Decentralized adaptive neural two-bit-triggered control for nonstrict-feedback nonlinear systems with actuator failures

ABSTR A C T This article studies the adaptive neural decentralized two-bit-triggered control problem for intercon-nected large-scale nonlinear systems in nonstrict-feedback forms (NFF) with actuator failures. Since actu-ator failures occur frequently in practical systems, it will affect the stability of the interconnected large-scale systems under consideration. Combining radial basis function neural networks (RBF NNs) , a command filter, an adaptive decentralized two-bit-triggered (TBT) control method based on backstep-ping recursive design is presented to deal with this problem. Different from the traditional event-triggered control, the problem of control signal transmission bit is further considered to save system transmission resources. The proposed control scheme can guarantee that all signals are bounded and have good tracking performance. Finally, two simulation examples are provided to verify the validity of the presented control scheme.(c) 2022 Elsevier B.V. All rights reserved.

Automated summary

This article presents an adaptive neural decentralized two-bit-triggered control method for interconnected large-scale nonlinear systems with actuator failures. The proposed control scheme, which combines radial basis function neural networks, a command filter, and backstepping recursive design, effectively addresses the problem of control signal transmission bit and ensures the system signals to be bounded and exhibit good tracking performance.