Fault compensation by online updating of genetic algorithm-selected neural network model for model predictive control

Abstract

This paper presents a methodology that combines a dual-net model and the model predictive control (MPC) to compensate degraded system performance caused by slow-paced faults/anomalies. The dual-net model is comprised of an offline and an online artificial neural networks (ANNs) along with a switch that selects one of them for MPC. Through selective online updating of weight parameters, the online ANN is able to accurately capture the fault-induced variations in system dynamics, and can be used for MPC reconfiguration and fault compensation. Specifically, the system dynamics is identified by training a multilayer perceptron (MLP). To improve the model accuracy, a meta-optimization approach based on the genetic algorithm is applied to optimize the MLP hyperparameters and the training algorithm. A dual-thread decision maker is proposed to manage the robust model updating scheme and the dual-net model switch. A case study of numerical simulation using an unmanned quadrotor is undertaken to verify the feasibility of the proposed method to mitigate performance degradation. Salient performance in the response prediction and control, subject to gradually growing anomaly is successfully demonstrated. Quantitatively, the proposed updating model outperforms the offline ANN model and yields 2× and 4× lower errors, respectively, for prediction and control of the system response.

A novel meta-optimizer for evolutionary algorithms: bipolar mating tendency

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Automated optimization of double heater convective polymerase chain reaction devices based on CFD simulation database and artificial neural network model

“…In addition, with the advent of deep learning, it is possible for the ANN to accurately model a highly complex system by utilizing deep architectures, e.g., multiple hidden layers (Stoecklein et al 2017 ; Malkiel et al 2018 ; Hong et al 2020 ; Kasim et al 2001 ). ANN-based optimization has proved its vast values in diverse areas, including hydrogen purification, design of a solar power plant, model predictive control, and prediction of smart grid energy consumption (Ye et al 2019 ; Boukelia et al 2016 ; Hong et al 2019 ; Muralitharan et al 2018 ).…”