coalitions. Within this context, a new game model is formulated in the present note, termed as resource allocation gam...
• Global Adaptive Tracking for Nonlinearly Parameterized Systems via Quantized Feedback AI翻译
  作者：Jia, Xianglei; Xu, Shengyuan
  作者单位：Hangzhou Dianzi University; Nanjing University of Science & Technology
  摘要：A new dynamic-scaling-based global adaptive quantized control approach is proposed for nonlinear systems with input and state quantization, which reduces some conservatism of relevant results, including matched nonlinearities, global Lipschitz continuity, and quantization error satisfying constant bound. A unique idea is used to deal with the quantized-state feedback problem, that is, the coefficient deviation problem in the decomposition of sector-bounded quantizer is converted to that of sol...
• Hybrid Control Barrier Functions for Continuous-Time Systems AI翻译
  作者：Marley, Mathias; Skjetne, Roger; Teel, Andrew R.
  作者单位：Norwegian University of Science & Technology (NTNU); University of California System; University of California Santa Barbara
  摘要：Control barrier functions (CBFs) enable constraint satisfaction in controlled dynamical systems, by mapping state constraints into state-dependent input constraints. This article investigates the use of hybrid CBF formulations for constraint satisfaction in continuous-time systems. Augmenting CBFs with logic variables enables solving control problems that are not solvable by continuous, or even discontinuous, control. Examples include robust deadlock resolution for vehicles moving in the prese...
• Optimal Communication and Control Strategies in a Cooperative Multiagent MDP Problem AI翻译
  作者：Sudhakara, Sagar; Kartik, Dhruva; Jain, Rahul; Nayyar, Ashutosh
  作者单位：University of Southern California
  摘要：The problem of controlling cooperative multiagent systems under different models of information sharing among agents has received significant attention in the recent literature. In this article, we consider a setup where rather than committing to a fixed and nonadaptive information sharing protocol (e.g., periodic sharing or no sharing, etc.), agents can dynamically decide at each time step whether to share information with each other and incur the resulting communication cost. This setup requ...
• Consensus of Discrete-Time Multiagent Systems With Transient Performance Constraints AI翻译
  作者：Zhang, Zhixin; Chen, Zhiyong
  作者单位：Central South University; University of Newcastle
  摘要：This note investigates the consensus of discrete-time multiagent systems, where the consensus error between neighboring agents is subject to transient performance constraints. The examination of transient performance constraints holds paramount importance in ensuring the secure and optimal functioning of real systems. Nevertheless, the existing body of research pertaining to constraints has predominantly focused on continuous-time systems, with limited attention given to discrete-time systems....
• Error Corrected References for Accelerated Convergence of Low Gain Norm Optimal Iterative Learning Control AI翻译
  作者：Owens, David H.; Chu, Bing
  作者单位：Zhengzhou University; University of Sheffield; University of Southampton
  摘要：To reduce the need for high gains (reduced control weighting) for fast convergence in norm optimal iterative learning control (NOILC), this article presents a simple data-driven mechanism for accelerating the convergence of low gain feedback NOILC controllers. The method uses a modification to the reference signal on each NOILC iteration using the measured tracking error from the previous iteration. The basic algorithm is equivalent to a gradient iteration combined with an NOILC iteration. The...
• Reinforcement Learning for Partially Observable Linear Gaussian Systems Using Batch Dynamics of Noisy Observations AI翻译
  作者：Yaghmaie, Farnaz Adib; Modares, Hamidreza; Gustafsson, Fredrik
  作者单位：Linkoping University; Michigan State University
  摘要：Reinforcement learning algorithms are commonly used to control dynamical systems with measurable state variables. If the dynamical system is partially observable, reinforcement learning algorithms are modified to compensate for the effect of partial observability. One common approach is to feed a finite history of input-output data instead of the state variable. In this article, we study and quantify the effect of this approach in linear Gaussian systems with quadratic costs. We coin the conce...