Aiming at the application occasions that require high dynamic performance of the drive motor speed controller, this paper proposes an adaptive sliding mode control (ASMC) with an extended state observer (ESO) to enhance the speed control performance of the permanent magnet synchronous motor (PMSM) against internal and external disturbances. Firstly, a new adaptive reaching law (ARL) is proposed, which can suppress the chattering effectively and reduce the convergence time of the system state. Based on ARL, the proposed ASMC for speed control of PMSM drives is developed, combined with an integral-type terminal sliding mode surface. Furthermore, by introducing the nonlinear term into the sliding mode control scheme, the convergence velocity to the equilibrium is improved and the finite-time convergence is guaranteed. Secondly, an ESO is developed to estimate the lumped disturbance for feedforward compensation and update the control algorithm of ASMC. The proposed ASMC+ESO control method enhances robustness against disturbances while maintaining fast dynamic response and high tracking accuracy. Finally, the proposed controller is implemented on a 0.4 kW PMSM test platform and the results confirm the validity.

Traditional stator iron loss models lack of precision when they are used for loss optimization of permanent-magnet synchronous motor (PMSM), because non-uniform distribution and non-sinusoidal waveform of the flux density in stator core is not fully considered. In this paper, a new model is developed with a higher precision, in which the non-uniform flux density in stator core is observed through two virtual coils wounded on the yoke and tooth of a finite-element model. Then the stator iron loss is calculated depending on induced voltages of the coils. Moreover, non-sinusoidal waveform of the flux density is considered in the development. Based on this model, a loss-optimization method is proposed, in which the optimal current reference is determined by minimizing the stator loss under a constraint of ripple-free torque. Limitations of DC voltage supply and current amplitude are also considered to improve robustness of the method. Finally, precision of the iron loss model and performance of the loss-optimization method are validated experimentally on a 3.8 kW PMSM setup.

The main challenges for Finite Set-model Predictive Torque and Flux Control (MPTC) include huge calculation amount, large torque ripple, and the selection of weighting factor. To deal with these challenges and achieve the fastest torque response, a novel MPTC strategy with virtual voltage vector-based pattern is proposed in this paper. In each control period, a desired voltage vector is computed based on the maximum torque variation rate. The virtual desired voltage vector is combined by using basic voltage vectors with the minimum switching times. The switching pattern between two adjacent control periods is optimized by using a cost function in realtime. Compared with the traditional MPTC approach, the novel strategy has multifaceted improved performances: faster torque response, less calculation amount, lower switching frequency, reduced torque ripple, higher efficiency and no weighting factor. The proposed method is contrasted with traditional MPTC method and a well-known generalized multiple-vector-based MPC method via experiments.

Conventional ultralocal model-based model-free predictive current control using an extended state observer (ESO) based on an ultralocal model significantly promotes the robustness against motor parameter mismatch. However, a large bandwidth of ESO leads to an enhanced disturbance rejection capability, but the noise suppression performance will deteriorate. To address this issue, an adaptive extended state observer (AESO) with an adaptive bandwidth tuning scheme based on the estimated error was proposed, in which the observer bandwidth is automatically adjusted to reduce the estimation errors of system states and total uncertainty against the measurement noise. Accordingly, the different requirements of observer bandwidth under various operating conditions can be satisfied, and the performance of disturbance rejection and noise suppression is balanced. Moreover, the stability of the proposed AESO is proved based on the Lyapunov method. Furthermore, to improve the dynamic performance and robustness of the speed loop, a robust sliding mode speed controller based on a novel reaching law and a sliding mode observer is proposed to replace the conventional proportional-integral. The comparative experiments on a 1.9 kW surface-mounted permanent magnet synchronous motor are conducted to validate the superiority of the proposed control methods compared with traditional methods.

Due to high power density and excellent efficiency, permanent-magnet synchronous motors (PMSMs) provide extremely competitive technology for the driving applications of transportation electrification. Among the various advanced control strategies for PMSMs, deadbeat predictive current control (DPCC) has the advantages of fast dynamic response and remarkable steady-state current tracking performance. However, DPCC is heavily dependent on the accuracy of motor parameters (resistance, inductance, and flux linkage). To alleviate the effect of parameter mismatch and maintain the advantages of conventional DPCC, this article proposes a sliding mode prediction error compensation (SM-PEC) method of DPCC. First, a composite reaching law (CRL) is proposed to accelerate the convergence while suppressing the chattering. Then, an incremental prediction model of PMSM is developed, and the parameter sensitivity is analyzed. Combined with this model, a CRL-based SM-PEC method is proposed to eliminate the current tracking error, enhance the robustness, maintain the excellent dynamic performance of DPCC, and avoid dependence on observers. Moreover, a CRL-based sliding mode speed controller (SMSC) is designed to improve the performance of the speed regulation system. Finally, comprehensive experiments are implemented on a 1.6-kW PMSM experimental platform, and the results verify the effectiveness of the proposed control methods.

The deadbeat predictive current control (DPCC) method for surface mounted magnet synchronous motor predicts the current by an accurate model, which suffers from parameter mismatch and harmonic disturbance. To enhance the dynamic tracking and steady-state predictive precision of DPCC under disturbance, a robust incremental model-based DPCC with a hybrid compensation method (HC-IDPCC) is proposed. First, the IDPCC dynamic model considering disturbances is derived, and the parameter sensitivity is analyzed. Then, a harmonic suppression extended state observer (HSESO) is designed to enhance the harmonic suppression ability and parameter robustness. In addition, an improved prediction error correction method (IPEC) is designed to enhance the dynamic performance, which utilizes the current and cumulative predictive errors to compensate for the disturbances. An improved switching method with a sliding window is designed to identify the dynamic and steady-state of the $q$-axis current and maintain a smooth switch between IPEC and HSESO compensation methods. HC-IDPCC integrates the advantages of IPEC and HSESO, which improves the parameter robustness and harmonic suppression ability while maintaining superior dynamic and steady-state performance. Finally, the effectiveness of the HC-IDPCC is verified by steady-state, dynamic, and switching method experiments.