0000000000336710
AUTHOR
P. Falugi
Set Membership (In) Validation of nonlinear positive models for biological systems
The complexity of biology needs quantitative tools in order to support and validate biologists intuition and traditional qualitative descriptions. In this paper, Nonlinear Positive models with constraints for biological systems are validated/invalidated in a worst-case deterministic setting. These models are usefull for the analysis of the DNA and RNA evolution and for the description of the population dynamics of viruses and bacteria. The conditional central estimate and the Uncertainty Intervals are determined in order to validate/invalidate the model. The effectiveness of the proposed procedure has been illustrated by means of simulation experiments.
LPV Predictive Control of the Stall and Surge for Jet Engine 1
Abstract Predictive control of constrained LPV systems is applied to the model of the stall and surge control for jet engine compressors. The objective of the used technique is to optimize nominal performance while guaranteeing robust stability and constraint satisfaction. This is achieved by exploiting invariant sets and a receding horizon optimization procedure which provides on-line a non-linear correction to a gain-scheduled linear feedback designed off-line. A comparison with a contractive gain-scheduling control technique is also shown.
Identification and validation of quasispecies models for biological systems
An identification procedure for biological systems cast as quasi-species models is proposed. Their identification is a challenging problem because of the bilinear dependence on the parameters and their physical constraints. The proposed solution is within the framework of set-membership identification. %The bilinear dependence on parameters of the model and their physical constraints make the present issue challenging. We determine an estimate of the model parameters together with their interval of variability (Uncertainty Intervals), taking into account all the physical constraints. Invalidation/validation is performed on the basis of the predictive capability of the estimated models. The …
Approximation of the Feasible Parameter Set in worst-case identification of Hammerstein models
The estimation of the Feasible Parameter Set (FPS) for Hammerstein models in a worst-case setting is considered. A bounding procedure is determined both for polytopic and ellipsoidic uncertainties. It consists in the projection of the FPS of the extended parameter vector onto suitable subspaces and in the solution of convex optimization problems which provide Uncertainties Intervals of the model parameters. The bounds obtained are tighter than in the previous approaches. hes.
PARAMETER BOUNDED ESTIMATION FOR QUASISPECIES MODELS OF MOLECULAR EVOLUTION
Abstract The Quasispecies models identification for Evolutionary Dynamics is considered in a worst-case deterministic setting. These models analyze the DNA and RNA evolution or describe the population dynamics of viruses and bacteria. In this paper we identify the Fitness and the Replication Probability parameters of a genetic sequences, subject to a set of stringent constraints to have physical meaning and to guarantee positiveness. The conditional central estimate and the Uncertainty Intervals are determined. The effectiveness of the proposed procedure has been illustrated by means of simulation experiments while tests on real data are under concern.
Identification of Replicator Mutator models
The complexity of biology literally calls for quantitative tools in order to support and validate biologists intuition and traditional qualitative descriptions. In this paper, the Replicator-Mutator models for Evolutionary Dynamics are validated/invalidated in a worst-case deterministic setting. These models analyze the DNA and RNA evolution or describe the population dynamics of viruses and bacteria. We identify the Fitness and the Replication Probability parameters of a genetic sequences, subject to a set of stringent constraints to have physical meaning and to guarantee positiveness. The conditional central estimate is determined in order to validate/invalidate the model. The effectivene…
LPV model identification for gain scheduling control: An application to rotating stall and surge control problem
Abstract We approach the problem of identifying a nonlinear plant by parameterizing its dynamics as a linear parameter varying (LPV) model. The system under consideration is the Moore–Greitzer model which captures surge and stall phenomena in compressors. The control task is formulated as a problem of output regulation at various set points (stable and unstable) of the system under inputs and states constraints. We assume that inputs, outputs and scheduling parameters are measurable. It is worth pointing out that the adopted technique allows for identification of an LPV model's coefficients without the requirements of slow variations amongst set points. An example of combined identification…
Application of model quality evaluation to systems biology
Application of model quality evaluation to the quasispecies models is presented. These models are useful for the analysis of the DNA and RNA evolution and for the description of the population dynamics of viruses and bacteria. An estimate of the parameters together with their interval of variability is computed and the quality evaluation is tested on the basis of the model prediction error capability.