Sliding Mode Observer Research Articles

Sensorless speed control of PMSM drive using nonlinear sliding mode observer with position estimation based on quadrature PLL

Sensorless speed control of PMSM drive using nonlinear sliding mode observer with position estimation based on quadrature PLL

Adaptive super-twisting sliding mode observer based positionless direct torque control strategy for PMSM

ABSTRACT A novel rotor position estimation technique utilising the super-twisting sliding mode observer (STSMO) was introduced to address diminishing reliability in position sensors of permanent magnet synchronous motors (PMSMs). A cutting-edge direct torque control (DTC) strategy for PMSM functioning without reliance on a position sensor was proposed to address the challenges of increased chattering and limited perturbation resistance resulting from fixed sliding mode parameters in standard STSMOs. This strategy employed an adaptive super-twisting sliding mode observer (ASTSMO). This method enhanced the system’s response speed and estimation accuracy by facilitating online adjustment of sliding mode coefficients. A rigorous stability analysis of the observer was presented. A hyperbolic tangent function of the segmented square root was introduced as a substitute for the conventional sign function to mitigate chattering and eliminate the need for low-pass filters and phase compensation. This approach simplified the system’s complexity while improving angle estimation accuracy, response speed, and observer stability. Additionally, a phase-locked loop (PLL) was used to estimate the rotor angle, which improved the accuracy of the rotor angle estimation. Comprehensive simulations and experimental validations demonstrated that the proposed ASTSMO could suppress system’s chattering under varying operational conditions, exhibiting superior estimation accuracy and stability compared to the traditional STSMO.

Sensorless control strategy of stepper motor based on super-twisting sliding mode observer

Sensorless control strategy of stepper motor based on super-twisting sliding mode observer

Sensorless Control Based on Discrete Fractional-Order Terminal Sliding Mode Observer for High-Speed PMSM With LCL Filter

Sensorless Control Based on Discrete Fractional-Order Terminal Sliding Mode Observer for High-Speed PMSM With LCL Filter

Adaptive dual control law sliding mode observer to reconstruct cyber attack and compensate power in cyber physical power system

Adaptive dual control law sliding mode observer to reconstruct cyber attack and compensate power in cyber physical power system

Distributed synchronous control of multiple electrohydraulic actuators with lumped uncertainty and communication delay

This study presents a distributed synchronous control in multiple electrohydraulic actuators (MEHAs), which ensures the motion consensus between the leader and the follower under a directed topology. Since the MEHAs system has many unknown uncertainties and communication delays, a terminal sliding mode observer (TSMO) is used to address hydraulic parametric uncertainties and load disturbance. Furthermore, one synchronized controller is designed by Lyapunov-Krasovskii method with backstepping iteration to ensure the synchronized errors of the MEHAs system converging to a zero neighborhood. Since some follower EHAs cannot directly acquire the position signal from the leader node, a demand estimation law is used to degrade the negative effect of unknown communication delay existed in the network topology. Finally, the effectiveness of the proposed synchronized controller is verified by a MEHAs platform with finite hydraulic nodes under constant and random communication delays.

An Improved SOGI-Higher-Order Sliding Mode Observer-Based Induction Motor Speed Estimation

Abstract This article presents a novel adaptive gain tuning second-order generalised integrator (SOGI)-higher-order sliding mode (HOSM) observer for robust speed estimation for an induction motor’s entire speed range. This article introduces a hyperbolic tangent function and a varying gain exponent that ensures accurate speed estimation under noisy conditions and significantly reduces chattering observed in conventional sliding mode observers (SMOs). The robustness of the proposed speed estimation method is verified through simulations conducted on MATLAB/Simulink R2024a developed by MathWorks, demonstrating its capability to effectively track the motor’s actual speed even under varying load torque conditions, parameter variations and additional sensor noise. The proposed approach’s superiority and robustness were compared with the conventional SOGI-frequency locked loop (FLL) and super twisting algorithm (STA) SMO.

Development of an Extended State Observer for Monitoring of a PWR Based on a Two-Point Kinetic Model

Accurate estimation of unmeasured system states and disturbances in a pressurized water reactor (PWR) is essential for effective control, operation optimization, and safety monitoring. To this end, this paper investigates the estimation of unmeasured system states and disturbances of the PWR system during load-following operation. First, a mathematical model for the PWR system is established based on the two-point kinetics equations with one equivalent delayed neutron precursor group. Subsequently, an extended state observer (ESO) integration scheme, incorporating two coupled ESOs, is constructed to estimate unmeasured system states, including relative density of delayed neutron precursor, average fuel temperature, total reactivity, xenon concentration, and iodine concentration, along with time-varying disturbances, with the use of measurements of the PWR system only. According to the Lyapunov stability theorem, it is proved that the estimation error dynamic of the proposed ESO integration scheme is uniformly ultimately bounded stable. Finally, simulation results confirm that the proposed ESO integration scheme provides higher estimation accuracy and stronger robustness against measurement noises, model uncertainties, and external disturbances compared to both a high-gain observer and a high-order sliding mode observer.

Adaptive Disturbance Rejection Motion Control of Direct-Drive Systems with Adjustable Damping Ratio Based on Zeta-Backstepping.

