Intelligent second‐order sliding mode control for permanent magnet linear synchronous motor servo systems with robust compensator

Abstract

In this study, an intelligent second‐order sliding mode control (SMC) method combining second‐order SMC (SOSMC) and recurrent radial basis function neural network (RRBFNN) applicable to the permanent magnet linear synchronous motor (PMLSM) is proposed to achieve high‐performance servo control fields. On the basis of a dynamic model of PMLSM and the SMC theory, the chattering problem in SMC is weakened and the tracking accuracy is improved by the design of SOSMC. As for the boundary of the uncertainty factors is difficult to obtain, the optimal performance of SOSMC is hard to achieve, the RRBFNN uncertainty observer is introduced for estimating the value of the uncertainty factors. Owing to the strong learning ability, the network parameters can be trained online. Besides, a robust compensator is developed to suppress the uncertainties such as approximation error, optimal parameter vector and higher Taylor series for further improving the robustness. Moreover, the adaptive learning algorithms are obtained by using the Lyapunov theorem to guarantee the asymptotical stability of the system. The experiments demonstrate that the proposed scheme provides high performance dynamic characteristics and strong robustness to uncertainties.

Calculation and analysis of the unbalanced magnetic thrust for the linear motor drive motion system

“…Zhao et al. [11] presented an intelligent second‐order sliding mode control method combining second‐order sliding mode control and recurrent radial basis function neural network applicable to the PMLSM. However, the above research mainly focuses on the linear motor itself.…”

“…The thrust fluctuation caused by the non-linear drive circuit and the permanent magnet linear motor (PMLM) is one of the significant restrictions in the linear motor drive system [2], which has obtained the attention of many scholars. Many studies focus on the modelling and analysis of the linear motor and various optimisation and compensation strategies have been put forward [3][4][5][6][7][8][9][10][11], which is of great value for diminishing the thrust fluctuations in the linear motor. Hu et al [3] analysed the end forces caused by the longitude end effects in linear permanent-magnet synchronous machines.…”

“…Liu et al [10] proposed an online data-driven multi-objective optimisation to suppress the undesired thrust ripple and improve the average thrust of the PMLSM. Zhao et al [11] presented an intelligent second-order sliding mode control method combining second-order sliding mode control and recurrent radial basis function neural network applicable to the PMLSM. However, the above research mainly focuses on the linear motor itself.…”

Adaptive Backstepping Nonsingular Terminal Sliding Mode Control of Servo System Based on New Sliding Mode and Reaching Law

“…Reference [24] showed that genetic algorithm is used to optimize adaptive parameters to estimate unknown disturbances in perturbed systems. On the basis of backstepping control, Lyapunov function method is used to design adaptive estimation of load variables [25,26]. Therefore, the adaptive algorithm is introduced into the identification of moment of inertia and viscous friction coefficient to ensure that the estimated value can track the actual value in real time.…”

Adaptive Neural Network Nonsingular Fast Terminal Sliding Mode Control for Permanent Magnet Linear Synchronous Motor

“…However, PMLSM is a nonlinear and uncertain object. It is difficult to obtain accurate mathematical models in practice, and PMLSM adopts direct drive structure, which reduces the elastic deformation between traditional mechanical structures and improves the stiffness of the transmission system [2]. The reduction of the intermediate buffer mechanism causes many uncertain factors to directly act on the PMLSM mover, which poses a severe challenge to achieve system control and improve performance.…”