Juncai Song

A novel multiple linear motor control system based on Nios II

A laser engraving machine is a kind of multi-axis CNC machine tool with no cutting force. In order to provide better feed motions in multi-axis, a novel multiple linear motor control system based on Nios II soft processors is introduced in this paper. Each Nios II soft processor controls a permanent magnet synchronous linear motor (PMSLM). The fuzzy two degree of freedom Proportional-Integral-Differential (PID) control algorithm based on Kalman filter written in C runs on the Nios II soft processors. Relative coupling-based multi-motor synchronous control algorithm for the PMSLM is introduced to provide high synchronization accuracy and strong robustness. Experimental results show that compared to conventional multiple linear motor control systems, control system based on Nios II soft processors provides a good synchronicity, higher efficiency and higher accuracy.

An Efficient Multi-Objective Design Optimization Method for PMSLM Based on Extreme Learning Machine

This paper focuses on the multiobjective design optimization of the permanent magnet synchronous linear motors (PMSLMs), which are applied to a high-precision laser engraving machine. A novel efficient multiobjective design optimization method for a PMSLM is proposed to achieve optimal performances as indicated by high average thrust, low thrust ripple, and low total harmonic distortion at different running speeds. First, based on the finite-element analysis (FEA) data, a regression machine learning algorithm, called an extreme learning machine (ELM), is introduced to solve the calculation modeling problem by mapping out the nonlinear and complex relationship between input structural factors and output motor performances. Comparative simulation experiments conducted using the traditional analytical modeling method and another machine learning modeling method, i.e., support vector machine, confirm the superiority of the ELM. Then, a new bionic intelligent optimization algorithm, called the gray wolf optimizer algorithm, is used to search the best optimization performances and structural parameters by performing iteration optimization calculation for multiobjective functions. Finally, FEA and prototype motor experiments prove the effectiveness and validity of the proposed method.

A new regression modeling method for PMSLM design optimization based on K-Nearest Neighbor Algorithm

This paper focuses on the rapid regression modeling research for the design optimization of Permanent magnet synchronous linear motors (PMSLM) which are applied in the linear motion machines. Based on the Finite Element Analysis (FEA), the initial PMSLM model is built and the modeling data of optimization are obtained. Then, the machine learning regression algorithm named K-Nearest Neighbor Algorithm (KNNA) is introduced to mapping the nonlinear relations of structure parameters and performances, and establishes the rapid calculation model for the next optimization. Finally, the superiority and reliability of this method is confirmed by the FEA experiments.

Current harmonic suppression in the flux-weakening control based on resonant-2DOF PID controllers for permanent magnet synchronous linear motor

In this paper, the current harmonic suppression problem of permanent magnet synchronous linear motor (PMSLM) in the flux-weakening control is studied by using the proposed resonant two degree of freedom (2DOF) current PID controllers. First, the principle of the flux-weakening is analyzed based on the mathematical model and the current voltage constraints of PMSLM in the rotating reference coordinate system. Then a current leading angle flux-weakening control method is designed, which provides a wide velocity operation of PMSLM over based velocity. Subsequently, the resonant-2DOF PID controllers are adopted to effectively suppress the current harmonics induced by the non-ideal factors in the flux-weakening control. At last, the simulation studies and experiments are performed for PMSLM based on the control platform in comparison with the traditional PID controller. The contrast results indicate that the proposed method suppresses the current harmonics effectively, and achieves optimal tracking ability and optimal antidisturbance performance simultaneously.