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

Abstract 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.