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The permanent magnet synchronous motor (PMSM) is a highly efficient energy saving machine. Due to its simple structural characteristics, good heat radiation capability, and high efficiency, PMSMs are gradually replacing AC induction motors in many industrial applications. The PMSM has a nonlinear system and lies on parameters that differ over time with complex high-class dynamics. To achieve the excessive performance operation of a PMSM, it essentially needs a speed controller for providing accurate speed tracking, slight overshoot, and robust disturbance repulsion. Therefore, this article provides an overview of different robust control techniques for PMSMs and reviews the implementation of a speed controller. In view of the uncertainty factors, such as parameter perturbation and load disturbance, the H∞ robust control strategy is mainly reviewed based on the traditional control techniques, i.e., robust H∞ sliding mode controller (SMC), and H∞ robust current controller based on Hamilton–Jacobi Inequality (HJI) theory. Based on comparative analysis, this review simplifies the development trend of different control technologies used for a PMSM speed regulation system.

Enhancing the efficiency of the electric vehicle’s powertrain becomes a crucial focus, wherein the control system for the traction motor plays a significant role. This paper presents a novel electric vehicle traction motor control system based on a robust predictive direct torque control approach, an improved version of the conventional DTC, where the traditional switching table and the hysteresis regulators are substituted with a predictive block based on an optimization algorithm. Additionally, a robust predictive speed loop regulator is employed instead of the proportional-integral regulator, which integrates a new cost function with a finite horizon, incorporating integral action into the control law based on a Taylor series expansion. This technique’s primary benefit is its independence from the necessity to measure and observe external disturbances, as well as uncertainties related to parameters. The effectiveness of the suggested system was confirmed through simulation and experimental results under the OPAL-RT platform. The findings indicate that the proposed approach outperforms the conventional method in terms of rejecting disturbances, exhibiting robustness to variations in parameters, and minimizing torque ripple.

In this paper, we study a mixed traffic system moving along a single-lane open-road. This platoon includes a number of human-driven vehicles (HDVs) together with one connected and automated vehicle (CAV). The dynamics of HDVs are assumed to follow the optimal velocity model (OVM), and the acceleration of the single CAV is directly controlled by a static output-feedback controller. Due to different traffic conditions, the desired velocity of the platoon can change over time. Moreover, there are multiple system parameters that are uncertain. The ultimate goal of this work is to present a gain-scheduled robust control strategy that, with a varying desired speed, smooths the traffic flow in the presence of undesired disturbances and parametric uncertainties. In this direction, a gain-scheduled H∞ static output-feedback controller is designed, and its efficiency is illustrated through numerical simulations.

This paper presents the design of a robust speed controller for brushless DC motors (BLDCMs) under field-oriented control (FOC). The proposed robust controller integrates extension theory (ET) and sliding mode theory (SMT) to achieve robustness. First, the speed difference between the speed command and the actual speed of the BLDCM, along with the rate of change of the speed difference, are divided into 20 interval categories. Then, the feedback speed difference and the rate of change of the speed difference are calculated for their extension correlation with each of the 20 interval categories. The interval category with the highest correlation is used to determine the appropriate control gain for the sliding mode speed controller. This gain adjustment tunes the parameters of the sliding surface in the SMT, thereby suppressing the overshoot of the motor’s speed. Because a sliding surface reaching law of the sliding mode controller (SMC) adopts the exponential approach law (EAL), the system’s speed response can quickly follow the speed command in any state and exhibit an excellent load regulation response. The simplicity of this robust control method, which requires minimal training data, facilitates its easy implementation. Finally, the speed control of the BLDCM is simulated using Matlab/Simulink software (2023b version), and the results are compared with those of the SMC using the constant-speed approach law (CSAL). The simulation and experimental results demonstrate that the proposed robust controller exhibits superior speed command tracking and load regulation responses compared to the traditional SMC.

This paper presents the issue of the Sub-synchronous resonance (SSR) phenomenon in a series compensated DFIG-based wind power plant and its alleviation using a Battery Energy Storage-based Damping Controller (BESSDC L ). A supplementary damping signal is developed considering the angular speed deviation and is incorporated into the BESS control system. Wide-area Measurement System data is used to determine the angular speed deviation. A linearized system model is developed to perform eigenvalue analysis, and to detect and examine unstable SSR modes. The variation of wind speed and three-phase fault are also taken into consideration to validate the robustness of the controller. To further verify the efficacy of the proposed damping controller, time-domain simulations are performed using MATLAB/Simulink. The application of the proposed BESSDC L stabilizes all the unstable system modes effectively at wind speeds of 7 m/s, 9 m/s, and 11 m/s, and at 40%, 50%, and 60% series compensation levels, as well three-phase fault conditions.

