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AN LMI APPROACH TO DESIGN H∞ CONTROLLERS FOR DISCRETE-TIME NONLINEAR SYSTEMS BASED ON UNIFIED MODELS

MEIQIN LIU, SENLIN ZHANG

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A unified neural network model termed standard neural network model (SNNM) is advanced. Based on the robust L2 gain (i.e. robust H performance) analysis of the SNNM with external disturbances, a state-feedback control law is designed for the SNNM to stabilize the closed-loop system and eliminate the effect of external disturbances. The control design constraints are shown to be a set of linear matrix inequalities (LMIs) which can be easily solved by various convex optimization algorithms (e.g. interior-point algorithms) to determine the control law. Most discrete-time recurrent neural network (RNNs) and discrete-time nonlinear systems modelled by neural networks or Takagi and Sugeno (T–S) fuzzy models can be transformed into the SNNMs to be robust H performance analyzed or robust H controller synthesized in a unified SNNM's framework. Finally, some examples are presented to illustrate the wide application of the SNNMs to the nonlinear systems, and the proposed approach is compared with related methods reported in the literature.