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In this paper we design a linear dynamic output feedback controller for a ball and beam system based on an identified neural state space model. This is done by applying dynamic backpropagation, constrained by NLq internal or I/O stability conditions. The performance of the controller has been tested on a real ball and beam set-up.
In cooperation with the Belgian gas company we investigated models for the analysis of the Belgian gas consumption. Different static time varying models with temperature as only input have been studied and compared. The main purpose of those models is to do market analysis and more in particular to normalize the gas consumption with respect to temperature.
We regard a network of coupled nonlinear dynamical systems that we want to control optimally. The cost function is assumed to be separable and convex. The algorithm we propose to address the numerical solution of this problem is based on two ingredients: first, we exploit the convex problem structure using a sequential convex programming framework that linearizes the nonlinear dynamics in each iteration...
A new sparse kernel model for spectral clustering is presented. This method is based on the incomplete Cholesky decomposition and can be used to efficiently solve large-scale spectral clustering problems. The formulation arises from a weighted kernel principal component analysis (PCA) interpretation of spectral clustering. The interpretation is within a constrained optimization framework with primal...
This paper reviews triple mode predictive control for LTI systems, and proposes a new algorithm for robust triple mode predictive control for constrained linear systems described by polytopic uncertainty models. The approach significantly enlarges the feasibility region compared to robust dual mode approaches. The efficacy of the approach is demonstrated with numerical examples
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