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作者：Li, Zhongguo; Chen, Wen-Hua; Yang, Jun
作者单位：Loughborough University
摘要：A concurrent learning framework is developed for source search in an unknown environment using autonomous platforms equipped with onboard sensors. Distinct from the existing solutions that require significant computational power for Bayesian estimation and path planning, the proposed solution is computationally affordable for onboard processors. A new concept of concurrent learning using multiple parallel estimators is proposed to learn the operational environment and quantify estimation uncer...
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作者：Tsiamis, Anastasios; Pappas, George J.
作者单位：Swiss Federal Institutes of Technology Domain; ETH Zurich; University of Pennsylvania
摘要：In this article, we consider the problem of predicting observations generated online by an unknown, partially observable linear system, which is driven by Gaussian noise. In the linear Gaussian setting, the optimal predictor in the mean square error sense is the celebrated Kalman filter, which can be explicitly computed when the system model is known. When the system model is unknown, we have to learn how to predict observations online based on finite data, suffering possibly a nonzero regret ...
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作者：Zhu, Jingxuan; Liu, Ji
作者单位：State University of New York (SUNY) System; Stony Brook University; State University of New York (SUNY) System; Stony Brook University
摘要：This article studies a distributed multiarmed bandit problem with heterogeneous observations of rewards. The problem is cooperatively solved by N agents assuming each agent faces a common set of M arms yet observes only local biased rewards of the arms. The goal of each agent is to minimize the cumulative expected regret with respect to the true rewards of the arms, where the mean of each arm's true reward equals the average of the means of all agents' observed biased rewards. Each agent recur...
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作者：Lederer, Armin; Yang, Zewen; Jiao, Junjie; Hirche, Sandra
作者单位：Technical University of Munich; Technical University of Munich; Harbin Engineering University
摘要：For single agent systems, probabilistic machine learning techniques such as Gaussian process regression have been shown to be suitable methods for inferring models of unknown nonlinearities, which can be employed to improve the performance of control laws. While this approach can be extended to the cooperative control of multiagent systems, it leads to a decentralized learning of the unknown nonlinearity, i.e., each agent independently infers a model. However, decentralized learning can potent...
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作者：Parsi, Anilkumar; Iannelli, Andrea; Smith, Roy S.
作者单位：Swiss Federal Institutes of Technology Domain; ETH Zurich
摘要：A finite horizon optimal tracking problem is considered for linear dynamical systems subject to parametric uncertainties in the state-space matrices and exogenous disturbances. A suboptimal solution is proposed using a model predictive control (MPC) based explicit dual control approach, which enables active uncertainty learning. A novel algorithm for the design of robustly invariant online terminal sets and terminal controllers is presented. Set membership identification is used to update the ...
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作者：Du, Bin; Qian, Kun; Claudel, Christian; Sun, Dengfeng
作者单位：Nanjing University of Aeronautics & Astronautics; University of Texas System; University of Texas Austin; Purdue University System; Purdue University
摘要：This article presents a learning-based algorithm for solving the online source-seeking problem with a multiagent system under an unknown dynamical environment. Our algorithm, building on a notion termed as dummy confidence upper bound (D-UCB), integrates both estimation of the unknown environment and task planning for the multiple agents simultaneously, and as a result, enables the multiple agents to track the extremum spots of the dynamical environment in an online manner. Unlike the standard...
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作者：Reed, Emily A. A.; Ramos, Guilherme; Bogdan, Paul; Pequito, Sergio
作者单位：University of Southern California; Universidade de Lisboa; Universidade de Lisboa; Uppsala University
摘要：Finding strongly connected components (SCCs) and the diameter of a directed network play a key role in a variety of machine learning and control theory problems. In this article, we provide for the first time a scalable distributed solution for these two problems by leveraging dynamical consensus-like protocols to find the SCCs. The proposed solution has a time complexity of O(NDd(max) (in-degree)), where N is the number of vertices in the network, D is the (finite) diameter of the network, an...
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作者：Khosravi, Mohammad
作者单位：Delft University of Technology
摘要：In this work, we consider the problem of learning the Koopman operator for discrete-time autonomous systems. The learning problem is formulated as a generic constrained regularized empirical loss minimization in the infinite-dimensional space of linear operators. We show that a representer theorem holds for the introduced learning problem under certain but general conditions, which allows convex reformulation of the problem in a specific finite-dimensional space without any approximation and l...
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作者：Kim, Seong-hun; Lee, Hanna; Cho, Namhoon; Kim, Youdan
作者单位：Seoul National University (SNU); Cranfield University
摘要：We propose an active weighting algorithm for composite adaptive control to reduce the state and estimate errors while maintaining the estimation quality. Unlike previous studies that construct the composite term by simply stacking, removing, and pausing observed data, the proposed method efficiently utilizes the data by providing a theoretical set of weights for observations that can actively manipulate the composite term to have desired characteristics. As an example, a convex optimization fo...
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作者：Bongard, Joscha; Berberich, Julian; Koehler, Johannes; Allgoewer, Frank
作者单位：Technical University of Munich; University of Stuttgart; University of Stuttgart; Swiss Federal Institutes of Technology Domain; ETH Zurich
摘要：In this article, we provide a theoretical analysis of closed-loop properties of a simple data-driven model predictive control (MPC) scheme. The formulation does not involve any terminal ingredients, thus allowing for a simple implementation without (potential) feasibility issues. The proposed approach relies on an implicit description of linear time-invariant systems based on behavioral systems theory, which only requires one input-output trajectory of an unknown system. For the nominal case w...
