NNs-Based Adaptive Control for Genetic Regulatory Networks With Sensor Faults

This article considers an adaptive control problem of genetic regulatory networks, where unknown sensor faults are considered. By using the function approximation capability of neural networks, a neural-networks-based gene circuit control method is designed, where the unknown sensor faults are compensated. Comparing with the existing results where regulatory functions meet known SUM logic, the regulatory functions considered in this article are unknown and do not satisfy SUM logic. Furthermore, the fault negative influence on neural network function approximation, which is caused by state sensor faults, has been compensated. In sense of Lyapunov stability theory, the closed-loop system is asymptotically bounded and all the signals in the system converge to an adjustable neighborhood of the origin. Finally, some simulation results are given to show the effectiveness of the design method.