This paper investigates a synchronous control method with variable proportion for multi-axis servo system based on sliding mode variable structure to solve the inherent shortcomings of parallel synchronous uncoupled control for multi-servo motors. The method is an improvement of the ring-coupled control strategy and is thus applicable to multi-axis proportional synchronous control. No-salient pole permanent magnet synchronous servo motors are taken as the controlled objects. A sliding mode controller is designed according to the state equation of the mathematical model. The rationality of the designed controller is proven by Lyapunov stability theory, and the method is compared with a parallel synchronous control strategy, which is widely used in the object of this research. Finally, the feasibility and superiority of the method in a multi-axis synchronous control system with variable proportion is confirmed by MATLAB simulation and experimental verification. The method is applied to a three-degree-of-freedom servo system, and makes the theoretical research have the corresponding practical engineering application value.

To improve the torque precision of permanent magnet synchronous motor (PMSM) in railway vehicles, a nonsingular terminal sliding mode observer (NTSMO) is designed to detect permanent-magnet (PM) demagnetization, and a precise torque control method is proposed for the railway PMSM vector control system. First-of-all, the PMSM mathematical model of PM demagnetization is presented. Secondly, a NTSMO is constructed to reconstruction PM flux linkage in d-q reference frame. Meanwhile, the observer stability proof is given. The estimated torque could be calculated according to the estimated PM flux linkage. Finally, the overall system is verified by simulation in Matlab/Simulink. Compared with the sliding mode observer method, the torque control precision of the driving system could be improved, and the driving performance of railway vehicles is fulfilled and guaranteed.

Vector quantization encoding requires expensive time to find the closest codeword through the codebook for each input vector. A fast search algorithm for encoding is proposed in this paper. The multilevel elimination criterion is still derived from the three features (mean, variance, and norm) inequalities constraints, but the order of the three inequalities constraints, instead of the predefined order like other conventional multilevel elimination criterion, is optimized to speed up the encoding procedure. In the proposed algorithm, the elimination criterion at the first level is set to mean inequality constraint because of its narrower search width, and the priority order at the second and/or third level of variance and norm inequalities constraints is optimized based on the codewords distribution and the location of input vector in terms of the considered features. The experimental results demonstrate the effectiveness of the proposed algorithm.