7期

最新文章

• Output Feedback Model Reference Adaptive Control of Piecewise Affine Systems With Parameter Convergence Analysis AI翻译
  作者：Liu, Tong; Buss, Martin
  作者单位：Technical University of Munich
  摘要：In this article, the output feedback-based direct model reference adaptive control of piecewise affine systems and its parameter convergence are investigated. Under the slow switching assumption, it is shown that all the closed-loop signals are bounded and the output tracking error is small in the mean square sense. Built upon this result, the estimation error of controller parameters is proved to converge to a residual set if the input signal is sufficiently rich. The relationship between the...
• On the Characterization of Butterfly and Multiloop Hysteresis Behavior AI翻译
  作者：Vasquez-Beltran, Marco Augusto; Jayawardhana, Bayu; Peletier, Reynier
  作者单位：University of Groningen; University of Groningen; Kapteyn Astronomical Institute
  摘要：While it is widely used to represent hysteresis phenomena with unidirectional-oriented loops, we study in this article the use of Preisach operator for describing hysteresis behavior with multidirectional-oriented loops. This complex hysteresis behavior is commonly found in advanced materials, such as, shape-memory alloys or piezoelectric materials, that are used for high-precision sensor and actuator systems. We provide characterization of the Preisach operators exhibiting such input-output b...
• Optimal Control for Nonlinear Systems Driven by a Known Exogenous Signal AI翻译
  作者：Sassano, Mario; Mylvaganam, Thulasi; Astolfi, Alessandro
  作者单位：University of Rome Tor Vergata; Imperial College London; Imperial College London
  摘要：We consider optimal control problems for continuous-time systems with time-dependent dynamics, in which the time-dependence arises from the presence of a known exogenous signal. The problem has been elegantly solved in the case of linear input-affine systems, for which it has been shown that the solution has a remarkable structure: It is given by the sum of two contributions; a state feedback, which coincides with the unperturbed optimal control law, and a purely feedforward term in charge of ...
• Differential Equivalence for Linear Differential Algebraic Equations AI翻译
  作者：Tognazzi, Stefano; Tribastone, Mirco; Tschaikowski, Max; Vandin, Andrea
  作者单位：University of Konstanz; IMT School for Advanced Studies Lucca; Aalborg University; Scuola Superiore Sant'Anna; Technical University of Denmark
  摘要：Differential-algebraic equations (DAEs) are a widespread dynamical model that describes continuously evolving quantities defined with differential equations, subject to constraints expressed through algebraic relationships. As such, DAEs arise in many fields ranging from physics, chemistry, and engineering. In this article, we focus on linear DAEs, and develop a theory for their minimization up to an equivalence relation. We present differential equivalence, which relates DAE variables that ha...
• Decoupled Data-Based Approach for Learning to Control Nonlinear Dynamical Systems AI翻译
  作者：Wang, Ran; Parunandi, Karthikeya S.; Yu, Dan; Kalathil, Dileep; Chakravorty, Suman
  作者单位：Texas A&M University System; Texas A&M University College Station; Nanjing University of Aeronautics & Astronautics; Texas A&M University System; Texas A&M University College Station
  摘要：This article addresses the problem of learning the optimal control policy for a nonlinear stochastic dynamical. This problem is subject to the curse of dimensionality associated with the dynamic programming method. This article proposes a novel decoupled data-based control (D2C) algorithm that addresses this problem using a decoupled, open-loop-closed-loop, approach. First, an open-loop deterministic trajectory optimization problem is solved using a black-box simulation model of the dynamical ...
• Online Reinforcement Learning of Optimal Threshold Policies for Markov Decision Processes AI翻译
  作者：Roy, Arghyadip; Borkar, Vivek; Karandikar, Abhay; Chaporkar, Prasanna
  作者单位：University of Illinois System; University of Illinois Urbana-Champaign; Indian Institute of Technology System (IIT System); Indian Institute of Technology (IIT) - Bombay; Indian Institute of Technology System (IIT System); Indian Institute of Technology (IIT) - Kanpur
  摘要：To overcome the curses of dimensionality and modeling of dynamic programming methods to solve Markov decision process problems, reinforcement learning (RL) methods are adopted in practice. Contrary to traditional RL algorithms, which do not consider the structural properties of the optimal policy, we propose a structure-aware learning algorithm to exploit the ordered multithreshold structure of the optimal policy, if any. We prove the asymptotic convergence of the proposed algorithm to the opt...
• Structural Equilibrium Control of Network Systems AI翻译
  作者：Azuma, Shun-ichi
  作者单位：Nagoya University
  摘要：During the analysis and design of a network system, a frequently encountered situation is when the network structure is known but the node dynamics is practically unknown. Thus, structural analysis in which the prior knowledge is mostly about the network structure has received attention in recent years. Motivated by this line of research, this article focuses on a structural design problem called the structural equilibrium control problem, which is used to find a sign pattern in the control in...
• Robust Discrete-Time Pontryagin Maximum Principle on Matrix Lie Groups AI翻译
  作者：Joshi, Anant A.; Chatterjee, Debasish; Banavar, Ravi N.
  作者单位：University of Illinois System; University of Illinois Urbana-Champaign; Indian Institute of Technology System (IIT System); Indian Institute of Technology (IIT) - Bombay
  摘要：This article considers a discrete-time robust optimal control problem on matrix Lie groups. The underlying system is assumed to be perturbed by exogenous unmeasured bounded disturbances, and the control problem is posed as a min-max optimal control wherein the disturbance is the adversary and tries to maximize a cost that the control tries to minimize. Assuming the existence of a saddle point in the problem, we present a version of the Pontryagin maximum principle (PMP) that encapsulates first...
• A Scalable Strategy for the Identification of Latent-Variable Graphical Models AI翻译
  作者：Alpago, Daniele; Zorzi, Mattia; Ferrante, Augusto
  作者单位：University of Padua
  摘要：In this article, we propose an identification method for latent-variable graphical models associated with autoregressive (AR) Gaussian stationary processes. The identification procedure exploits the approximation of AR processes through stationary reciprocal processes thus benefiting of the numerical advantages of dealing with block-circulant matrices. These advantages become more and more significant as the order of the process gets large. We show how the identification can be cast in a regul...
• A Game-Theoretic Framework for Security-Aware Sensor Placement Problem in Networked Control Systems AI翻译
  作者：Pirani, Mohammad; Nekouei, Ehsan; Sandberg, Henrik; Johansson, Karl Henrik
  作者单位：University of Toronto; City University of Hong Kong; Royal Institute of Technology
  摘要：This article studies the sensor placement problem in a leader-follower networked control system for improving its security against cyber-physical attacks. In a zero-sum game, the attacker selects f nodes of the network to attack, and the detector places f sensors to detect the presence of the attack signals. In our formulation, the attacker's objective is to have a large impact on a target node in the network while being as little visible as possible to the detector. The detector, however, see...