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Updated: Jan 30, 2026

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
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Improved state space model predictive fault-tolerant control for injection molding batch processes with partial

Tao Zou1, Sheng Wu2, Ridong Zhang2

  • 1The Belt and Road Information Research Institute, School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China; Department of Information Service & Intelligent Control, Shenyang Institute of Automation, Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang 110016, China.

ISA Transactions
|January 29, 2019
PubMed
Summary

A new model predictive fault-tolerant control (MPFTC) strategy uses a genetic algorithm (GA) to improve batch process control despite disturbances and actuator faults. This method enhances performance in applications like injection molding.

Keywords:
Batch processesGenetic algorithmModel predictive fault-tolerant controlPartial actuator failure

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Area of Science:

  • Process Control
  • Automation Engineering
  • Artificial Intelligence in Control

Background:

  • Batch processes are susceptible to disturbances and actuator faults, impacting control performance.
  • Existing control strategies may lack robustness or adaptability to such challenging conditions.

Purpose of the Study:

  • To propose a novel model predictive fault-tolerant control (MPFTC) strategy for batch processes.
  • To enhance control performance by integrating a genetic algorithm (GA) for optimizing control parameters.

Main Methods:

  • Development of an extended state space model incorporating tracking error.
  • Implementation of a model predictive control framework with fault-tolerance capabilities.
  • Utilization of a genetic algorithm (GA) to optimize weighting matrices within the cost function.

Main Results:

  • The proposed MPFTC strategy demonstrates improved control performance.
  • The extended state space model provides greater flexibility in controller design.
  • Optimization via GA effectively tunes control parameters for better results.

Conclusions:

  • The novel MPFTC strategy effectively manages disturbances and partial actuator faults in batch processes.
  • The integration of GA significantly enhances the control performance.
  • The approach is validated through its application to injection velocity regulation in injection molding.