Xiangfei Li

To solve the problem of PI control cannot achieve satisfactory dynamic performance under complex operating conditions, an improved model-free continuous super-twisting non-singular fast terminal sliding mode control (NFTSM) algorithm (IMFSTNFTSMC) for IPMSM based on improved extended higher-order sliding mode disturbance observer (IESMDO) is proposed. Firstly, the speed-current hyperlocal models of IPMSM are developed; Then, the speed-current controllers for IMFSTNFTSMC are designed to optimize the current-speed dual closed-loop; where the jitter of system is effectively attenuated by combining third-order super-twisting with NFTSM; At the same time, IESMDO feeds back the disturbance of the speed and current loops to the controllers, it can effectively improve the performance for system control, and achieve fast-tracking of speed and d-q axis current without overshoot; Finally, the algorithm is experimentally compared with PI and traditional MFNFTSMC to verify that the algorithm effectively improves the dynamic steady-state response and robustness of IPMSM.

To solve the problems such as poor anti-interference performance and more steady-state current harmonic components of a permanent magnet synchronous motor(PMSM) drive system based on traditional PI control, this paper presents a model predictive control method based on sliding-mode-controller (SMC-MPC). Firstly, a sliding mode speed controller based on exponential reaching law is designed to replace the traditional PI speed controller, and to reduce the influence of the load disturbance on system performance. Secondly, the model predictive current control is adopted based on the discrete model of PMSM to reduce current harmonics and improve the torque control accuracy. Then the cost function is designed by controlling the d- and q-axis current, and the optimal output voltage vector is obtained to drive the motor. Finally, compared with the traditional PI method, the simulation results show that the proposed method not only improves the robustness of the system to load disturbance effectively, but also decreases the ripples of current and torque. The presented method has good dynamic and static performance for PMSM drive system.

An optimal utilization of adhesion force based on extremum seeking with sliding mode (SMES) and asymmetric barrier Lyapunov function (ABLF) is proposed for heavy-haul electric locomotives (HHELs), which can eliminate the wheel skidding at optimal adhesion point and achieves maximum traction for HHELs. First, the state equation of wheel-rail adhesion control system is described. The optimal utilization of adhesion force and anti-slip control are analyzed considering the condition changes at the wheel-rail surface. Then, the nonsingular terminal sliding mode observer (NTSMO) is designed to achieve the accurate adhesion coefficient of the wheel-rail. Finally, the SMES method for HHEL is developed to obtain the optimal slip speed and the maximum adhesion coefficient of the uncertain wheel-rail surface. Meanwhile, the ABLF controller is designed to achieve anti-slip control for HHELs in the optimal adhesion state. Comparing with the conventional differential acceleration control (DAC) method, the simulations and experiments verify that the proposed method can achieve optimal adhesion anti-slip control with quick dynamic response, and the HHEL achieves maximum traction.

This paper proposes a new scheme of reconstructing current sensor faults and estimating unknown load disturbance for a permanent magnet synchronous motor (PMSM)-driven system. First, the original PMSM system is transformed into two subsystems; the first subsystem has unknown system load disturbances, which are unrelated to sensor faults, and the second subsystem has sensor faults, but is free from unknown load disturbances. Introducing a new state variable, the augmented subsystem that has sensor faults can be transformed into having actuator faults. Second, two sliding mode observers (SMOs) are designed: the unknown load disturbance is estimated by the first SMO in the subsystem, which has unknown load disturbance, and the sensor faults can be reconstructed using the second SMO in the augmented subsystem, which has sensor faults. The gains of the proposed SMOs and their stability analysis are developed via the solution of linear matrix inequality (LMI). Finally, the effectiveness of the proposed scheme was verified by simulations and experiments. The results demonstrate that the proposed scheme can reconstruct current sensor faults and estimate unknown load disturbance for the PMSM-driven system.