Integrating Model Based Fault Diagnosis with Model Predictive Control

Abstract

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.

Fault Tolerant Flight Control - A Survey

“…However, if state-feedback MPC is used in an interconnection with an observer one should also take care to also reconfigure the observer appropriately in order to achieve fault-tolerant state estimation. Examples of the application of MPC to FTC are numerous [66,51,76,50,56].…”

Integrating Fuzzy based Fault diagnosis with Constrained Model Predictive Control for Industrial Applications

“…It is also mandatory to have higher switching response between the schemes i.e. when a control configuration is getting failed, the process should immediately switch over to the functioning scheme to have continuity of the process and fault tolerance capability [1].…”

“…al. [1] for a CSTR system. The main drawback of this method is that estimation of the parameter θ would become difficult when fault ratio increases.…”

Intelligent state estimation for fault tolerant nonlinear predictive control

“…Though this approach achieves fault tolerance while maintaining the integrity in the estimate of the lost output, the fault detection and isolation aspect does not feature in the formulation. Recently, Prakash et al [8] have proposed an active fault tolerant linear MPC (FTMPC) scheme, which can systematically deal with soft faults in a unified framework. The FTMPC scheme is developed by integrating generalized likelihood ratio (GLR) method, a model based fault detection and identification (FDI) scheme, with the state space formulation of MPC based on Kalman filter.…”