Volume 06,Issue 04

Model Reference Adaptive Control of Linear System Despite Sensor Bias

Authors

Ebadollah Amouzad Mahdiraji

Abstract
Based on the simulation results, steady-state tracking errors are improved. Control of indeterminate systems, despite the actuator and sensor bias, has been and remains a major challenge. Sensor error can cause process error. Among the cases where sensor bias is common, air velocity measurements and gyroscope rates can be mentioned. Although considerable research efforts have previously focused on adapting the error, the bias correction of the sensor appears to be relatively limited. However, the cause of several crashes was the sensor error (due to radio altimeter error, angle of attack sensor error, airspeed speed sensor error). Also, finding a way to fix the sensor bias problem is of the utmost importance. The direct model reference adaptive control (MRAC) method is used to control uncertain systems using controllers that are adapted to achieve a performance close to a reference model. However, these controllers maintain system stability and provide close tracking of the reference model response. In this paper, we intend to address the problem of unknown sensor bias matching by adjusting the direct reference model adaptive control for state-feedback for state-tracking (SFST). Also, to obtain an asymptotic stable bias sensor estimator, we use the Kalman filter to estimate the bias sensor error. Based on the simulation results, steady-state tracking errors are improved.

Keyword: Model Reference Adaptive Control, Sensor Bias, Kalman Filter, Fault Estimation.

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