Abstract
This paper presents two estimation methods for systems with unknown time-varying input dynamics. By defining auxiliary filtered variables, an invariant manifold is derived and used to drive the input estimator with only one tuning parameter. Exponential error convergence to a small compact set around the
origin can be proved. Robustness against noise is studied and compared with two well-known schemes. Moreover, when the input dynamics to be estimated are parameterized in a quasilinear form with unknown parameters, the proposed idea is further investigated to estimate the associated unknown
time-varying parameters. The algorithms are tested by considering the torque estimation of internal combustion engines (ICEs). Comparative simulation results based on a benchmark engine simulation model show satisfactory transient and
robustness performance.
origin can be proved. Robustness against noise is studied and compared with two well-known schemes. Moreover, when the input dynamics to be estimated are parameterized in a quasilinear form with unknown parameters, the proposed idea is further investigated to estimate the associated unknown
time-varying parameters. The algorithms are tested by considering the torque estimation of internal combustion engines (ICEs). Comparative simulation results based on a benchmark engine simulation model show satisfactory transient and
robustness performance.
| Original language | English |
|---|---|
| Title of host publication | 2015 54th IEEE Conference on Decision and Control (CDC) |
| Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| Pages | 3687-3692 |
| Number of pages | 6 |
| ISBN (Print) | 9781479978847 |
| DOIs | |
| Publication status | Published - 15 Dec 2015 |