Shankar Narasimhan

Department Of Chemical Engineering

Actuators

Batch data processing

Degrees of freedom (mechanics)

Fault detection

Nonlinear systems

Predictive control systems

Closed-loop performance

Continuous process

In-process parameters

Modeling behavior

Nonlinear model predictive control

Process industries

Sensors and actuators

Sensors or actuators

Model predictive control

IIT Madras is a public technical and research university located in Chennai, Tamil Nadu. Founded in 1959, it is recognised as an Institute of National Importance.

IIT Madras has been ranked as the top engineering institute in India for four years in a row by the National Institutional Ranking Framework of the MHRD

It currently offers undergraduate, postgraduate and research degrees across 16 disciplines in Engineering, Sciences, Humanities and Management. About 596 faculty belonging to science and engineering departments and centres of the Institute are engaged in teaching, research and industrial consultancy.

IIT Madras

Integrating Fault Diagnosis with Nonlinear Model Predictive Control

Lecture Notes in Control and Information Sciences

There is growing realization that on-line model maintenance is the key to realizing long term benefits of a predictive control scheme. In this work, a novel intelligent nonlinear state estimation strategy is proposed, which keeps diagnosing the root cause(s) of the plant model mismatch by isolating the subset of active faults (abrupt changes in parameters/disturbances, biases in sensors/actuators, actuator/sensor failures) and auto-corrects the model on-line so as to accommodate the isolated faults/failures. To carry out the task of fault diagnosis in multivariate nonlinear time varying systems, we propose a nonlinear version of the generalized likelihood ratio (GLR) based fault diagnosis and identification (FDI) scheme (NL-GLR). An active fault tolerant NMPC (FTNMPC) scheme is developed that makes use of the fault/failure location and magnitude estimates generated by NL-GLR to correct the state estimator and prediction model used in NMPC formulation. This facilitates application of the fault tolerant scheme to nonlinear and time varying processes including batch and semi-batch processes. The advantages of the proposed intelligent state estimation and FTNMPC schemes are demonstrated by conducting simulation studies on a benchmark CSTR system, which exhibits input multiplicity and change in the sign of steady state gain, and a fed batch bioreactor, which exhibits strongly nonlinear dynamics. By simulating a regulatory control problem associated with an unstable nonlinear system given by Chen and Allgower [H. Chen, F. Allgower, A quasi infinite horizon nonlinear model predictive control scheme with guaranteed stability, Automatica 34(10) (1998) 1205-1217], we also demonstrate that the proposed intelligent state estimation strategy can be used to maintain asymptotic closed loop stability in the face of abrupt changes in model parameters. Analysis of the simulation results reveals that the proposed approach provides a comprehensive method for treating both faults (biases/drifts in sensors/actuators/model parameters) and failures (sensor/ actuator failures) under the unified framework of fault tolerant nonlinear predictive control. © 2008 Elsevier Ltd. All rights reserved.

Fulltext

Journal of Process Control

Intelligent state estimation for fault tolerant nonlinear predictive control

Model predictive control (MPC) schemes are typically developed under the assumption that the sensors and actuators are free from faults. Attempts to develop fault-tolerant MPC schemes have mainly focused on dealing with hard faults, such as sensor or actuator failures, process leaks, etc. However, soft faults such as biases or drifts in sensors or actuators are more frequently encountered in the process industry. Occurrences of such faults can lead to degradation in the closed loop performance of the MPC controller. Since MPC controllers are typically used to control key operations in a chemical plant, this can have an impact on safety and productivity of the entire plant. The conventional approach to dealing with such soft faults in MPC formulations is through the introduction of additional artificial states to the model. The main limitation of this approach is that number of artificial extra states introduced cannot exceed the number of measurements. This implies that it is necessary to have a priori knowledge of which subset of faults are most likely to occur. In this paper, an active on-line fault-tolerant model predictive control (FTMPC) scheme is proposed by integrating state space formulation of MPC with the fault detection and identification (FDI) method based on generalized likelihood ratios. The fact that both these schemes use a Kalman filter as their basis facilitates tight integration of these two components. The main difference between the conventional MPC formulation and FTMPC formulation is that the bias corrections to the model are made as and when necessary and at qualified locations identified by the FDI component. The FTMPC eliminates offset between the true values and set points of controlled variables in the presence of a variety of faults while conventional MPC does not. Also, the true values of state variables, manipulated inputs, and measured variables are maintained within their imposed bounds in FTMPC, while in conventional MPC, these may be violated when soft faults occur. These advantages of the proposed scheme are demonstrated using simulation studies on a CSTR process and experimental studies conducted on the temperature control of a coupled two tank heater system. © 2005 American Chemical Society.

Industrial and Engineering Chemistry Research

Integrating model based fault diagnosis with model predictive control

Coventional control schemes are developed under the assumption that the sensors and actuators are free from faults. However, the occurrence of faults will cause degradation in the closed-loop performance and also will have an impact on safety, productivity, and plant economy. In the present work, we have proposed a fault-tolerant control scheme (FTCS) by integrating a fault detection and identification (FDI) technique with conventional control. The principal component of our proposed FTCS is a compensation strategy (supervisory system) which uses the information provided by the FDI to appropriately modify the controller as well as the model used in FDI. This allows online application of the FTCS without causing significant degradation in the closed-loop performance due to the occurrences of biases in sensors and actuators or due to changes in unmeasured disturbance variables and due to moderate change in process parameters. Through stochastic simulation studies of a continuous stirred tank reactor process, we demonstrate that our proposed FTCS leads to significant improvement in the closed-loop performance in comparison to a conventional control scheme, especially as the fault magnitude increases. The proposed compensation scheme also allows identification of multiple faults that occur sequentially in time and is also found to be robust with respect to moderate plant-model mismatch.

A supervisory approach to fault-tolerant control of linear multivariable systems

Gross error detection (GED) is an important function in automated processing of plant data. All GED tests developed so far are based on a linear theory and can be applied to nonlinear processes only after suitable linearization of the process constraints. In this paper, we propose a test for GED in nonlinear processes which does not require the constraints to be linearized. Although the proposed test does not have a rigorous statistical basis, it is entirely analogous to the generalized maximum likelihood ratio test. This test is combined with different existing strategies for multiple GED to determine the best possible method. Simulation results show that for a significantly nonlinear system the proposed test performs better than tests which rely on linearizing the constraints. However, for mildly nonlinear systems such as those with only bilinear constraints, the performances are comparable. The simple serial compensation strategy is shown to be better than its modified version as well as the serial-elimination strategy, especially when the aim is to maximize accuracy of the final estimates.Gross error detection (GED) is an important function in automated processing of plant data. All GED tests developed so far are based on a linear theory and can be applied to nonlinear processes only after suitable linearization of the process constraints. In this paper, we propose a test for GED in nonlinear processes which does not require the constraints to be linearized. Although the proposed test does not have a rigorous statistical basis, it is entirely analogous to the generalized maximum likelihood ratio test. This test is combined with different existing strategies for multiple GED to determine the best possible method. Simulation results show that for a significantly nonlinear system the proposed test performs better than tests which rely on linearizing the constraints. However, for mildly nonlinear systems such as those with only bilinear constraints, the performances are comparable. The simple serial compensation strategy is shown to be better than its modified version as well as the serial elimination strategy, especially when the aim is to maximize accuracy of the final estimates.

A strategy for detection of gross errors in nonlinear-processes

Chemical Engineering Science

State estimation based model predictive control applied to shell control problem: a case study

Integrating fault diagnosis with nonlinear model predictive control

Journal	Lecture Notes in Control and Information Sciences
ISSN	01708643
Open Access	No