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Related Experiment Videos

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

Ding-Li Yu1, T K Chang, Ding-Wen Yu

  • 1Control Systems Research Group, School of Engineering, Liverpool John Moores University, Liverpool, L3 3AF, UK.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|February 22, 2005
PubMed
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This study introduces a fault-tolerant control system using an auto-tuning PID controller and an adaptive neural network. The system effectively compensates for process faults, ensuring stable control performance in dynamic systems.

Area of Science:

  • Control Engineering
  • Process Systems Engineering
  • Artificial Intelligence in Engineering

Background:

  • Dynamic processes are susceptible to faults, leading to degraded performance and potential instability.
  • Traditional control systems often lack the adaptability to effectively manage unforeseen system failures.
  • Developing robust fault-tolerant control (FTC) strategies is crucial for reliable industrial operations.

Purpose of the Study:

  • To propose a novel fault-tolerant control scheme for dynamic processes.
  • To enhance system resilience and maintain control performance despite component failures.
  • To develop an auto-tuning PID controller integrated with an adaptive neural network for real-time fault compensation.

Main Methods:

  • An adaptive neural network model trained online using the extended Kalman filter (EKF) algorithm to capture post-fault system dynamics.

Related Experiment Videos

  • An auto-tuning proportional-integral-derivative (PID) controller designed using the Lyapunov method for guaranteed stability.
  • Online parameter adjustment of the PID controller based on the learned system model to counteract fault effects.

    • Main Results:

    • The proposed scheme successfully adapted to various simulated faults in a continuous stirred tank reactor (CSTR).
    • The auto-tuning PID controller effectively compensated for fault-induced performance degradation.
    • Asymptotic convergence of the model-predicted tracking error was guaranteed by the Lyapunov-based design.

    Conclusions:

    • The developed fault-tolerant control scheme demonstrates high applicability for industrial processes.
    • The integration of adaptive neural networks and auto-tuning PID controllers offers a robust solution for dynamic process control under fault conditions.
    • The method ensures reliable operation and performance recovery in the presence of system faults.