6期

最新文章

• Computationally Efficient Robust Model Predictive Control for Uncertain System Using Causal State-Feedback Parameterization AI翻译
  作者：Georgiou, Anastasis; Tahir, Furqan; Jaimoukha, Imad M.; Evangelou, Simos A.
  作者单位：Imperial College London
  摘要：This article investigates the problem of robust model predictive control (RMPC) of linear-time-invariant discrete-time systems subject to structured uncertainty and bounded disturbances. Typically, the constrained RMPC problem with state-feedback parameterizations is nonlinear (and nonconvex) with a prohibitively high computational burden for online implementation. To remedy this, a novel approach is proposed to linearize the state-feedback RMPC problem, with minimal conservatism, through the ...
• Solid Boundary Output Feedback Control of the Stefan Problem: The Enthalpy Approach AI翻译
  作者：Petrus, Bryan; Chen, Zhelin; El-Kebir, Hamza; Bentsman, Joseph; Thomas, Brian G.
  作者单位：University of Illinois System; University of Illinois Urbana-Champaign; Colorado School of Mines
  摘要：By taking enthalpy-an internal energy of a diffusion-type system-as the system state and expressing it in terms of the temperature profile and the phase-change interface position, the output feedback boundary control laws for a fundamentally nonlinear single-phase one-dimensional (1-D) PDE process model with moving boundaries, referred to as the Stefan problem, are developed. The control objective is tracking of the spatiotemporal temperature and temporal interface (solidification front) traje...
• Value of Information in Feedback Control: Global Optimality AI翻译
  作者：Soleymani, Touraj; Baras, John S.; Hirche, Sandra; Johansson, Karl H.
  作者单位：Royal Institute of Technology; University System of Maryland; University of Maryland College Park; Technical University of Munich
  摘要：rate-regulation tradeoff, defined between two objective functions, one penalizing the packet rate and one the regulation cost, can express the fundamental performance bound of networked control systems. However, the characterization of the set of globally optimal solutions in this tradeoff for multidimensional Gauss-Markov processes has been an open problem. In this article, we characterize a policy profile that belongs to this set without imposing any restrictions on the information structure...
• Prescribed-Time Tracking Control of MIMO Nonlinear Systems With Nonvanishing Uncertainties AI翻译
  作者：Ye, Hefu; Song, Yongduan
  作者单位：Chongqing University
  摘要：It is highly desirable yet challenging to achieve zero-error tracking within a given short time period for uncertain multi-input-multi-output (MIMO) nonlinear systems. Previous results are primarily state-regulation oriented and are valid only for single-input single-output linear systems or nonlinear systems in normal-form. This note presents a tracking control solution for high-order MIMO nonlinear systems in strict-feedback-like form. The proposed control is able to achieve precise tracking...
• Adaptive Tracking Control With Global Performance for Output-Constrained MIMO Nonlinear Systems AI翻译
  作者：Kong, Linghuan; He, Wei; Liu, Zhijie; Yu, Xinbo; Silvestre, Carlos
  作者单位：University of Science & Technology Beijing; University of Science & Technology Beijing; University of Science & Technology Beijing; University of Macau; Universidade de Lisboa
  摘要：In this article, a novel adaptive tracking control technique is developed for multiple-input-multiple-output nonlinear systems with model uncertainty and under output constraints occurring in a limited time interval (OCOLT). The OCOLT, which is a type of constraints occurring sometime after (rather than the beginning of) system operation and with limited duration, can be found in many practical systems and has not been effectively addressed in the literature until now. By designing a new shift...
• Smoother Entropy for Active State Trajectory Estimation and Obfuscation in POMDPs AI翻译
  作者：Molloy, Timothy L.; Nair, Girish N.
  作者单位：University of Melbourne; Australian National University; University of Melbourne
  摘要：In this article, we study the problem of controlling a partially observed Markov decision process (POMDP) to either aid or hinder the estimation of its state trajectory. We encode the estimation objectives via the smoother entropy, which is the conditional entropy of the state trajectory given measurements and controls. Consideration of the smoother entropy contrasts with previous approaches that instead resort to marginal (or instantaneous) state entropies due to tractability concerns. By est...
• Reactive Symbolic Planning and Control in Dynamic Adversarial Environments AI翻译
  作者：Shamgah, Laya; Tadewos, Tadewos G.; Newaz, Abdullah Al Redwan; Karimoddini, Ali; Esterline, Albert C.
  作者单位：University of North Carolina; North Carolina A&T State University; University of North Carolina; North Carolina A&T State University; University of Louisiana System; University of New Orleans
  摘要：Satisfying both safety and reachability requirements in dynamic adversarial environments is very challenging, particularly when little or no information about the dynamics and intentions of the adversarial objects is available. Therefore, this article addresses the problem of path planning and control of autonomous vehicles in a dynamic adversarial reach-avoid scenario with two noncooperative vehicles and their competitive objectives: 1) reaching a target and avoiding the other vehicle for one...
• Maximally Permissive Supervisors for Nonblocking Similarity Control of Nondeterministic Discrete-Event Systems AI翻译
  作者：Li, Jinglun; Takai, Shigemasa
  作者单位：University of Osaka
  摘要：This article investigates a nonblocking similarity control problem for nondeterministic discrete-event systems, which is a problem of synthesizing a nonblocking supervisor such that the supervised system is simulated by the given specification. In this article, the state of the system is not required to be observable, and the event occurrence is allowed to be partially observed. We propose an algorithm that computes a nonblocking supervisor from a possibly blocking one by iteratively removing ...
• Sufficient Conditions for Convergent Recursive Extrapolation of qLPV Scheduling Parameters Along a Prediction Horizon AI翻译
  作者：Morato, Marcelo Menezes; Normey-Rico, Julio Elias; Sename, Olivier
  作者单位：Universidade Federal de Santa Catarina (UFSC); Communaute Universite Grenoble Alpes; Institut National Polytechnique de Grenoble; Universite Grenoble Alpes (UGA); Centre National de la Recherche Scientifique (CNRS)
  摘要：predictive control (MPC) algorithms have long been applied to nonlinear processes. In a quasi-linear parameter varying (qLPV) setting, nonlinearities are included into bounded scheduling parameters, which are given as a function of endogenous variables; these scheduling parameters are a priori unknown along a future prediction horizon, which complicates MPC design. To address this problem, the literature points out two options: robust MPC approaches, considering the scheduling to be uncertain;...
• Disturbance Observer-Based Boundary Control for an Antistable Stochastic Heat Equation With Unknown Disturbance AI翻译
  作者：Wu, Ze-Hao; Zhou, Hua-Cheng; Deng, Feiqi; Guo, Bao-Zhu
  作者单位：Foshan University; Central South University; South China University of Technology; Chinese Academy of Sciences; Academy of Mathematics & System Sciences, CAS; North China Electric Power University
  摘要：In this article, a novel control strategy namely disturbance observer-based control is first applied to stabilization and disturbance rejection for an antistable stochastic heat equation with Neumann boundary actuation and unknown boundary external disturbance generated by an exogenous system. A disturbance observer-based boundary control is designed based on the backstepping approach and estimation/cancellation strategy, where the unknown disturbance is estimated in real time by a disturbance...