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Solving unstructured linear differential-algebraic equations in the presence of numeric uncertainty in the equation coefficients is an ill-posed problem — arbitrarily small changes in the coefficients of the leading matrix may change the solution completely. To obtain well-posedness, assumptions must be made, even for DAE of index 0. In this work, we propose assumptions about the system poles to obtain...
Top-k processing in Uncertain Databases is semantically and computationally different from traditional top-k processing. The interplay between score and uncertainty information makes traditional top-k processing techniques inapplicable to uncertain databases. The existing approaches are all based on the assumption that the underlying data are exact (or certain). We construct a framework that encapsulates...
The use of natural gas for power generation has been rising rapidly in the past two decades. To ensure the security of supply of gas to the market and meet strict specifications on gas quality (e.g., sulfur content), natural gas production network design must address uncertainty explicitly as well as tracking the quality of each gas flow in the entire system. This leads to the stochastic pooling problem,...
This paper presents the design of a robust Iterative Learning Control (ILC) algorithm for linear systems in the presence of parametric uncertainties and repetitive disturbances. The robust ILC design is formulated as a min-max problem with a quadratic performance index subjected to constraints of the control input. Employing Lagrange duality, we can reformulate the robust ILC design as a convex optimization...
We study how the maximum/minimum gain of a decentralized controller influences the maximum/minimum gain of the corresponding closed loop transfer function, and provide lower bounds on the best closed-loop performance achievable by decentralized controllers satisfying additional frequency domain restrictions. These bounds are obtained using generalizations of the structured singular value and approximated...
In this paper, we present the design of a robust Iterative Learning Control (ILC) algorithm for a single flexible link in the presence of parametric uncertainty. The robust ILC design is formulated as a min-max problem with a quadratic performance index. An upper bound of the worst-case performance is employed in the min-max problem. Applying Lagrange duality to the min-max problem, we can reformulate...
Based on the analysis of the standard Particle Swarm Optimization and the characteristic of typical multi-intersection for urban trunk road, a traffic flow forecasting model using dynamic recursion neural network is presented. The feature of this network is that the output of the hidden layer connects to the input of itself through the delay and storage of the context layer. The method of self-connection...
A performance criterion given as a bound on the H?? norm of some transfer function is known to be equivalent to the stability condition with respect to the auxiliary unstructured uncertainty. Therefore, robust stability and performance of the system in the presence of a block diagonal norm bounded uncertainty with (m) elements is easily transformed into a structured singular value problem with respect...
This paper presents a technique for designing fixed order dynamic compensators in controller canonical form which are robust to both structured and unstructured uncertainty. The formulation uses an approach which combines an H2 and H∞ optimization process. Specificaly, a quadratic performance index is minimized subject to a constraint on the H∞, norm of the closed loop transfer function from disturbances...
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