Permanent magnet synchronous motor (PMSM) AC servo system has the characteristics of uncertainty, time-varying, nonlinear and fractional order. Applying the traditional control method is difficult to achieve the desired control effect. The fuzzy control has strong adaptability to the parameter change, nonlinearity and imprecise model of the controlled object. The simulation model of permanent magnet synchronous motor three closed-loop systems is established. The control principle and the realization of space vector pulse width modulation (SVPWM) are studied by using a vector control strategy. Due to the fractional-order characteristic of the motor, a fuzzy logic algorithm is used to realize the parameter self-tuning of the fractional-order proportional integral differential (PID) controller. The controller is selected as the position regulator of the servo motor. It combines the precision of fractional-order PID controller with the adaptability of fuzzy control and adds feed-forward to improve the response speed. The path tracking experiments on several different paths are carried out, and the results show that the control method is effective and can meet the trajectory tracking requirements of servo control. Finally, the speed and position tracking test of the PMSM AC servo system is carried out on the test verification platform, which verifies the effectiveness of the control algorithm.

In this paper, a novel modified auto disturbance rejection control (ADRC) design of a permanent magnet synchronous motor based on the improved memetic algorithm (IMA) is proposed. Firstly, there is an obvious system ripple caused by the defect that the optimal control function used in traditional ADRC cannot be differentiable and smooth at the segment point; aiming at weakening the system ripple effectively, the proposed method constructs a novel differentiable and smooth optimal control function to modify the ADRC design. Furthermore, aiming at improving the integration parameters optimization effect effectively, a novel improved memetic algorithm is proposed for obtaining the optimal parameters of ADRC. Specifically, an IMA with high-quality balance based on an adaptive nonlinear decreasing strategy for the convergence factor, Gaussian mutation mechanism, improved learning mechanism with the high-quality balance between competitive and opposition-based learning (OBL) and an elite set maintenance mechanism based on fusion distance is proposed so that these strategies can improve the optimization precision by a large margin. Finally, the experiment results of the PMSM speed control practical cases show that the ADRC based on IMA has an apparent better optimization effect than that of fuzzy PI, traditional ADRC based on the genetic algorithm and an improved ADRC based on improved moth–flame optimization.

This paper investigates the fault-tolerant tracking control problem of high-speed trains (HSTs) subject to unknown model parameters with unavailable uncertainties, unmeasurable additional disturbance, and unpredictable actuator faults constrained by actuator saturation. An adaptive passive fault-tolerant tracking control strategy based on variable-gain proportion-integral-derivative (PID)-type sliding mode surface is proposed to handle the problem. Unknown model parameters, gains of the PID-type sliding mode surface, and upper bounds of the lumped system uncertainty which includes additional disturbance, modeling uncertainties, and uncertainties resulting from actuator faults, are estimated online by adaptive technology. The input saturation (actuator output saturation) constraint is handled by introducing an auxiliary signal. The proposed controller can compensate for the effects of the lumped uncertainty and the actuator faults effectively. Moreover, the controller is model-independent, which means it requires no prior knowledge of model parameters and upper bounds of the lumped uncertainty, and does not depend upon fault detection and diagnosis module. The asymptotic stability of the closed-loop train system is demonstrated by Lyapunov theory. Good fault-tolerant tracking capacity, effective anti-actuator saturation ability, and strong robustness of the proposed controller are verified via numerical simulation.

Due to the huge volume of gearless ball mill direct drive permanent magnet synchronous machine (PMSM), an unwinding modular technology is convenient for its redundant power control, manufacture and maintenance. Several module units are wrapped around the ball mill roller for direct drive. The motor frame adopts a modular structure to achieve complete decoupling between modules. And the cooling problem of the motor is very important for its stable operation. The traditional waterway structure design and cooling analysis methods are difficult to meet the engineering requirements of large modular permanent magnet synchronous machine (MPMSM). A 3D equivalent thermal network method is proposed to study the cooling problem of MPMSM. The design program of modular frame cooling structure is written based on a 3D equivalent thermal network method. According to the temperature distribution of each node, the cyclic check is carried out to quickly design the modular waterway. The cooling effect of the designed waterway structure is analyzed by fluid-solid coupled temperature field simulation. An 80 kW prototype is developed and the temperature rise experiment is carried out. The simulation and experiment results prove the rationality of a 3D equivalent thermal network method for the MPMSM cooling structure design.

This work discusses the electric vehicle (EV) ordered charging planning (OCP) optimization problem. To address this issue, an improved dual-population genetic moth–flame optimization (IDPGMFO) is proposed. Specifically, to obtain an appreciative solution of EV OCP, the design for a dual-population genetic mechanism integrated into moth–flame optimization is provided. To enhance the global optimization performance, the adaptive nonlinear decreasing strategies with selection, crossover and mutation probability, as well as the weight coefficient, are also designed. Additionally, opposition-based learning (OBL) is also introduced simultaneously. The simulation results show that the proposed improvement strategies can effectively improve the global optimization performance. Obviously, more ideal optimization solution of the EV OCP optimization problem can be obtained by using IDPGMFO.