This paper introduces a novel sensorless speed control strategy for squirrel-cage induction motors, which ensures robust operation in the presence of external disturbances by applying the state feedback technique. Based on the induction motor model, the speed controller is synthesized by defining a sliding variable that is driven to zero through the supertwisting control law, ensuring the stabilization of the tracking error. The time derivative of the error variable is estimated using a robust differentiator based on the sliding-mode twisting algorithm, thereby eliminating the need to estimate the load torque. A robust observer is employed to estimate the rotor speed and flux linkages simultaneously. The convergence of the estimated rotor flux linkages is enforced through a discontinuous first-order sliding-mode input, while the convergence of the rotor speed estimate is attained via a quasi-continuous super-twisting sliding-mode input. In the proposed model, the inductance parameters are determined from the magnetizing inductance and the leakage inductances of the stator and rotor. A procedure is also presented for adjusting the stator resistance and leakage inductances, taking into account the squirrel-cage rotor type and the skin effect in alternating current conduction. The performance of the sensorless speed control system under variations in load torque and reference speed is validated through experimental testing. The rotor speed estimation provided by the robust observer is accurate. The reference speed tracking control, evaluated using a 1600–1700 rpm pulse train phase-shifted by 4 s with respect to a 0–0.5 N·m pulse train, demonstrates high precision.

To enhance the responsiveness and resilience against disturbances in the speed regulation system of Permanent Magnet Linear Synchronous Motors (PMLSM), an advanced control approach leveraging optimized sliding mode control is introduced. Initially, a foundation is laid by designing a PI-based speed control system and a conventional sliding mode control system within the framework of PMLSM vector control. Simulation models are then constructed to validate the efficacy of these systems. Subsequently, focusing on the traditional sliding mode controller, optimizations are implemented to refine the sliding mode convergence dynamics and the switching function, leading to the development of an optimal sliding mode controller model. Ultimately, simulation tests confirm that this optimized sliding mode speed controller achieves seamless transition without overshoot, swift response times, and robust disturbance rejection capabilities. Its performance in terms of responsiveness and anti-interference surpasses both the PI controller and the conventional sliding mode speed controller.

In this paper, an extendable fuzzy robust speed controller suitable for induction motor drive systems was proposed. Firstly, the two-degrees-of-freedom (2DOF) robust control technology with feedforward control and disturbance elimination method was adopted. Upon parameter variation and load disturbance, the motor drive system could utilize a robust controller to generate compensation signals and reduce the impact on the controlling performance of the motor drive system. The magnitude of the compensation signal was adjusted via the weighting factor. However, should a fixed weighting factor be adopted, system instability might be generated easily when time delay and saturation of control force occur. Based on the above, the smart method of extendable fuzzy theory (EFT) was adopted in this paper to adjust adequate weighting factors, where the controlling performance of the induction motor drive system could be improved accordingly. Lastly, the simulation software Matlab/Simulink (R2023b version) was applied to simulate the utilization of the controlling method proposed for the induction motor drive system. The simulation results proved that the extendable fuzzy robust speed controller proposed provided better speed tracking and load regulation-controlling performance than the conventional robust controller.

A robust and improved control scheme of a variable speed multi-rotor wind turbine (MRWT) system with a doubly fed asynchronous generator (DFAG) is displayed in this work. In order to improve the performances and effectiveness of the traditional direct power control (DPC) strategy of the DFAG, a new kind of sliding mode controller (SMC) called modified SMC (MSMC) is proposed. The most important advantage of the DPC-MSMC strategy is to reduce the power ripples and improve the quality of the currents provided to the grid. In addition, to control the rotor inverter, a pulse width modulation (PWM) technique is used. The proposed DPC-MSMC strategy was modeled and simulated using MATLAB/Simulink software. The simulation results showed that the ripples in stator currents, active and reactive powers and torque were considerably reduced for the proposed DPC-MSMC strategy compared to the traditional DPC. Additionally, the proposed DPC-MSMC method works excellently to reduce the total harmonic distortion (THD) of the stator current in the case of variable wind speed. On the other hand, a robustness test against parametric variations showed and confirmed the robustness of the proposed technique compared to the classical method.

High-speed trains are very sensitive due to their very high speed of movement, so the slightest defect or fault is not acceptable. This sensitivity necessitates the development of advanced and robust control strategies capable of handling dynamic uncertainties, nonlinearities, and external disturbances commonly present in high-speed rail systems. To achieve this, the control system must be very precise and eliminate the smallest errors. In this paper, a very precise nonlinear controller is designed by combining the TSK type-2 fuzzy system with the sliding mode control (SMC) method. The integration leverages the robustness of sliding mode control and the superior uncertainty modeling capability of TSK Type-2 fuzzy logic, aiming to overcome the limitations of conventional SMC and Type-1 fuzzy approaches. The TSK Type-2 fuzzy system can estimate sliding surfaces well and the control system will be very fast and accurate. In the simulation section, an attempt has been made to apply the parameters of a real train in order to evaluate it more accurately with the proposed control system. The results show the high efficiency of the proposed control system so that the RMSE of the control system reaches less than 1%. Compared to other existing control methods, the proposed controller demonstrates significant improvements in tracking accuracy, vibration reduction, and control effort minimization. Theoretical analysis based on Lyapunov stability further confirms the stability and reliability of the closed-loop system.