Adaptive divided difference filtering for simultaneous state and parameter estimation. (English) Zbl 1184.93110
Summary: A novel adaptive version of the Divided Difference Filter (DDF) applicable to non-linear systems with a linear output equation is presented in this work. In order to make the filter robust to modeling errors, upper bounds on the state covariance matrix are derived. The parameters of this upper bound are then estimated using a combination of offline tuning and online optimization with a linear matrix inequality constraint, which ensures that the predicted output error covariance is larger than the observed output error covariance. The resulting sub-optimal, high-gain filter is applied to the problem of joint state and parameter estimation. Simulation results demonstrate the superior performance of the proposed filter as compared to the standard DDF.
MSC:
| 93E10 | Estimation and detection in stochastic control theory |
| 93E11 | Filtering in stochastic control theory |
| 93E15 | Stochastic stability in control theory |
| 93C55 | Discrete-time control/observation systems |
Keywords:
state estimation; nonlinear filters; stochastic systems; robust estimation; parameter estimation; fault detectionReferences:
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