Sensor and actuator bias estimation using multi model approach

doi:10.1016/j.compeleceng.2016.11.003

Sensor and actuator bias estimation using multi model approach

2017-01-01

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DOI：10.1016/j.compeleceng.2016.11.003

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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-infinity Filter (MMALH(infinity)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. (C) 2016 Elsevier Ltd. All rights reserved.

Keywords:

Kalman filter

H- infinity filter

Process monitoring

Actuator bias

Sensor bias

Conical tank process

CSTR process

Sensor and actuator bias estimation using multi model approach

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