Actuator Failures and Sensor Bias Compensation by Combination of Model Reference Adaptive Control & Kalman Filter Theory

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  • Hossein Rahmani Amoli*1,
  • Karim Salahshor2,
  1. Department of Electrical Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran
  2. Department of Automation and Instrumentation Engineering, Petroleum University of Technology, Ahvaz, Iran

Revised: 2019-04-22

Accepted: 2019-05-14

Published in Issue 2019-06-01

How to Cite

Rahmani Amoli, H., & Salahshor, K. (2019). Actuator Failures and Sensor Bias Compensation by Combination of Model Reference Adaptive Control & Kalman Filter Theory. Signal Processing and Renewable Energy (SPRE), 3(2), 37-47. https://oiccpress.com/spre/article/view/7795

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Abstract

The actuator failure and sensor bias pose very important challenges in the aerospace industry. Why adaptive control is a good way to deal with these problems? The paper addresses the problem of actuator failures and sensor bias compensation using the combination ofa model reference adaptive control (MRAC) approach and Kalman filter (KF) for state tracking objective. To comply this condition, an enhanced MRAC method is introduced for state tracking based on state feedback configuration and a number of adaptation laws have been formulated to maintain the desired system performance. The Kalman filter is used to estimate the states despite sensor bias, providing excellent and reliable estimates. Simulation results demonstrate the effectiveness of the presented method to achieve good state tracking performances in spite of the presence of actuator failures and sensor bias.
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