Neural Network Backstepping Controller Design for Uncertain Permanent Magnet Synchronous Motor Drive Chaotic Systems via Command Filter

Abstract

In this study, an adaptive neural network (NN) command filtered control (CFC) method is proposed for a permanent magnet synchronous motor (PMSM) system with system uncertainties and external disturbance by means of a backstepping technique. At every backstepping step, a novel command filter is proposed, and the complicated virtual input and its derivative together can be approximated by this filter. The "explosion of complexity" problem in conventional backstepping design can be avoided because we do not need to calculate the derivative of the virtual input repeatedly. NNs are used to model system uncertainties and disturbances. Finally, an adaptive NN CFC is designed, and the convergence of the tracking error and the boundedness of all signals involved can be guaranteed. Finally, a simulation study is presented to verify the theoretical results.

Robust decomposed system control for an electro‐mechanical linear actuator mechanism under input constraints

“…Input vectors (control variables) of the system consist of input voltages of a PMSM along the dq-axis, which are given in (25):…”

“…22 Furthermore, numerous studies in the literature have focused on enhancing the robustness of PMSMs using different types of adaptive backstepping control techniques, which have been demonstrated to possess significant potential for modeling complex systems and as adaptive controllers for nonlinear systems, as evidenced in previous works. [23][24][25] In the field of PMSM control, most research and studies have been conducted with a focus on velocity control. However, the current work is being discussed in a context focused primarily on position servo control.…”

Adaptive backstepping controller design based on neural network for PMSM speed control

“…With the assistance of neural networks, the linearity-in-the-parameter assumption of nonlinear function and the determination of regression matrices can be avoided. As a consequent of this association that in the last decade, a several scheme based on backstepping design schemes which combine between the backstepping technique and adaptive NNs are proposed [19], [20], [21], [24]- [28]. In this section, a neural network for parameter estimation of the adaptive nonlinear backstepping controller is investigated.…”

“…Exploiting their universal approximation potentienality, the intelligent controller associated with NNs may be constructed without knowledge requirement of the systems. A detailed state-of-art about the different applications of NNs and their employement in the power electronics devices and variable speed applications has been presented in [24]. The authors of [19] have been proposed an intelligent adaptive-backstepping controller design using a hidden-layer RNN for the mover position control of linear induction motor, in which the method of gradient-descent is utiliszed to determine the NN parameter-training algorithms.…”

Dual-Motor Synchronization Control Design Based on Adaptive Neural Networks Considering Full-State Constraints and Partial Asymmetric Dead-Zone

“…At the same time, they took no account of introducing an error compensation mechanism to obtain a better control performance for the controlled systems. Fortunately, a CFB approach was presented in [ 14 , 15 , 16 , 17 ] to solve the same problem, and the error compensation was also introduced to cope with the drawbacks of DSC. Thus, the computational burden of the design process was reduced, and the tracking error decreased.…”