The deadbeat control is one of the widely concerned control methods for the permanent-magnet synchronous motor (PMSM) due to its fast dynamic performance. However, its control performance relies heavily on the accuracy of the PMSM model parameters, which may vary with the operation cases. In this article, a novel online parameter identification algorithm is proposed for the PMSM deadbeat control. First, an identification model is established to estimate the parameter errors from the offsets of the deadbeat control, in which the nonlinearity of the voltage source inverter is fully concerned. Then, a novel “parameter perturbation method” is proposed for gathering the essential data to solve the rank deficient problem in the parameter identification process. It does not need to inject additional instructions in the control, resulting in no impact on the normal operation of the PMSM. The Adaline neural network is employed to achieve the online acquisition of parameter identification results. Finally, the effectiveness and superiority of the proposed algorithm are verified by experimental results on a PMSM platform.

In dual-motor system, uncertain disturbance, parameter differences, and mechanical issues deteriorate the synchronization. Moreover, motor collision problem arises in rigidly connected systems because of poor synchronization and transient large impact. Considering these factors, this article proposes a nonlinear cross-coupled control strategy based on common mode differential mode (CM/DM) separation to achieve position synchronization for dual-motor system under speed control. By utilizing the CM/DM separation method in cross-coupled control, the objectives of position synchronization control and speed tracking control is decoupled. Taking into account the collision model, mechanical clearance, disturbance, and real-time status of the dual-motor system, a nonlinear controller is designed to achieve high-accuracy synchronization and collision recovery. The stability of the dual-motor system is proved by analysis of frequency domain and nonlinear differential equations, and the tuning method for the proposed controller is provided. By comparison with the traditional cross-coupled control method, the effectiveness of the proposed control strategy is validated by experimental results.

Oil-immersed cooling provides an effective cooling scheme for high-power hub motors with compact structure and serious heating problems. However, with this cooling method, some oil friction loss will be generated, making the output torque and efficiency of the motor lower, which limits its application in the motor. It is essential to get an exact calculation of the oil friction loss so that it can be reduced in the future research. Firstly, a new method was proposed to improve the accuracy of oil friction loss calculated by an existing analytical method (Kori’s method), while the influence laws of oil-soaked depth and rotation speed on it were explored. Secondly, a three-dimensional transient Computational Fluid Dynamic (CFD) model based on Volume of Fluid (VOF) was established, considering the actual complex structure and the disorderly mixing of oil and air inside the motor. Finally, the oil friction loss calculated with an improved analytical method and a VOF model was verified by a testing. It was indicated that the VOF model was more precise but more time-consuming. The proposed method has the second highest accuracy but takes less time.

Trajectory planning and tracking control strategies have a significant impact on the fast and stable operation of high-precision position servo permanent magnet synchronous motors (PMSMs). Therefore, this paper proposes an active disturbance rejection control (ADRC) strategy for high-precision position servo PMSMs based on jerk- and time-optimal trajectory planning. Firstly, in order to meet the requirement of continuous jerk in the positioning process of precision loads, the seventh-degree Chebyshev polynomial is adopted to establish the point-to-point trajectory planning function. Based on the dynamic boundary conditions under the short-term overload of PMSMs, and with the positioning time as the optimization objective, the optimal coefficient of the polynomial is solved through the fast particle swarm optimization (FPSO) algorithm to obtain the trajectory planning function that takes into account both jerk and time performance. Then, the trajectory plan is used as the position loop reference signal to construct a non-cascade second-order ADRC strategy, leading to a position servo PMSM control strategy that combines a second-order disturbance observer and feedback control law. Finally, the experimental platform is set up to verify the proposed method. The results show that, compared with the traditional control methods, the steady-state positioning time of the control strategy proposed under typical working conditions is reduced by 12.5%, and the jerk continuity during the positioning process has also been significantly improved.

Motor efficient design is an important measure to reduce the energy consumption of servo motor system. Existing methods typically focus on optimizing the efficiency at specific operating points or the proportion of the high-efficiency region, making it difficult to quantify the matching of the position servo motor's periodic wide-domain operating conditions under trajectory planning. In this article, an energy efficiency optimization design method for position servo permanent magnet synchronous motor (PMSM) based on a cycle operating energy consumption model is proposed. First, the periodic operating states of PMSM under position trajectory planning are characterized by the speed-torque operating curve. A neural network mapping between PMSM full-domain dynamic losses and speed-torque-temperature is constructed based on finite element data. Combined with the physical analytical model of mechanical power and friction, a data-model driven precise model is established, enabling quantitative evaluation of the cycle energy consumption with different PMSM design schemes; then, taking cycle operating energy consumption and peak torque as optimization objectives, the optimal Latin hypercube sampling method is employed to generate finite element optimization data samples. Dimension reduction of design variables is performed through correlation analysis, followed by the establishment of a precise response surface model for optimization objectives and significant variables. The optimal design scheme after global optimization is quickly solved by the evolutionary algorithm. Finally, the effectiveness of the proposed method is verified through simulation and prototype experiments.