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Fault tolerant control of multivariable processes using auto-tuning PID controller.

Ding-Li Yu·T K Chang·Ding-Wen Yu
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Abstract

Fault tolerant control of dynamic processes is investigated in this paper using an auto-tuning PID controller. A fault tolerant control scheme is proposed composing an auto-tuning PID controller based on an adaptive neural network model. The model is trained online using the extended Kalman filter (EKF) algorithm to learn system post-fault dynamics. Based on this model, the PID controller adjusts its parameters to compensate the effects of the faults, so that the control performance is recovered from degradation. The auto-tuning algorithm for the PID controller is derived with the Lyapunov method and therefore, the model predicted tracking error is guaranteed to converge asymptotically. The method is applied to a simulated two-input two-output continuous stirred tank reactor (CSTR) with various faults, which demonstrate the applicability of the developed scheme to industrial processes.

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AlgorithmsArtificial IntelligenceComputer SimulationEquipment FailureFeedbackModels, TheoreticalMultivariate AnalysisSystems TheoryViscosityEvaluation StudyJournal Article

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Ding-Li Yu, T K Chang, Ding-Wen Yu. 2005. Fault tolerant control of multivariable processes using auto-tuning PID controller.. https://doi.org/10.1109/tsmcb.2004.839247

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