### Abstract

This article addresses the problem of recursively estimating the parameter uncertainty intervals for a linear discrete system, whose output measurements are contaminated with noise. The estimator provides the maximally tight bounding rectangular hyperparallepiped, whose vertices are computed in an optimal manner so as to contain all parameter values consistent with the system structure and the l_{1} norm of the error bounds. The proposed method relies on a recursive formulation of the underlying posed linear programming problem in [1], by exploring its distinct structure. The computational effort associated with the finding of this optimal outer box is minimal. A recursive algorithm is presented for the cases of an increasing, and a fixed size sample time-sliding window. Simulation studies are included to highlight the algorithm's performance.

Original language | English (US) |
---|---|

Pages (from-to) | 3010-3015 |

Number of pages | 6 |

Journal | Proceedings of the IEEE Conference on Decision and Control |

Volume | 3 |

State | Published - Dec 1 1995 |

Event | Proceedings of the 1995 34th IEEE Conference on Decision and Control. Part 1 (of 4) - New Orleans, LA, USA Duration: Dec 13 1995 → Dec 15 1995 |

### Fingerprint

### ASJC Scopus subject areas

- Control and Systems Engineering
- Modeling and Simulation
- Control and Optimization

### Cite this

*Proceedings of the IEEE Conference on Decision and Control*,

*3*, 3010-3015.

**Development of a recursive algorithm for parameter uncertainty interval estimation.** / Tzes, Antonios; Hu, Qingyang; Le, Ke.

Research output: Contribution to journal › Conference article

*Proceedings of the IEEE Conference on Decision and Control*, vol. 3, pp. 3010-3015.

}

TY - JOUR

T1 - Development of a recursive algorithm for parameter uncertainty interval estimation

AU - Tzes, Antonios

AU - Hu, Qingyang

AU - Le, Ke

PY - 1995/12/1

Y1 - 1995/12/1

N2 - This article addresses the problem of recursively estimating the parameter uncertainty intervals for a linear discrete system, whose output measurements are contaminated with noise. The estimator provides the maximally tight bounding rectangular hyperparallepiped, whose vertices are computed in an optimal manner so as to contain all parameter values consistent with the system structure and the l1 norm of the error bounds. The proposed method relies on a recursive formulation of the underlying posed linear programming problem in [1], by exploring its distinct structure. The computational effort associated with the finding of this optimal outer box is minimal. A recursive algorithm is presented for the cases of an increasing, and a fixed size sample time-sliding window. Simulation studies are included to highlight the algorithm's performance.

AB - This article addresses the problem of recursively estimating the parameter uncertainty intervals for a linear discrete system, whose output measurements are contaminated with noise. The estimator provides the maximally tight bounding rectangular hyperparallepiped, whose vertices are computed in an optimal manner so as to contain all parameter values consistent with the system structure and the l1 norm of the error bounds. The proposed method relies on a recursive formulation of the underlying posed linear programming problem in [1], by exploring its distinct structure. The computational effort associated with the finding of this optimal outer box is minimal. A recursive algorithm is presented for the cases of an increasing, and a fixed size sample time-sliding window. Simulation studies are included to highlight the algorithm's performance.

UR - http://www.scopus.com/inward/record.url?scp=0029482476&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0029482476&partnerID=8YFLogxK

M3 - Conference article

AN - SCOPUS:0029482476

VL - 3

SP - 3010

EP - 3015

JO - Proceedings of the IEEE Conference on Decision and Control

JF - Proceedings of the IEEE Conference on Decision and Control

SN - 0191-2216

ER -