Active fault tolerant control using innovation form of state space models

Given a state space model together with the state noise and measurement noise characteristics, there are well established procedures to design a Kalman filter based fault diagnosis scheme. In practice, however, such disturbance models relating the true root cause of the unmeasured disturbances with the states / outputs are difficult to develop. To alleviate this difficulty, we reformulate the fault tolerant control scheme (FTCS) proposed by Prakash et al. [1] starting from the innovations form of state space model identified using generalized orthonormal basis function (GOBF) parameterization. The efficacy of the proposed on-line FTCS is demonstrated by conducting experimental studies on a laboratory scale continuous stirred tank heater (CSTH) system. The analysis of the experimental results reveals that the FTCS reformulated using the innovations form of state space model is able to isolate sensor as well as actuator faults occurring sequentially in time. In particular, the proposed FTCS is able to eliminate offset between the true value of the measured variable and the setpoint in the presence of sensor biases. Thus, the experimental study clearly demonstrate the advantages of formulating generalized likelihood ratio (GLR) based fault diagnosis schemes using the innovations form of state space model identified from input output data.