Neural networks-based command filtering control of nonlinear systems with uncertain disturbance

This paper is concerned with neural networks-approximation based command filtering backstepping control for uncertain strict-feedback nonlinear systems with unknown disturbances. The “explosion of complexity” problem arising from the virtual controllers' derivatives is resolved by utilizing the command filtering technique, and the shortcoming existing in dynamic surface method is properly overcome via an introduced error compensation mechanism (ECM). Moreover, the nonlinear functions of the underlying system are well approximated by exploiting neural networks-based framework. The developed strategy may cover two features with comparison of current achievements: 1) The filtering error can be eliminated in the light of the designed compensating signals; 2) The requirement of adaptive parameters is reduced to only one, which may enhance the control performance for realistic project implementation. At last, an application example in position tracking control of surface permanent magnet synchronous motor (SPMSM) is carried out to further verify the effectiveness and advantages of the theoretical result.