Fixed-time neural control for output-constrained synchronization of second-order chaotic systems

In this article, a fixed-time neural control methodology is presented for the output-constrained synchronization of second-order chaotic systems with unknown parameters and perturbations. The presented controller is synthesized under the fixed-time backstepping control framework. In the virtual control law design, the barrier Lyapunov function (BLF) is introduced to tackle the output constraints. In the actual control law design, the neural network (NN) is embedded to identify the total unknown item. Stability argument shows that the resultant closed-loop system is practically fixed-time stable. A distinctive feature of the presented controller is that it is capable of stabilizing the synchronization errors in fixed time while ensuring the output constraints can always be satisfied simultaneously. The efficiency and superiority of the presented control methodology are examined through two simulated examples.

Automated summary

This article presents a fixed-time neural control methodology for the output-constrained synchronization of second-order chaotic systems with unknown parameters and perturbations. The controller is synthesized under the fixed-time backstepping control framework. The barrier Lyapunov function (BLF) is introduced in the virtual control law design to handle the output constraints. The neural network (NN) is embedded in the actual control law design to identify the total unknown item. Stability analysis shows that the resultant closed-loop system is practically fixed-time stable.