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The good control performance of the permanent magnet linear synchronous motor (LSM) drive system is very difficult achieved by using linear controller due to the uncertainty effects such as ending-frictious force. An adaptive modified recurrent Laguerre orthogonal polynomial neural network (NN) backstepping control system is proposed to increase the robustness of the LSM drive system. Firstly, the...
Due to uncertainties exist in the applications of the permanent magnet synchronous motor (PMSM) servo drive which seriously influence the control performance. The integral back stepping controller and adaptive recurrent neural network uncertainty observer (RNNUO) is proposed to control the rotor of the PMSM to track periodic references in this paper. Firstly, the field-oriented mechanism is applied...
In this paper an adaptive backstepping control system is proposed to control the rotor position of a permanent magnet synchronous motor (PMSM) drive using recurrent fuzzy neural network (RFNN). First, the field-oriented mechanism is applied to formulate the dynamic equation of the PMSM servo drive. Then, an adaptive backstepping approach is proposed to compensate the uncertainties in the motion control...
In this paper an adaptive backstepping control system is proposed to control the rotor position of a permanent magnet synchronous motor (PMSM) drive using recurrent neural network (RNN). First, the field-oriented mechanism is applied to formulate the dynamic equation of the PMSM servo drive. Then, an adaptive backstepping approach is proposed to compensate the uncertainties in the motion control system...
The linear synchronous motor (LSM) drive system using adaptive backstepping fuzzy neural network (ABFNN) control is investigated for the tracking of periodic reference inputs. First, the field-oriented mechanism is applied to formulate the dynamic equation of the LSM servo drive. Then, an adaptive backstepping approach is proposed to compensate the uncertainties in the motion control system. With...
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