Name
Abstract | ||
|---|---|---|
Methods for direct data-driven tuning of the parameters of precompensators for LPV systems are developed. Since the commutativity property is not always satisfied for LPV systems, previously proposed methods for LTI systems that use this property cannot be directly adapted. When the ideal precompensator giving perfect mean tracking exists in the proposed parameterisation of the precompensator, the LPV transfer operators do commute and an algorithm using only two experiments on the real system is proposed. It is shown that this algorithm gives consistent estimates of the ideal parameters despite the presence of stochastic disturbances. For the more general case, when the ideal precompensator does not belong to the set of parameterised precompensators, another technique is developed. This technique requires a number of experiments equal to twice the number of precompensator parameters and it is shown that the calculated parameters minimise the mean squared tracking error. I. I NTRODUCTION |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/CDC.2008.4739190 | Cancun |
Keywords | Field | DocType |
compensation,continuous time systems,linear systems,mean square error methods,parameter estimation,stochastic processes,tracking,commutativity property,direct data-driven precompensator tuning,linear parameter varying system transfer operator,linear time-invariant system,mean squared tracking error minimization,parameter estimation,stochastic disturbance | LTI system theory,Mathematical optimization,Square (algebra),Commutative property,Polynomial,Linear system,Control theory,Stochastic process,Estimation theory,Mathematics,Tracking error | Conference |
ISSN | ISBN | Citations |
0191-2216 E-ISBN : 978-1-4244-3124-3 | 978-1-4244-3124-3 | 2 |
PageRank | References | Authors |
0.40 | 6 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mark Edward John Butcher | 1 | 2 | 0.40 |
| A. Karimi | 2 | 289 | 40.41 |
| Roland Longchamp | 3 | 2 | 0.40 |