Fault Tolerant Control Using Proportional-Integral-Derivative Controller Tuned by Genetic Algorithm

Abstract

Problem statement: The growing demand for reliability, maintainability and survivability in industrial processes has drawn significant research in fault detection and fault tolerant control domain. A fault is usually defined as an unexpected change in a system, such as component malfunction and variations in operating condition, which tends to degrade the overall system performance. The purpose of fault detection is to detect these malfunctions to take proper action in order to prevent faults from developing into a total system failure. Approach: In this study an effective integrated fault detection and fault tolerant control scheme was developed for a class of LTI system. The scheme was based on a Kalman filter for simultaneous state and fault parameter estimation, statistical decisions for fault detection and activation of controller reconfiguration. Proportional-Integral-Derivative (PID) control schemes continue to provide the simplest and yet effective solutions to most of the control engineering applications today. Determination or tuning of the PID parameters continues to be important as these parameters have a great influence on the stability and performance of the control system. In this study GA was proposed to tune the PID controller. Results: The results reflect that proposed scheme improves the performance of the process in terms of time domain specifications, robustness to parametric changes and optimum stability. Also, A comparison with the conventional Ziegler-Nichols method proves the superiority of GA based system. Conclusion: This study demonstrates the effectiveness of genetic algorithm in tuning of a PID controller with optimum parameters. It is, moreover, proved to be robust to the variations in plant dynamic characteristics and disturbances assuring a parameter-insensitive operation of the process.