Decentralized adaptive neural fixed-time tracking control of constrained interconnected nonlinear systems with partially unmeasurable states

This article devises a new adaptive fixed-time tracking control strategy for interconnected nonlinear systems containing partially unmeasurable states and time-varying output constraints. Radial basis function neural networks, as function approximators, are utilized to model the unknown functions, and the partially unmeasurable states of the systems are estimated by a reduced-order observer. By constructing a transferred function, system outputs are directly constrained in a time-varying constraint bound. Meanwhile, the first-order sliding mode differentiators are utilized to reduce the computational burden caused by the repeated differentiations of virtual controllers. Under the Lyapunov function and the fixed-time theory, the decentralized adaptive fixed-time controllers are constructed. It is proved that the closed-loop systems are fixed-time stable and the output signals are restricted in the bounded compact set. Finally, two simulation examples demonstrate the validity of the proposed control scheme.

Automated summary

This article proposes a new adaptive fixed-time tracking control strategy for interconnected nonlinear systems with partially unmeasurable states and time-varying output constraints. Radial basis function neural networks are used to model unknown functions, and a reduced-order observer is employed to estimate the partially unmeasurable states. By constructing a transferred function, the system outputs are constrained within a time-varying constraint bound. The proposed approach also reduces computational burden by using first-order sliding mode differentiators. The decentralized adaptive fixed-time controllers are constructed based on Lyapunov function and fixed-time theory, achieving fixed-time stability and restricting output signals within a bounded compact set. The effectiveness of the proposed control scheme is demonstrated through simulation examples.