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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.

This paper presents a new artificial neural network (ANN)-based system model that concatenates an optimized artificial neural network (OANN) and a neural network compensator (NNC) in series to capture temporally varying system dynamics caused by slow-paced degradation/anomaly. The OANN comprises a complex, fully connected multilayer perceptron, trained offline using nominal, anomaly-free data, and remains unchanged during online operation. Its hyperparameters are selected using genetic algorithm-based meta-optimization. The compact NNC is updated continuously online using collected sensor data to capture the variations in system dynamics, rectify the OANN prediction, and eventually minimize the discrepancy between the OANN-predicted and actual response. The combined OANN-NNC model then reconfigures the model predictive control (MPC) online to alleviate disturbances. Through numerical simulation using an unmanned quadrotor as an example, the proposed model demonstrates salient capabilities to mitigate anomalies introduced to the system while maintaining control performance. We compare the OANN-NNC with other online modeling techniques (adaptive ANN and multi-network model), showing it outperforms them in reference tracking of altitude control by at least 0.5 m and yaw control by 1°. Moreover, its robustness is confirmed by the MPC consistency regardless of anomaly presence, eliminating the need for additional model management during online operation.

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