Neural-Based Decentralized Adaptive Finite-Time Control for Nonlinear Large-Scale Systems With Time-Varying Output Constraints

This paper addresses the adaptive finite-time decentralized control problem for time-varying output-constrained nonlinear large-scale systems preceded by input saturation. The intermediate control functions designed are approximated by neural networks. Time-varying barrier Lyapunov functions are used to ensure that the system output constraints are never breached. An adaptive finite-time decentralized control scheme is devised by combining the backstepping approach with Lyapunov function theory. Under the action of the proposed approach, the system stability and desired control performance can be obtained in finite time. The feasibility of this control strategy is demonstrated by using simulation results.

Automated summary

This paper addresses the adaptive finite-time decentralized control problem for time-varying output-constrained nonlinear large-scale systems preceded by input saturation. The control functions designed are approximated by neural networks, and time-varying barrier Lyapunov functions are used to ensure that the system output constraints are never breached. The proposed approach combines the backstepping approach with Lyapunov function theory, demonstrating the feasibility of the control strategy through simulation results.