A traditional linear extended state observer (LESO) does not have enough capacity to handle the rapidly-varying back electromotive force (EMF). This key weakness limits the application of LESO in the internal permanent magnet synchronous motor (IPMSM) sensorless control system. In this article, an IPMSM sensorless drive method using an improved LESO (ILESO) is proposed. The ILESO consists of a pure integral and rapidly-varying sinusoidal interference estimator. The rapidly-varying sinusoidal interference estimator is added to the unknown interference estimation loop to estimate rapidly-varying back EMF. And the frequency of the rapidly-varying sinusoidal interference estimator changes adaptively with the operating frequency of the motor to guarantee the desired estimation accuracy of back EMF in a wide-speed range. This design makes it possible to observe the back EMF in a relatively low bandwidth, thereby solving the tradeoff between LESO bandwidth and high-frequency noise filtering. Then, an enhanced phase-locked loop is presented to improve the ability of tracking the rapidly-changing speed. The effectiveness of the proposed method is verified at a 2.0-kW IPMSM sensorless drive.

To heighten the position estimation accuracy of sensorless control of interior permanent magnet synchronous motor (IPMSM), an improved full-order sliding mode observer (FSMO) based on novel phase-locked loop (PLL) is proposed in this article. By contrast with the traditional FSMO, the improved FSMO (IFSMO) with piecewise power function has merits of higher estimation accuracy and more chattering suppress capability, which is beneficial to the position sensorless drives. Based on that, to pledge the rotor position observation precision of the IFSMO during wide speed range, a variable sliding mode gain related to the rotor speed is designed. However, due to the inverter nonlinearity and rotor spatial harmonics, the extended back electromotive force contains high-order harmonics, which will reduce the estimation accuracy of PLL and cause notable position error. Therefore, a novel PLL with complex bandpass filter as the preprocessing stage that features high estimation accuracy, high filtering capability, and fast dynamic response is proposed. Meanwhile, the linearized model and parameter design of the proposed PLL is developed, and hence the stability of the designed PLL is analyzed by the derived linearized model. The availability of the proposed sensorless strategy is verified via experiments on 2-kW IPMSM drive.

An active disturbance rejection control (ADRC) of induction motor based on adaptive particle swarm optimization (APSO) algorithm is proposed in this paper, in order to realize the precise decoupling of induction motor and the disturbance compensation. The novel control method employs APSO as the automatic tune mechanism for ADRC controller. According to the feedback information of induction motor, an optimal solution can be achieved via the optimization mechanism and self-learning ability of APSO, so the reliance of ADRC controller on parameters can be reduced. In order to make the optimal solution more reasonable and reliable, the aggregation degree and evolution speed are introduced into the APSO to dynamically modify the inertia weight based on the practical optimization process. Simulation and experimental results indicate that the robustness of the proposed optimal design method for ADRC is better than the conventional ADRC when the disturbances occur, and the method is feasible and effective.

In this paper, to improve the robustness and realize the precision positioning as well as speed control of the servo drive system, an integrated sliding-mode control (ISMC) optimized by differential evolution (DE-ISMC) algorithm is proposed. ISMC guarantees that a motor can reach at given position with given speed, and given speed is set first. ISMC, which is combined with speed regulator and position regulator, is different from the conventional sliding-mode control (SMC) replacing speed regulator. ISMC can guarantee that motor speed is controlled under position control mode, which is a novelty and an advantage of ISMC in this paper. To achieve good control performance of ISMC, parameters of it should be chosen exactly, so DE algorithm is selected to optimize them. The most significant influence factor of DE algorithm is the optimization iteration. Once parameters of ISMC are optimized under convergent iteration, servo system performance reaches given indices in the shortest time. Experimental results indicate that robustness of the servo system is improved; correctness and effectiveness of DE-ISMC are verified.

Conventional model predictive torque control (MPTC) for induction motor (IM) drives evaluates the electromagnetic torque and stator flux linkage in the cost function. However, in the speed outer loop design, the torque reference in the cost function generally utilizes a classic proportional-integral (PI) controller, which is not only dependent on motor parameters, but also sensitive to the change of load torque. In this paper, an adaptive sliding mode control based model predictive torque control (ASMC-MPTC) method is proposed to improve the robustness of finite control set model predictive control (FCS-MPC). Firstly, the influence of the mismatched parameters for FCS-MPTC is introduced and the shortcoming of the PI based MPTC (PI-MPTC) is analyzed. Then a sliding mode control based MPTC (SMC-MPTC) is studied, in which the exponential reaching law is introduced to control the trajectories of the state variables. To further improve the control performance of SMC-MPTC, the exponential reaching law is optimized to adjust the switching gain adaptively, thus an adaptive sliding mode control based MPTC (ASMC-MPTC) is proposed. The PI-MPTC, SMC-MPTC and ASMC-MPTC are compared theoretically and experimentally, and the results show that the proposed ASMC-MPTC has the best disturbance rejection ability and robustness against motor parameters variation and load torque change.

A novel sliding mode observer (SMO) with optimized equal reaching law for speed estimation in sensorless induction motor drives is proposed in this paper to balance the dilemma between the requirement of fast reaching transient and the chattering reduction on sliding mode surface. By considering the reaching way to the sliding mode surface, a widely-used reaching law called equal reaching law (ERL) is introduced into SMO. However, SMO with ERL can not satisfy the rapidity of transient performance and the low chattering level simultaneously. To solve this problem, an optimized equal reaching law (OERL), which is based on the options of an exponential term that adapts to the variations of sliding mode surface and system states, is introduced into SMO. The proposed SMO with OERL is able to decrease the reaching time and suppress the chattering problem simultaneously. Simulation and experimental results both show the validity of the proposed approach.