From data to diagnosis and control using generalized orthonormal basis filters. Part II: Model predictive and fault tolerant control
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 model predictive control (MPC) and 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 MPC scheme proposed by K.R. Muske and J.B. Rawlings [Model predictive control with linear models, AIChE J. 39 (1993) 262-287] and the fault tolerant control scheme (FTCS) proposed by J. Prakash, S.C. Patwardhan, and S. Narasimhan [A supervisory approach to fault tolerant control of linear multivariable systems, Ind. Eng. Chem. Res. 41 (2002) 2270-2281] starting from the innovations form of state space model identified using generalized orthonormal basis function (GOBF) parameterization. The efficacy of the proposed MPC scheme and the on-line FTCS is demonstrated by conducting simulation studies on the benchmark shell control problem (SCP) and experimental studies on a laboratory scale continuous stirred tank heater (CSTH) system. The analysis of the simulation and experimental results reveals that the MPC scheme formulated using the identified observers produces superior regulatory performance when compared to the regulatory performance of conventional MPC controller even in the presence of significant plant model mismatch. 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 simulation and experimental study clearly demonstrate the advantages of formulating MPC and generalized likelihood ratio (GLR) based fault diagnosis schemes using the innovations form of state space model identified from input output data. © 2005 Elsevier Ltd. All rights reserved.
Fault tolerant control with identified models: An experimental study
Experimental evaluation of the active fault tolerant control scheme developed with state space models (innovations form) identified purely from input-output data is carried out in the present work. A key problem in fault diagnosis with identified models is the isolation of abrupt changes in unmeasured disturbances. A novel approach was developed to model and isolate such changes (classified as unknown input fault) with identified models. Simulation results for a heavy oil fractionator process (Shell Control Workshop challenge problem) demonstrated, the efficacy of the developed FDI method and the Fault tolerant control strategy. The present work experimentally evaluates the developed scheme on a laboratory scale two tank temperature control system for a variety of faults.
Fault diagnosis and fault tolerant control using reduced order models
In this work, we have developed a reduced order model relevant for fault diagnosis and control. This model is combined with a generalized likelihood ratio (GLR) method and integrated with fault tolerant control schemes developed earlier. Simulation studies of an ideal binary distillation column show that the use of reduced order model improves the diagnostic performance thereby leading to improved fault tolerance. Furthermore, the proposed strategy is also computational more efficient. In inferential control, such as the use of temperature measurements to infer product compositions, the proposed fault tolerant control scheme performs better than the conventional control scheme when various soft faults occur. © 2008 Canadian Society for Chemical Engineering.
Fault diagnosis and fault tolerant control using reduced order models
The present work considers a model reduction approach for fault diagnosis with generalised likelihood ratio (GLR) method to improve upon its diagnostic performance and computational efficiency in large dimensional applications. Model reduction techniques are widely used in controller design. A similar concept with balanced truncation technique is employed to obtain a reduced order diagnostic model of the process for GLR implementation. The proposed method is incorporated in the fault tolerant control strategy developed by Prakash et al. (2002). Simulation results for a binary distillation column demonstrate the efficacy of the proposed fault detection and identification (FDI) method and the fault tolerant control strategy in comparison to the full-scale implementation for a variety of faults.
Bayesian inference for fault-tolerant control
In this contribution, we present initial developments in view of model-based fault-tolerant control (FTC). In this context, we use an original method based on the Kalman-filter by which fault detection, diagnosis and accommodation is possible provided that an accurate model is available. Since this is not generally true, we attempt to alleviate this necessity by means of accounting for uncertainty, in both model as well as in the measurements used for fault diagnosis. Our preliminary results are focused on the diagnosis step in the FTC scheme. © 2009 IEEE.
Unknown input modeling and robust fault diagnosis using black box observers
A key issue that needs to be addressed while performing fault diagnosis using black box models is that of robustness against abrupt changes in unknown inputs. A fundamental difficulty with the robust FDI design approaches available in the literature is that they require some a priori knowledge of the model for unmeasured disturbances or modeling uncertainty. In this work, we propose a novel approach for modeling abrupt changes in unmeasured disturbances when innovation form of state space model (i.e. black box observer) is used for fault diagnosis. A disturbance coupling matrix is developed using singular value decomposition of the extended observability matrix and further used to formulate a robust fault diagnosis scheme based on generalized likelihood ratio test. The proposed modeling approach does not require any a priori knowledge of how these faults affect the system dynamics. To isolate sensor and actuator biases from step jumps in unmeasured disturbances, a statistically rigorous method is developed for distinguishing between faults modeled using different number of parameters. Simulation studies on a heavy oil fractionator example show that the proposed FDI methodology based on identified models can be used to effectively distinguish between sensor biases, actuator biases and other soft faults caused by changes in unmeasured disturbance variables. The fault tolerant control scheme, which makes use of the proposed robust FDI methodology, gives significantly better control performance than conventional controllers when soft faults occur. The experimental evaluation of the proposed FDI methodology on a laboratory scale stirred tank temperature control set-up corroborates these conclusions. © 2008 Elsevier Ltd. All rights reserved.