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In this article, we proposed a discrete-time sliding mode load thrust observer to reduce the influence of load thrust mutation on the linear motor and improve the antiinterference ability and speed tracking performance of the system. First, the electrical and dynamic models of linear motor are established, and the limitation of conventional double-closed-loop control based on proportional integral (PI) algorithm is analyzed. Second, the extended state equation of velocity and load thrust is given and an improved sliding mode reaching law is designed to establish the discrete-time load thrust observer. Based on Lyapunov theory, the stability of the observer is proved. The observed load thrust is compensated to the controller in the form of feedforward. Finally, a series of comparative experiments were carried out. The experimental results show that compared with the PI control, the maximum speed fluctuation of this method is reduced by about 18.1% when the load changes between no-load and rated load. This means that the method in this article can effectively improve the antidisturbance ability of the system. [ABSTRACT FROM AUTHOR]

To solve the problem that both analytical model (AM) and finite element model are difficult to give consideration to the calculation accuracy and efficiency at the same time in motor optimization work, a novel Lasso regression with the embedded AM, called EAM-LR, is proposed to quickly and accurately calculate the thrust performance of the permanent magnet synchronous linear motor (PMSLM) in this article. First, the thrust performances of PMSLM are analyzed by the AM to determine the variation range of structural design parameters. Based on the variation range, a finite-element sample database is established. Then, combined with the finite-element sample database, the analytical mapping functions derived from AM, are integrated into Lasso regression to establish EAM-LR. The accuracy of EAM-LR was verified by comparison with AM, traditional lasso regression, and well-known extreme learning machine. Finally, combined with the EAM-LR, the chaotic golden section algorithm is introduced to search the optimal structure parameters of PMSLM, and the control simulation and prototype experiment prove the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]