System identification: theory for the user
System identification: theory for the user
Algorithms for clustering data
Algorithms for clustering data
A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimal estimation theory for dynamic systems with set membership uncertainty: an overview
Automatica (Journal of IFAC)
Matrix Sum-of-Squares Relaxations for Robust Semi-Definite Programs
Mathematical Programming: Series A and B
Subspace identification of MIMO LPV systems using a periodic scheduling sequence
Automatica (Journal of IFAC)
Fuzzy clustering with volume prototypes and adaptive cluster merging
IEEE Transactions on Fuzzy Systems
Subspace identification of multivariable linear parameter-varying systems
Automatica (Journal of IFAC)
Survey Research on gain scheduling
Automatica (Journal of IFAC)
Identification and predictive control for a circulation fluidized bed boiler
Knowledge-Based Systems
A convex relaxation approach to set-membership identification of LPV systems
Automatica (Journal of IFAC)
| Hi-index | 22.15 |
A global model structure is developed for parametrization and identification of a general class of Linear Parameter-Varying (LPV) systems. By using a fixed orthonormal basis function (OBF) structure, a linearly parametrized model structure follows for which the coefficients are dependent on a scheduling signal. An optimal set of OBFs for this model structure is selected on the basis of local linear dynamic properties of the LPV system (system poles) that occur for different constant scheduling signals. The selected OBF set guarantees in an asymptotic sense the least worst-case modeling error for any local model of the LPV system. Through the fusion of the Kolmogorov n-width theory and Fuzzy c-Means clustering, an approach is developed to solve the OBF-selection problem for discrete-time LPV systems, based on the clustering of observed sample system poles.