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    ABSTRACT: This work introduces a unified framework for model invalidation and parameter estimation for nonlinear systems. We consider a model given by implicit nonlinear difference equations that are polynomial in the variables. Experimental data is assumed to be available as possibly sparse, uncertain, but (set-)bounded measurements. The derived approach is based on the reformulation of the invalidation and parameter/state estimation tasks into a set-based feasibility problem. Exploiting the polynomial structure of the considered model class, the resulting non-convex feasibility problem is relaxed into a convex semi-definite one, for which infeasibility can be efficiently checked. The parameter/state estimation task is then reformulated as an outer-bounding problem. In comparison to other methods, we check for feasibility of whole parameter/state regions. The practicability of the proposed approach is demonstrated with two simple biological example systems.
    Decision and Control, 2009 held jointly with the 2009 28th Chinese Control Conference. CDC/CCC 2009. Proceedings of the 48th IEEE Conference on; 01/2010
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    ABSTRACT: We consider the class of nonlinear optimal control problems (OCP) with polynomial data, i.e., the differential equation, state and control con- straints and cost are all described by polynomials, and more generally for OCPs with smooth data. In addition, state constraints as well as state and/or action constraints are allowed. We provide a simple hierarchy of LMI (lin- ear matrix inequality)-relaxations whose optimal values form a nondecreasing sequence of lower bounds on the optimal value. Under some convexity assump- tions, the sequence converges to the optimal value of the OCP. Preliminary results show that good approximations are obtained with few moments.
    04/2007;
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    ABSTRACT: Analysis and safety considerations of chemical and biological processes frequently require an outer approximation of the set of all feasible steady-states. Nonlinearities, uncertain parameters, and discrete variables complicate the calculation of guaranteed outer bounds. In this paper, the problem of outer-approximating the region of feasible steady-states, for processes described by uncertain nonlinear differential algebraic equations including discrete variables and discrete changes in the dynamics, is adressed. The calculation of the outer bounding sets is based on a relaxed version of the corresponding feasibility problem. It uses the Lagrange dual problem to obtain certificates for regions in state space not containing steady-states. These infeasibility certificates can be computed efficiently by solving a semidefinite program, rendering the calculation of the outer bounding set computationally feasible. The derived method guarantees globally valid outer bounds for the steady-states of nonlinear processes described by differential equations. It allows to consider discrete variables, as well as switching system dynamics. The method is exemplified by the analysis of a simple chemical reactor showing parametric uncertainties and large variability due to the appearance of bifurcations characterising the ignition and extinction of a reaction.
    Proceedings of International Symposium on Advanced Control of Chemical Processes ADCHEM09 (2009).

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