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Integration of genetic algorithm tuned adaptive fading memory Kalman filter with model predictive controller for active fault-tolerant control of cement kiln under sensor faults with inaccurate noise covariance

Author

Listed:
  • Veerasamy, Gomathi
  • Kannan, Ramkumar
  • Siddharthan, RakeshKumar
  • Muralidharan, Guruprasath
  • Sivanandam, Venkatesh
  • Amirtharajan, Rengarajan

Abstract

Reliable control of cement kiln under sensor faults is vital to ensure cement clinker quality. To accomplish this objective, an active fault-tolerant control (FTC) scheme is proposed for burning zone temperature control of cement kiln even under inaccurate measurements. FTC is achieved by integrating adaptive fading memory Kalman filter (AFMKF) in an already existing model predictive controller (MPC) framework. Residue from the Kalman filter is used to detect faults, and the model prediction replaces sensor measurements. Hence, there is a need for an accurate model and its associated noise covariance. Model accuracy is ensured by the online estimation of sensor noise covariance using the proposed AFMKF. Hyperparameters governing the AFMKF are tuned using the multi-objective genetic algorithm (GA), aiming to minimise the modelling and tracking error. Sensor measurement failure, bias, and stuck are considered for performance validation of the proposed FTC. Percentage recovery from fault is evaluated in terms of Integral square error (ISE) and energy utilisation compared with a conventional MPC. Experimental results illustrate the significant improvement in model accuracy and fault-tolerant behaviour using the proposed GA tuned AFMKF.

Suggested Citation

  • Veerasamy, Gomathi & Kannan, Ramkumar & Siddharthan, RakeshKumar & Muralidharan, Guruprasath & Sivanandam, Venkatesh & Amirtharajan, Rengarajan, 2022. "Integration of genetic algorithm tuned adaptive fading memory Kalman filter with model predictive controller for active fault-tolerant control of cement kiln under sensor faults with inaccurate noise ," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 191(C), pages 256-277.
    • Handle: RePEc:eee:matcom:v:191:y:2022:i:c:p:256-277
    DOI: 10.1016/j.matcom.2021.07.023
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    References listed on IDEAS

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    1. Jun Zhou & Yuan Liu & Tianhong Zhang, 2016. "Analytical Redundancy Design for Aeroengine Sensor Fault Diagnostics Based on SROS-ELM," Mathematical Problems in Engineering, Hindawi, vol. 2016, pages 1-9, April.
    2. Amrane, Ahmed & Larabi, Abdelkader & Aitouche, Abdel, 2020. "Unknown input observer design for fault sensor estimation applied to induction machine," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 167(C), pages 415-428.
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