Results 251 to 260 of about 2,459,786 (308)

Chance-Constrained H∞ State Estimation for Recursive Neural Networks Under Deception Attacks and Energy Constraints: The Finite-Horizon Case

IEEE Transactions on Neural Networks and Learning Systems, 2022
In this article, the chance-constrained $H_{\infty }$ state estimation problem is investigated for a class of time-varying neural networks subject to measurements degradation and randomly occurring deception attacks.
Fanrong Qu, Engang Tian, Xia Zhao
semanticscholar   +1 more source

Probabilistic Collision Checking With Chance Constraints

IEEE Transactions on Robotics, 2011
Obstacle avoidance, and by extension collision checking, is a basic requirement for robot autonomy. Most classical approaches to collision-checking ignore the uncertainties associated with the robot and obstacle's geometry and position. It is natural to use a probabilistic description of the uncertainties.
Du Toit, Noel E., Burdick, Joel W.
openaire   +2 more sources

Probabilistic distribution planning: Including the interactions between chance constraints and renewable energy

, 2020
Integration of renewable energy resources introduces several uncertainties for planning of distribution networks, requiring the consideration of random variables.
F. Ugranli
semanticscholar   +1 more source

Chance Constrained Programming with Joint Constraints

Operations Research, 1965
This paper considers the mathematical properties of chance constrained programming problems where the restriction is on the joint probability of a multivariate random event. One model that is considered arises when the right-handside constants of the linear constraints are random.
Miller, B. L., Wagner, H. M.
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On integrated chance constraints

1986
In Chapter 3 we introduced integrated chance constraints (ICCs) as a modeling tool for here-and-now stochastic programming problems; see (3.28). In the present chapter we analyze the mathematical properties of this new concept in some detail. Let us review its rationale. As indicated in Section 3.2, if in the constraints of a linear programming problem
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Stochastic Linear Programming with Chance Constraints

International Economic Review, 1970
An ordinary linear programming model is said to be chance-constrained if its linear constraints are associated with a set of measures indicating the extent of violation of the constraints. The CCP approach usually assumes the resource vector to be normally and mutually independently distributed and then derives a deterministic concave programming ...
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The Linear Quadratic Regulator with chance constraints

2013 European Control Conference (ECC), 2013
This paper is concerned with the design of linear state feedback control laws for linear systems with additive Gaussian disturbances. The objective is to find the feedback gain that minimizes a quadratic cost function in closed-loop operation, while observing chance constraints on the input and/or the state. It is shown that this problem can be cast as
Georg Schildbach, Paul Goulart, Manfred Morari   +2 more
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Chance-Constrained Programming with Joint Constraints

Operations Research, 1974
Miller and Wagner have shown that a deterministic equivalent of a joint chance-constrained programming model with independent random right-hand-side elements is a concave programming problem. This paper obtains similar equivalents for chance-constrained programming models with coefficient matrices whose elements are normally distributed and with ...
openaire   +1 more source

A Constraint-Tightening Approach to Nonlinear Model Predictive Control with Chance Constraints for Stochastic Systems

American Control Conference, 2019
This paper presents a nonlinear model predictive control (NMPC) strategy for stochastic systems subject to chance constraints. The notion of stochastic tubes is extended to nonlinear systems to present a constraint tightening strategy that ensures ...
T. Santos, Angelo D. Bonzanini, Tor Aksel N. Heirung, A. Mesbah   +3 more
semanticscholar   +1 more source

Model Predictive Control for Stochastic Max-Plus Linear Systems With Chance Constraints

IEEE Transactions on Automatic Control, 2019
The topic of this paper is model predictive control (MPC) for max-plus linear systems with stochastic uncertainties the distribution of which is supposed to be known. We consider linear constraints on the inputs and the outputs. Due to the uncertainties,
Jia Xu, T. van den Boom, B. de Schutter
semanticscholar   +1 more source