This paper presents a neural network-based robust finite-time H∞ control
design approach for a class of nonlinear Markov jump systems (MJSs). The system
under consideration is subject to norm bounded parameter uncertainties and external
disturbance. In the proposed framework, the nonlinearities are initially approximated
by multilayer feedback neural networks. Subsequently, the neural networks undergo
piecewise interpolation to generate a linear differential inclusion model. Then, based
on the model, a robust finite-time state-feedback controller is designed such that the
nonlinear MJS is finite-time bounded and finite-time stabilizable. The H∞ control is
specified to ensure the elimination of the approximation errors and external disturbances
with a desired level. The controller gains can be derived by solving a set of
linear matrix inequalities. Finally, simulation results are given to illustrate the effectiveness
of the developed theoretic results.