0000000000347387

AUTHOR

Giovanni Zappa

showing 4 related works from this author

LPV Predictive Control of the Stall and Surge for Jet Engine 1

2001

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.

Engineeringbusiness.industryControl engineeringConstraint satisfactionJet enginelaw.inventionModel predictive controlGain schedulinglawControl theoryRobust controlbusinessGas compressorSurge controlStall (engine)IFAC Proceedings Volumes
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Approximation of the Feasible Parameter Set in worst-case identification of Hammerstein models

2005

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.

Mathematical optimizationEstimation theorySystem identificationIdentification (control systems)PolytopeLinear subspaceInterval arithmeticSettore ING-INF/04 - AutomaticaControl and Systems EngineeringBounding overwatchConvex optimizationNonlinear systemsApplied mathematicsElectrical and Electronic EngineeringProjection (set theory)static nonlinearityMathematics
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Approximation of Feasible Parameter Set in worst case identification of block-oriented nonlinear models

2003

Abstract The estimation of the Feasible Parameter Set for block-oriented nonlinear models in a worst case setting is considered. A bounding procedure is determined both for polytopic and ellipsoidie sets, consisting in the projection of the FPS ⊂ R MN of the extended parameter vector onto suitable M or N-dimensional subspaces and in the solution of convex optimization problems which provide the extreme points of the Parameter Uncertainties Intervals of the model parameteres. Bounds obtained are tighter then in the previous approaches.

Set (abstract data type)Nonlinear systemMathematical optimizationBounding overwatchConvex optimizationApplied mathematicsExtreme pointLinear subspaceProjection (linear algebra)MathematicsBlock (data storage)
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NARX Models of an Industrial Power Plant Gas Turbine

2005

This brief reports the experience with the identification of a nonlinear autoregressive with exogenous inputs (NARX) model for the PGT10B1 power plant gas turbine manufactured by General Electric-Nuovo Pignone. Two operating conditions of the turbine are considered: isolated mode and nonisolated mode. The NARX model parameters are estimated iteratively with a Gram-Schmidt procedure, exploiting both forward and stepwise regression. Many indexes have been evaluated and compared in order to perform subset selection in the functional basis set and determine the structure of the nonlinear model. Various input signals (from narrow to broadband) for identification and validation have been consider…

EngineeringNonlinear autoregressive exogenous modelbusiness.industryTurbinesSystem identificationControl engineeringNonlinear controlTurbineDistributed power generationElectric power systemNonlinear systemAutoregressive modelControl and Systems EngineeringSteam turbineControl theoryElectrical and Electronic EngineeringbusinessGas turbines
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