title: Approach to adaptive neural net-based H/sub infinity / control design
abstract: An approach is investigated for the adaptive neural net-based H/sub infinity / control design of a class of nonlinear uncertain systems. In the proposed framework, two multilayer feedforward neural networks are constructed as an alternative to approximate the nonlinear system. The neural networks are piecewisely interpolated to generate a linear differential inclusion model by which a linear state feedback H/sub infinity / control law can be applied. An adaptive weight adjustment mechanism for the multilayer feedforward neural networks is developed to ensure H/sub infinity / regulation performance. It is shown that finding the control gain matrices can be transformed into a standard linear matrix inequality problem and solved via a developed recurrent neural network
keyphrases: adaptive neural net-based H/sub infinity / control design nonlinear uncertain systems multilayer feedforward neural networks piecewise interpolation linear differential inclusion model linear state feedback control gain matrices linear matrix inequality problem recurrent neural network LMI