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.

An autodisturbance-rejection control (ADRC) of an induction motor based on an ant colony optimization (ACO) algorithm is proposed in this paper, in order to realize the precise decoupling of the induction motor and the disturbance compensation. A novel control method employs ACO as an automatic tune mechanism for an ADRC controller. According to the feedback information from the induction motor, an optimal solution can be achieved via the optimization mechanism and self-learning ability of ACO after the iterative calculation; therefore, the reliance of the ADRC controller on parameters can be reduced. The simulation and experimental results indicate that the robustness of the proposed optimal design method for ADRC is better than that of the conventional ADRC when the disturbances occur and that the method is feasible and effective.

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.

This paper presents a 12/8-pole, three-phase, modular-stator hybrid excitation switched reluctance machine (MSHSRM) assisted with permanent magnets (PMs) as well as its design consideration and performance evaluation. The basic operating principle is illustrated through a specific form of this machine, and the magnetic equivalent circuit model is built and analyzed. Then, the main parameters of the machine are optimized by the finite-element method. The static magnetic characteristics and dynamic performance of this novel machine are simulated and compared with other two SRMs, one is segmented 12/8 SRM without PM, and the other is the conventional 12/8 SRM with unsegmented structure. Finally, two motor prototypes, including one MSHSRM and one conventional SRM of the same size are manufactured. The static magnetic characteristic and dynamic operation of two motor drives are measured to confirm the predictions. The simulation and experimental results indicate that this MSHSRM has higher power and stronger load capacities than the conventional SRM in whole speed range with higher torque/power density and higher torque per ampere.

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.