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To improve the thrust performance of permanent magnet synchronous linear motor (PMSLM), novel deep adaptive ridge regression with embedded analytical mapping function (EAMF-RR) is proposed to calculate the thrust performance of PMSLM quickly and accurately, and combined with the EAMF-RR, a whale optimization algorithm (WOA) is introduced to optimize the PMSLM structure. First, the PMSLM's thrust is analyzed by the analytical modeling (AM) to determine the variation range of structural design parameters. Based on the variation range, a finite-element sample database is established for the EAMF-RR. Then, EAMF derived from AM, k-nearest neighbor algorithm, and WOA are integrated into RR to establish deep adaptive EAMF-RR model. Comparative simulation experiments conducted by using another two machine learning methods, i.e., extreme learning machine and weighted random forest, confirm the superiority of the EAMF-RR. Finally, WOA is reused to optimize the thrust performance of PMSLM, and prototype experiments prove the effectiveness of the proposed optimization method. [ABSTRACT FROM AUTHOR]

To realize the demagnetization fault diagnosis and degree estimation of permanent-magnet synchronous linear motors in linear motion applications with high precision and multiple working conditions, a novel double closed-loop control with an adaptive linear neuron (ALN) model reference adaptive system (MRAS) flux linkage observer is proposed in this article. First, a sliding-mode control (SMC) is introduced to design the velocity loop to obtain good speed adaptability. Then, the MRAS flux observer is established, and the ALN algorithm for modifying the MRAS adaptive law online is designed combining with MRAS flux observer. In addition, the finite element models of normal, weak (F1), mild (F2), moderate (F3), and severe (F4) demagnetization faults are established, and the accurate values of flux linkage under different demagnetization degrees are simulated. Furthermore, the demagnetization fault diagnosis algorithm is introduced with an SMC-ALN-MRAS flux observer. Finally, a prototype motor experimental platform is built, and comparative experiments are designed to confirm the correctness and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper proposes a high-performance current regulation scheme to enhance the current bandwidth and robustness of the permanent magnet synchronous linear motor (PMSLM). First, the disturbance caused by parameter variations and thrust ripple is considered in the modeling of PMSLM. Based on the model, a two-degree-of-freedom (2-DOF) controller consisting of improved predictive current control (PCC) and adaptive internal model control is derived. The improved PCC is used to overcome time delay and increase current control bandwidth, whilst the adaptive internal model is utilized to estimate the disturbance caused by parameter variations to compensate PCC. Second, a new discrete-time Jacobian linearization observer is designed to estimate the thrust ripple. The actual thrust ripple can be suppressed by injecting the estimated value into the control system. Subsequently, a comparatively smoother output thrust and higher precision position tracking is obtained. Finally, the precise test platform based on semiphysical system AD5435 is established. The experimental results verify the validity of the proposed approach. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper focuses on the thrust ripple reduction in permanent magnet synchronous linear motors (PMSLMs), which are applied to laser cutting machines. To meet the high-precision performance requirements of laser cutting machines, a novel auxiliary suppression device, tuned viscoelastic damper (TVD), is designed to reduce the thrust ripple of PMSLM. First, TVD is designed with the structure and motion characteristics of PMSLM. The PMSLM with TVD is an integrated structure. Then, the equivalent magnetization method is used to analyze the PMSLM thrust, which is the excitation of TVD. Under the excitation of the PMSLM thrust, the damping force of TVD is expressed by the Kelvin model and validated by finite-element analysis (FEA). Subsequently, the thrust of PMSLM with TVD is obtained by adding the PMSLM thrust and TVD damping force. Finally, the thrust ripple rate is selected as a performance index of TVD in reducing the thrust ripple. Simulation is performed to confirm the theoretical analysis. A prototype of PMSLM with TVD is manufactured and experiments are conducted to validate the effectiveness of the proposed auxiliary suppression device. [ABSTRACT FROM AUTHOR]