Stabilization of delayed semi-Markov jump neural networks with actuator faults: A quantized hybrid control approach

The presented research is to focus on the issue of fault alarm-based quantized hybrid control strategy for semi-Markov jump neural networks subject to multiple vulnerable factors, namely, actuator faults, quantization effects and time-varying delays. Particularly, the fault-based alarm signal with a threshold value is proposed for controller switching and also for preventing false alarms. Precisely, a logarithmic quantizer is incorporated in the control design to adjust the transmission of signals and to enhance better robustness on system performance. Besides, a mixed $H_{\infty }$ and passivity performance is employed in order to handle the traces of external disturbances. By proposing Lyapunov–Krasovskii functional involving time delays along with Wirtinger based integral inequality, the anticipated control gain parameters that confirm the stochastic stability of the addressed system can be determined with the assistance of linear matrix inequality. The excellent dynamic performances of the proposed control scheme are clarified through two numerical examples, whereas the stability of the system is restrained with the timely alert performance of the configured alarm signal.