11期

最新文章

• Adaptive Internal Model Theory of the Oculomotor System and the Cerebellum AI翻译
  作者：Broucke, Mireille E.
  作者单位：University of Toronto
  摘要：In this article, we present a computational model of the oculomotor system and the cerebellum. In contrast with prevailing theories of cerebellar function, we propose the cerebellum embodies adaptive internal models of all persistent, exogenous, deterministic signals acting on the body and observable through the error signals it receives. Our model is validated by simulations, recovering results from a number of oculomotor experiments.
• New Results on the Local Linear Convergence of ADMM: A Joint Approach AI翻译
  作者：Erseghe, Tomaso
  作者单位：University of Padua
  摘要：Thanks to its versatility, its simplicity, and its fast convergence, alternating direction method of multipliers (ADMM) is among the most widely used approaches for solving a convex problem in distributed form. However, making it running efficiently is an art that requires a fine tuning of system parameters according to the specific application scenario, and which ultimately calls for a thorough understanding of the hidden mechanisms that control the convergence behavior. In this framework, we...
• Relevance of Network Characteristics to Controllability Degree AI翻译
  作者：Hou, Baoyu
  作者单位：Qingdao University
  摘要：This article studies the controllability degree via analyzing the condition number of Gramian matrix. Our aim is to explore how the network characteristics affect the controllability degree. Specifically, we prove that a large time parameter would worsen the controllability degree. The time parameter could be understood as the network coupling strength. For directed path networks, we derive how edge weights and time parameter jointly determine the best controllability degree. Furthermore, we p...
• Stabilization of a Class of Underactuated Nonlinear Systems via Underactuated Back-Stepping AI翻译
  作者：Jiangand, Jingjing; Astolfi, Alessandro
  作者单位：Loughborough University; Imperial College London; University of Rome Tor Vergata
  摘要：The stabilization problem for a class of nonlinear systems is solved via a novel method inspired by back-stepping. The method, that we call underactuated back-stepping, is introduced by solving the stabilization problem for an inertia wheel pendulum and it is then developed for a class of underactuated mechanical systems. The properties of the resulting closed-loop systems are studied in detail and case studies are given to show the effectiveness of the proposed method.
• An Iterative Data-Driven Linear Quadratic Method to Solve Nonlinear Discrete-Time Tracking Problems AI翻译
  作者：Possieri, Corrado; Incremona, Gian Paolo; Calafiore, Giuseppe C.; Ferrara, Antonella
  作者单位：Consiglio Nazionale delle Ricerche (CNR); Polytechnic University of Milan; Polytechnic University of Turin; Consiglio Nazionale delle Ricerche (CNR); Istituto di Elettronica e di Ingegneria dell'Informazione e delle Telecomunicazioni (IEIIT-CNR); University of Pavia
  摘要：The objective of this article is to introduce a novel data-driven iterative linear quadratic (LQ) control method for solving a class of nonlinear optimal tracking problems. Specifically, an algorithm is proposed to approximate the Q-factors arising from LQ stochastic optimal tracking problems. This algorithm is then coupled with iterative LQ-methods for determining local solutions to nonlinear optimal tracking problems in a purely data-driven setting. Simulation results highlight the potential...
• Cooperative Adaptive Containment Control With Parameter Convergence via Cooperative Finite-Time Excitation AI翻译
  作者：Yuan, Chengzhi; Stegagno, Paolo; He, Haibo; Ren, Wei
  作者单位：University of Rhode Island; University of Rhode Island; University of California System; University of California Riverside
  摘要：This article addresses the problem of cooperative adaptive containment control for multiagent systems, which specifies the objective of jointly achieving containment control and accurate adaptive learning/identification of unknown system parameters. We consider a class of linear uncertain multiagent systems with multiple leaders subject to bounded unmeasurable inputs and multiple followers subject to unknown system dynamics. A novel cooperative adaptive containment control architecture is prop...
• Modularity in Design of Dynamical Network Systems: Retrofit Control Approach AI翻译
  作者：Ishizaki, Takayuki; Sasahara, Hampei; Inoue, Masaki; Kawaguchi, Takahiro; Imura, Jun-ichi
  作者单位：Institute of Science Tokyo; Tokyo Institute of Technology; Royal Institute of Technology; Keio University; Gunma University
  摘要：In this article, we develop a modular design method of decentralized controllers for linear dynamical network systems, where multiple subcontroller designers aim at individually regulating their local control performance with accessibility only to their respective subsystem models. First, we derive a constrained version of the Youla parameterization that characterizes all retrofit controllers for a single subcontroller, defined as an add-on-type subcontroller that manages a subsystem. The resu...
• Componentwise Holder Inference for Robust Learning-Based MPC AI翻译
  作者：Maria Manzano, Jose; Munoz de la Pena, David; Calliess, Jan-Peter; Limon, Daniel
  作者单位：Universidad Loyola Andalucia; University of Sevilla; University of Oxford
  摘要：This article presents a novel learning method based on componentwise Holder continuity, which allows one to consider independently the contribution of each input to each output of the function to be learned. The method provides a bounded prediction error, and its learning property is proven. It can be used to obtain a predictor for a nonlinear robust learning-based predictive controller for constrained systems. The resulting controller achieves better closed loop performance and larger domains...
• Indirect Adaptive MPC for Discrete-Time LTI Systems With Parametric Uncertainties AI翻译
  作者：Dhar, Abhishek; Bhasin, Shubhendu
  作者单位：Indian Institute of Technology System (IIT System); Indian Institute of Technology (IIT) - Delhi
  摘要：This article addresses the problem of controlling discrete-time linear time-invariant systems with parametric uncertainties in the presence of hard state and input constraints. A suitably designed gradient-descent-based indirect adaptive controller, used to handle parametric uncertainties, is combined with a model predictive control (MPC) algorithm, which guarantees constraint satisfaction. An estimated model of the actual uncertain plant is used for predictions of the future states. The param...
• Coordinated Online Learning for Multiagent Systems With Coupled Constraints and Perturbed Utility Observations AI翻译
  作者：Tampubolon, Ezra; Boche, Holger
  作者单位：Technical University of Munich; Ruhr University Bochum
  摘要：Competitive noncooperative online decision-making agents whose actions increase congestion of scarce resources constitute a model for widespread modern large-scale applications. To ensure sustainable resource behavior, we introduce a novel method to steer the agents toward a stable population state, fulfilling the given coupled resource constraints. The proposed method is a decentralized resource pricing method based on the resource loads resulting from the augmentation of the game's Lagrangia...