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

Gomathi Veerasamy, Ramkumar Kannan, RakeshKumar Siddharthan, Guruprasath Muralidharan, Venkatesh Sivanandam and Rengarajan Amirtharajan

Mathematics and Computers in Simulation (MATCOM), 2022, vol. 191, issue C, 256-277

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.

Keywords: Genetic algorithm; Kalman filter; Fading memory; Model predictive control; Cement kiln; Fault-tolerant control (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:191:y:2022:i:c:p:256-277

DOI: 10.1016/j.matcom.2021.07.023

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