Finite-Time and Fixed-Time Stabilization Control of Delayed Memristive Neural Networks: Robust Analysis Technique

This paper provides finite-time and fixed-time stabilization control strategy for delayed memristive neural networks. Considering that the parameters in the memristive model are state-dependent, which may contain unexpected parameter mismatch when different initial conditions are chosen, in this case, the traditional robust control and analytical methods cannot be carried out directly. To overcome this problem, a brand new robust control strategy was designed under the framework of Filippov solution. Based on the designed discontinuous controller, numerically testable conditions are proposed to stabilize the states of the target system in finite time and fixed time. Moreover, the upper bound of the settling time for stabilization is estimated. Finally, numerical examples are exhibited to explain our findings.