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A practical optimal sensor design strategy based on nonlinear closed-loop Monte-Carlo simulations is developed.
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This algorithm has been validated on both frequency and time domain experiments, allowing the validation on a nonlinear closed-loop system the performance evaluation tools.
This paper proposes an extension of neural network identification capabilities for on-line identification of a nonlinear closed-loop control system.
A computational framework based on nonlinear direct-adjoint looping is presented for optimizing mixing strategies for binary fluid systems.
The residual evaluation is carried out using a threshold based on an uncertain closed-loop framework.
The suggested approach uses iterative re-discretization based on clustering of closed-loop data.
This paper presents an approach to analyse the controllability of process alternatives based on the optimal closed-loop performance.
A detuning factor for each loop is specified based on the closed-loop time constant.
In this paper, optimal operating policies for HPLC separation processes based on a closed-loop recycling strategy are presented.
The improved proposed control design method with direct closed-loop pole position assessment using metric L2, is based on robust optimal regional closed-loop pole assignment technique.
The on-line closed-loop optimization strategy uses a descending horizon dynamic optimization algorithm based on nonlinear programming and an additive unknown disturbance for feedback.
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