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Sensor and actuator bias estimation using multi model approach
Published in Elsevier BV
Volume: 57
Pages: 118 - 133

The objective of this paper is first to design an Adaptive Linear Kalman Filter (ALKF) to estimate nonlinear process states and to compare the performance of the ALKF with the Extended Kalman Filter (EKF). The designed ALKF is next used to detect sensor and actuator biases which may occur either sequentially or simultaneously using a Multi Model ALKF (MMALKF). Finally the Multi Model Adaptive Linear H∞ Filter (MMALH∞F) is designed to increase the robustness of bias Detection in the presence of unknown noise statistics and unmodeled dynamics. The proposed estimator is demonstrated on the variable area tank process and Continuously Stirred Tank Reactor (CSTR) process to show its efficacy.

About the journal
JournalData powered by TypesetComputers & Electrical Engineering
PublisherData powered by TypesetElsevier BV
Open Access0