Robust Composite H∞ Synchronization of Markov Jump Reaction–Diffusion Neural Networks via a Disturbance Observer-Based Method

This article focuses on the composite $\mathcal {H}_{\infty }$ synchronization problem for jumping reaction–diffusion neural networks (NNs) with multiple kinds of disturbances. Due to the existence of disturbance effects, the performance of the aforementioned system would be degraded; therefore, improving the control performance of closed-loop NNs is the main goal of this article. Notably, for these disturbances, one of them can be described as a norm-bounded, and the other is generated by an exogenous model. In order to reject the above one kind of disturbance, a disturbance observer is developed. Furthermore, combining the disturbance observer approach and conventional state-feedback control scheme, a composite disturbance rejection controller is specifically designed to compensate for the influences of the disturbances. Then, some criteria are established based on the general Lyapunov stability theory, which can ensure that the synchronization error system is stochastically stable and satisfies a fixed $\mathcal {H}_{\infty } $ performance level. A simulation example is finally presented to verify the availability of our developed method.