8期

最新文章

• A Deterministic Least Squares Approach for Simultaneous Input and State Estimation AI翻译
  作者：Gakis, Grigorios; Smith, Malcolm C. C.
  作者单位：University of Cambridge
  摘要：In this article, we consider a deterministic estimation problem to find the input and state of a linear dynamical system, which minimize a weighted integral squared error between the resulting output and the measured output. A completion of squares approach is used to find the unique optimum in terms of the solution of a Riccati differential equation. The optimal estimate is obtained from a two-stage procedure that is reminiscent of the Kalman filter. The first stage is an end-of-interval esti...
• Homothetic Tube Model Predictive Control for Nonlinear Systems AI翻译
  作者：Rakovic, Sasa V.; Dai, Li; Xia, Yuanqing
  作者单位：Beijing Institute of Technology
  摘要：Homothetic tube model predictive control is generalized to the setting of nonlinear discrete-time systems. The developed generalization is natural in the sense that the utilized assumptions in the nonlinear setting are suitably generalized variants of the related assumptions deployed in the linear case. The main analysis is carried out for Lipschitz systems, and it is subsequently extended to set-valued systems and other classes of nonlinear systems as well as particularized for parametericall...
• Explicit Solutions and Stability Properties of Homogeneous Polynomial Dynamical Systems AI翻译
  作者：Chen, Can
  作者单位：University of Michigan System; University of Michigan; Harvard University; Harvard University Medical Affiliates; Brigham & Women's Hospital; Harvard University; Harvard Medical School
  摘要：In this article, we provide a system-theoretic treatment of certain continuous-time homogeneous polynomial dynamical systems (HPDS) via tensor algebra. In particular, if a system of homogeneous polynomial differential equations can be represented by an orthogonally decomposable (odeco) tensor, we can construct its explicit solution by exploiting tensor Z-eigenvalues and Z-eigenvectors. We refer to such HPDS as odeco HPDS. By utilizing the form of the explicit solution, we are able to discuss t...
• Regression Filtration With Resetting to Provide Exponential Convergence of MRAC for Plants With Jump Change of Unknown Parameters AI翻译
  作者：Glushchenko, Anton; Petrov, Vladislav; Lastochkin, Konstantin
  作者单位：Russian Academy of Sciences
  摘要：This article proposes a new method to provide the exponential convergence of both the parameter and tracking errors of the composite adaptive control system without the persistent excitation (PE) requirement. Instead, the derived composite adaptive law ensures the abovementioned properties under the strictly weaker finite excitation (FE) condition. Unlike known solutions, in addition to the PE requirement relaxation, it provides better transient response under jump change of the plant uncertai...
• Stability of Nonlinear Impulsive Systems: Self-Triggered Comparison System Approach AI翻译
  作者：Li, Xiaodi; Wang, Yuhan; Song, Shiji
  作者单位：Shandong Normal University; Tsinghua University
  摘要：This article presents a new approach to the problem of globally asymptotical stability for nonlinear impulsive systems by utilizing self-triggered impulsive control (STIC), where impulse instants are determined by a well-designed self-triggered mechanism, which is based on the comparison approach. Different from event-triggered impulsive control strategy in which the triggering conditions need to be continuously or periodically monitored to determine whether an impulse should be generated, our...
• Fully Distributed Algorithm for Resource Allocation Over Unbalanced Directed Networks Without Global Lipschitz Condition AI翻译
  作者：Zhang, Jin; Liu, Lu; Wang, Xinghu; Ji, Haibo
  作者单位：Chinese Academy of Sciences; University of Science & Technology of China, CAS; City University of Hong Kong; City University of Hong Kong
  摘要：This note investigates the distributed optimal resource allocation problem of multiagent systems over unbalanced directed networks under the relaxed condition that the gradients of local cost functions are locally Lipschitz. The objective is to cooperatively drive the decision variables of the agents to the optimal solution, which minimizes the sum of the local cost functions, while ensuring that the network resource constraints and local feasibility constraints are satisfied. A novel distribu...
• A Small-Gain Theory for Abstract Systems on Topological Spaces AI翻译
  作者：Bin, Michelangelo; Parisini, Thomas
  作者单位：Imperial College London; University of Cyprus; University of Trieste
  摘要：We develop a small-gain theory for systems described by set-valued maps between topological spaces. We introduce an abstract notion of stability unifying the continuity properties underlying different existing concepts, such as Lyapunov stability of equilibria, sets, or motions, (incremental) input-output stability, asymptotic gain properties, and continuity with respect to fast-switching inputs. Then, we prove that a feedback interconnection enjoying a given abstract small-gain property is st...
• An Enhanced Feedback Adaptive Observer for Nonlinear Systems With Lack of Persistency of Excitation AI翻译
  作者：Tomei, Patrizio; Marino, Riccardo
  作者单位：University of Rome Tor Vergata
  摘要：The design of exponentially convergent adaptive observers is addressed for linear observable systems which are perturbed by linearly parameterized nonlinearities depending on measured signals (inputs and outputs). When there is a lack of persistency of excitation a new robust adaptive observer is presented, which performs an additional feedback depending on the kernel of the Gramian of the regressor vector, which is computed online, and generates state variables estimates whose estimation erro...
• Distributed Optimization Over Dependent Random Networks AI翻译
  作者：Aghajan, Adel; Touri, Behrouz
  作者单位：University of California System; University of California Santa Barbara; University of California System; University of California San Diego
  摘要：We study the averaging-based distributed optimization solvers over random networks. We show a general result on the convergence of such schemes using weight matrices that are row-stochastic almost surely and column-stochastic in expectation for a broad class of dependent weight-matrix sequences. In addition to implying many of the previously known results on this domain, our work shows the robustness of distributed optimization results to link failure. Also, it provides a new tool for synthesi...
• Combining Prior Knowledge and Data for Robust Controller Design AI翻译
  作者：Berberich, Julian; Scherer, Carsten W.; Allgower, Frank
  作者单位：University of Stuttgart; University of Stuttgart
  摘要：We present a framework for systematically combining data of an unknown linear time-invariant system with prior knowledge on the system matrices or on the uncertainty for robust controller design. Our approach leads to linear matrix inequality (LMI)-based feasibility criteria that guarantee stability and performance robustly for all closed-loop systems consistent with the prior knowledge and the available data. The design procedures rely on a combination of multipliers inferred via prior knowle...