Abstract
Fault-Tolerant Control (FTC) strategy has gain maximum attention in recent years in chemical industries due to economical and safety hazards perspective. Targeting at the decreasing control performance of the single-tank level control process under the constraint of system and sensor faults, this article presents model-based Passive Fault-Tolerant Control (PFTC) strategy which are based on conventional and artificial intelligence control. The deviation between system outputs and model output are called residuals and are used to detect and identify faults. The mathematical model of single-tank level system is derived from real time process data using process reaction curve method. The paper discusses about the performance comparison between model-based PFTC using fuzzy logic and conventional proportional Plus Integral controller (PI). The proposed PFTC strategy is applied on single-tank level control process with system and sensor faults and verifies the performance of PFTC using fuzzy logic plus conventional PI control and other PFTC configuration. Proposed PFTC using fuzzy logic plus PI control gives better control performance even though fault occurs in the system. The control performance of different PFTC strategies are measured in terms of Mean Square Error (MSE), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) indices.
Highlights
Fault Tolerant Control (FTC) comprises diagnosis with control methods to handle faults in smart way
This paper focuses on comparative study of various Passive Fault-Tolerant Control (PFTC) strategy using Plus Integral controller (PI) and Fuzzy logic controller when single or multiple faults occur with different magnitude in terms of different error indices
It is obvious that when the conventional feedback PI controller is used and system and sensor faults occur, the performance of the system degrades extremely and the fault occurred has an enormous effect on the system yield
Summary
Fault Tolerant Control (FTC) comprises diagnosis with control methods to handle faults in smart way. The aim is to prevent that simple faults develop into severe failure and increase plant availability and reduce the risk of safety hazards (Jiang, 2010). ‘fault’ is defined as an unpredicted variation of the system functionality. We are concerned to detection, diagnosis of faults in an engineering system, whether they occur in the plant and control instrument (Sensor and actuators) or in the components of the process itself. Any Faults deteriorate the system performance as well as stability
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