Abstract
The input-parameter-state estimation capabilities of a novel unscented Kalman filter is examined herein on both linear and nonlinear systems. The unknown input is estimated in two stages within each time step. Firstly, the predicted dynamic states and the system parameters provide an estimation of the input. Secondly, the corrected with measurements states and parameters provide a final estimation. Importantly, it is demonstrated using the perturbation analysis that, a system with at least a zero or a non-zero known input can potentially be uniquely identified. This output-only methodology allows for a better understanding of the system compared to classical output-only parameter identification strategies, given that all the dynamic states, the parameters, and the input are estimated jointly and in real-time.
| Original language | English |
|---|---|
| Article number | 108026 |
| Journal | Mechanical Systems and Signal Processing |
| Volume | 162 |
| Early online date | 10 May 2021 |
| DOIs | |
| Publication status | Published - 1 Jan 2022 |
Acknowledgements
The authors would like to acknowledge the support of the U.S. National Science Foundation , which partially supported this research under Grant No. CMMI-1563364.Funding
The authors would like to acknowledge the support of the U.S. National Science Foundation , which partially supported this research under Grant No. CMMI-1563364.
| Funders | Funder number |
|---|---|
| National Science Foundation | Grant No. CMMI-1563364 |
Keywords
- Unscented Kalman Filter (UKF)
- Input-Parameter-State Estimation (IPS-UKF)
- Real-time system identification
- Bayesian filtering
- Recursive estimation
- Sigma points
- Nonlinear filtering
- State-space modeling
- Unknown input estimation
- Output-only identification
- Structural health monitoring
- Dynamic system monitoring
- Damage detection
- Data fusion
- Sensor networks
- Ambient vibration analysis
- Linear multi-degree-of-freedom (MDOF) systems
- Nonlinear MDOF systems
- Structural dynamics
- Mechanical systems
- Civil engineering structures
- Vibration analysis
- Force identification
- Perturbation analysis
- Observability rank condition
- Identifiability
- Covariance calibration
- Gaussian noise modeling
- Process and measurement noise
- Sensitivity analysis
- Recursive least squares
- Frequency domain decomposition
- Displacement measurement
- Velocity measurement
- Acceleration measurement
- Output-only sensors
- Noise-to-signal ratio
- Ambient input
- Pulse excitation
- Convergence analysis
- Noise filtering
- Drift correction
- High-frequency noise amplification
- Low-frequency noise integration error
- Real-time monitoring constraints
- Extended Kalman Filter (EKF)
- Minimum variance unbiased estimation
- Auto-regressive models
- Dual Kalman filter
- Multi-rate Kalman filtering
ASJC Scopus subject areas
- Chemical Engineering(all)
- Computer Science(all)
- Decision Sciences(all)
- Economics, Econometrics and Finance(all)
- Energy(all)
- Engineering(all)
- Materials Science(all)
- Environmental Science(all)
- Mathematics(all)
- Physics and Astronomy(all)
