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This paper focuses on designing an adaptive radial basis function neural network U+0028 RBFNN U+0029 control method for a class of nonlinear systems with unknown parameters and bounded disturbances. The problems raised by the unknown functions and external disturbances in the nonlinear system are overcome by RBFNN, combined with the single parameter direct adaptive control method. The novel adaptive control method is designed to reduce the amount of computations effectively. The uniform ultimate boundedness of the closed U+002D loop system is guaranteed by the proposed controller. A coupled motor drives U+0028 CMD U+0029 system, which satisfies the structure of nonlinear system, is taken for simulation to confirm the effectiveness of the method. Simulations show that the developed adaptive controller has favorable performance on tracking desired signal and verify the stability of the closed-loop system.
Yang et al. (Wed,) studied this question.