Quantised control for local Mittag–Leffler stabilisation of fractional‐order neural networks with input saturation: A refined sector condition

Abstract

This brief focuses on the local Mittag–Leffler stabilisation of fractional‐order neural networks via quantised control with input saturation. First, a refined sector condition is put forward, which can deal with the problem of the simultaneous existence of quantiser effect and actuator constraint. With the aid of refined sector condition, theoretical analysis on the local Mittag–Leffler stabilisation of the resulting closed‐loop systems is carried out by using some inequality techniques on Mittag–Leffler function and fractional‐order Lyapunov theory. In addition, two convex optimisation schemes are respectively developed to minimise the cost of actuators and to enlarge the admissible initial region. Finally, a three‐neurons fractional‐order neural networks is applied to illustrate the effectiveness of the derived results.

Combination projection synchronization of fractional-order complex dynamic networks with time-varying delay couplings and disturbances

“…The theory of fractional calculus is an extension of the theory of integer order calculus, and integer order calculus is only a special case of it. Fractional calculus can describe the physical phenomena in engineering applications more accurately than integer calculus and is more in line with engineering reality [14][15][16][17][18][19]. Fractional-order complex networks exhibit more irregular and unpredictable dynamic behaviors than integer-order complex networks, and therefore exhibit broader development trends and application potentials.…”