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作者：Liu, Tong; Krstic, Miroslav; Jiang, Zhong-Ping
作者单位：New York University; New York University Tandon School of Engineering; University of California System; University of California San Diego
摘要：This note studies the distributed feedbak optimization for linear multiagent systems without precise knowledge of cost functions and agent dynamics. The goal is to regulate the outputs of the agents toward an unknown minimizer of a sum of local costs. To achieve this, distributed reference signals are combined with an extremum seeking mechanism to search for the minimizer. Meanwhile, each agent steers its output toward the designed reference signal using a learning-based adaptive optimal track...
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作者：Yu, Xin; Lin, Wei
作者单位：Jiangsu Normal University; University System of Ohio; Case Western Reserve University
摘要：This article considers the problem of input delay tolerance (IDT) for stochastic control systems that are almost surely asymptotically stabilizable but inherently nonlinear (e.g., neither locally Lipschitz continuous nor linear growth and nonsmoothly stabilizable). Using the notions of almost sure forward completeness and weighted homogeneity, together with the existence of the solutions of continuous time-delay stochastic systems, we prove under Lyapunov-like conditions that almost sure asymp...
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作者：Zhao, Qianhong; Tao, Gang
作者单位：University of Virginia
摘要：This article develops an adaptive state tracking control scheme for discrete-time systems, using least-squares algorithms, as the new solution to the long-standing discrete-time adaptive state tracking control problem. The system stability and state tracking properties are proved mathematically. The developed adaptive state tracking control scheme, combined with a newly proposed collision avoidance mechanism, is applied to a multirobot system to achieve tracking objectives. Simulation results ...
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作者：Dai, Pengcheng; Mo, Yuanqiu; Yu, Wenwu; Ren, Wei
作者单位：Southeast University - China; Southeast University - China; Purple Mountain Laboratories; University of California System; University of California Riverside
摘要：This article studies the networked multiagent reinforcement learning problem, where the objective of agents is to collaboratively maximize the discounted average cumulative rewards. Different from the existing methods that suffer from poor expression due to linear function approximation, we propose a distributed neural policy gradient algorithm that features two innovatively designed neural networks, specifically for the approximate Q-functions and policy functions of agents. This distributed ...
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作者：Weng, Chuanghong; Nekouei, Ehsan; Johansson, Karl H.
作者单位：City University of Hong Kong; Royal Institute of Technology
摘要：In this article, we study the optimal privacy-aware estimation problem for a (nonlinear or non-Gaussian) system where a private process derives the system's states. In our setup, the private process is modeled as a first-order Markov chain, and the state estimates are shared with an untrusted party, called the adversary, who might attempt to infer the private process based on the state estimates. We cast the optimal design of a privacy-aware estimator as an optimization problem that minimizes ...
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作者：Yang, Songlin; Olaru, Sorin; Rodriguez-Ayerbe, Pedro; Dorea, Carlos E. T.
作者单位：Universite Paris Saclay; Centre National de la Recherche Scientifique (CNRS); Universidade Federal do Rio Grande do Norte
摘要：This article deals with the fragility margins of discrete-time piecewise affine (PWA) closed-loop dynamics. The chosen framework is one of the nominal linear systems in closed-loop with a PWA controller implemented using a binary search tree mechanism for effective gain selection. Our objective is to preserve the properties of nominal dynamics, particularly the positive invariance under perturbations in the control law representation. The main contribution revolves around defining and construc...
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作者：Zhang, Wentao; Hu, Guoqiang; Zhang, Hui; Wang, Yaonan
作者单位：Nanyang Technological University; Hunan University; Nanyang Technological University; Hunan University
摘要：When confronting a practical dial-a-ride problem (DARP), addressing the transportation demands of joining and removal at any operational time is of practical significance yet a theoretical challenge. To this end, this article formulates the DARP into an architecture that integrates a physical agent (vehicle or robot) and an information decision that suffers from a fluctuation of vector fields with respect to mixed-integer linear programming (MILP). Specifically, physical agents ensure that veh...
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作者：Lin, Yeming; Liu, Kun; Bistritz, Ilai; Ma, Qian; Xia, Yuanqing
作者单位：Beijing Institute of Technology; Tel Aviv University; Nanjing University of Science & Technology
摘要：This article addresses the bandit game problem subject to privacy leakage, where the cooperative players aim to learn the optimal action profile that minimizes the global cost. The players do not have closed-form expressions for their payoff functions and can only receive the feedback of their local costs. We propose a privacy-preserving distributed bandit learning algorithm based on the residual gradient estimator, which adopts the stochastic quantization with a binary randomized response sch...
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作者：Yang, Guitao; Gallo, Alexander J.; Barboni, Angelo; Ferrari, Riccardo M. G.; Serrani, Andrea; Parisini, Thomas
作者单位：Loughborough University; Polytechnic University of Milan; Delft University of Technology; University of Bologna; Imperial College London; Aalborg University; University of Trieste
摘要：This article examines the properties of output-redundant systems, that is, systems possessing a larger number of outputs than inputs, through the lense of the geometric approach of Wonham et al. We begin by formulating a simple output allocation synthesis problem, which involves concealing input information from a malicious eavesdropper having access to the system output, while still allowing for a legitimate user to reconstruct it. It is shown that the solvability of this problem requires the...
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作者：Takamichi, Kenji; Susuki, Yoshihiko; Netto, Marcos
作者单位：Osaka Metropolitan University; Kyoto University; New Jersey Institute of Technology
摘要：We devise a novel formulation and propose the concept of modal participation factors to nonlinear dynamical systems. The original definition of modal participation factors (or simply participation factors) provides a simple yet effective metric. It finds use in theory and practice, quantifying the interplay between states and modes of oscillation in a linear time-invariant (LTI) system. In this article, with the Koopman operator framework, we present the results of participation factors for no...