Direct-drive servo systems are extensively applied in biomimetic robotics and other bionic applications, but their performance is susceptible to uncertainties and disturbances. This paper proposes an adaptive disturbance rejection Zeta-backstepping control scheme with adjustable damping ratios to enhance system robustness and precision. An iron-core permanent magnet linear synchronous motor (PMLSM) was employed as the experimental platform for the development of a dynamic model that incorporates compensation for friction and cogging forces. To address model parameter uncertainties, an indirect parameter adaptation strategy based on a recursive least squares algorithm was introduced. It updates parameters based on the system state instead of output error, ensuring robust parameter convergence. An integral sliding mode observer (ISMO) was constructed to estimate and compensate for residual uncertainties, achieving finite-time state estimation. The proposed Zeta-backstepping controller enables adjustable damping ratios through parameterized control laws, offering flexibility in achieving desired dynamic performance. System stability and bounded tracking performance were validated via a second-order Lyapunov function analysis. Experimental results on a real PMLSM platform demonstrated that, while achieving adjustable damping ratio dynamic characteristics, there is a significant improvement in tracking accuracy and disturbance suppression. This underscores the scheme's potential for advancing precision control in biomimetic robotics and other direct-drive system applications.

Iterative Fast Super-Twisting Flux Sliding Mode Observer for SPMSM with Tangent Quadrature Phase-Locked Loop

Traditional low-order flux sliding mode observer (FSMO) and quadrature phase-locked loop (QPLL) structures generally encounter issues such as estimated signal chattering and inadequate dynamic performance. To overcome these challenges, this paper proposes an iterative fast super-twisting flux sliding mode observer (IFST-FSMO) and a tangent quadrature phase-locked loop (TQPLL) for sensorless control of surface-mounted permanent magnet synchronous motors (SPMSMs). Building on the traditional super-twisting algorithm (STA), the IFST-FSMO is proposed to accelerate convergence and enhance chattering suppression, which incorporates a linear term and utilizes the hyperbolic tangent function to replace the intrinsic sign function. Notably, the feedback matrix is redesigned to ensure the algorithm’s stability during speed reversal. Furthermore, an iterative calculation strategy is implemented under low-speed and light-load conditions, improving steady-state accuracy of estimated flux while avoiding increased computational burden at medium and high speeds. Regarding position estimation, a novel TQPLL with correction factor is proposed, utilizing the tangent function of the electrical angle error to achieve normalization and bandwidth adaptation. Ultimately, the proposed method is implemented on a motor test platform. Comparative experimental results demonstrate that the IFST-FSMO combined with TQPLL exhibits superior dynamic response and steady-state accuracy, while achieving efficient speed reversal.

Joint Fault Diagnosis of IGBT and Current Sensor in LLC Resonant Converter Module Based on Reduced Order Interval Sliding Mode Observer.

LLC resonant converters have emerged as essential components in DC charging station modules, thanks to their outstanding performance attributes such as high power density, efficiency, and compact size. The stability of these converters is crucial for vehicle endurance and passenger experience, making reliability a top priority. However, malfunctions in the switching transistor or current sensor can hinder the converter's ability to maintain a resonant state and stable output voltage, leading to a notable reduction in system efficiency and output capability. This article proposes a fault diagnosis strategy for LLC resonant converters utilizing a reduced-order interval sliding mode observer. Initially, an augmented generalized system for the LLC resonant converter is developed to convert current sensor faults into generalized state vectors. Next, the application of matrix transformations plays a critical role in decoupling open-circuit faults from the inverter system's state and current sensor faults. To achieve accurate estimation of phase currents and detection of current sensor faults, a reduced-order interval sliding mode observer has been designed. Building upon the estimation results generated by this observer, a diagnostic algorithm featuring adaptive thresholds has been introduced. This innovative algorithm effectively differentiates between current sensor faults and open switch faults, enhancing fault detection accuracy. Furthermore, it is capable of localizing faulty power switches and estimating various types of current sensor faults, thereby providing valuable insights for maintenance and repair. The robustness and effectiveness of the proposed fault diagnosis algorithm have been validated through experimental results and comparisons with existing methods, confirming its practical applicability in real-world inverter systems.

Wide-speed-adaptive sliding mode observer for the fuel cell air compressor controller

Wide-speed-adaptive sliding mode observer for the fuel cell air compressor controller

Experimental validation of model-free predictive control based on the active vector execution time for grid-connected system

The application of Model-Free Predictive Control (MFPC) in power electronic systems has garnered increasing attention. In this paper, MFPC control based on replacing the classical factory model with an ultra-local model (ULM) is studied. Generally, the Integral Sliding Mode Observer (ISMO) is used to estimate the unknown part in the ULM where the non-physical factor in the ULM is selected with approximate values ​​ranging from the nominal value of the system which is contrary to the concept of MFPC control. In this research, an improved adaptive integral sliding mode observer (AISMO) based MFPC (AISMO-MFPC ) is proposed to estimate this factor with the unknown function in the ULM equation. The new observer design enables the estimation of this factor based on the current error, which allows for independent prposedcontrol of the system parameters.To obtain the lowest current ripple, the concept of active vector execution time (AVET ) has been incorporated into the proposed control where two vectors are selected in the sampling period to minimize the cost function instead of selecting a single vector. ULM is also used to calculate AVET which facilitates the implementation of the imposed control. The combination of the proposed AISMO-MFPC and AVET gives faster system response and reduces the current ripple and lower harmonics, especially in case of mismatch parameters. Finally, the effectiveness of the proposed control is confirmed under various conditions by the presented simulation and experimental results.

Detection of internal short circuit in lithium-ion batteries based on electrothermal coupling model

Internal short circuit (ISC) is the main cause of thermal runaway (TR) in lithium-ion batteries, and early detection of ISC is crucial to improve battery safety. This paper introduces a method for detecting ISC and classifying fault severity by analyzing the variations in voltage and surface temperature during battery operation. Firstly, the electrothermal coupling model (ETCM) of the battery was constructed, and the Pearson correlation coefficient (PCC) was used to identify the ISC of the battery through the real-time collected voltage and temperature sensor data When the battery was working. Then the battery model with ISC is updated by using equivalent ISC resistance. The battery status estimation is achieved by integrating the extended Kalman filter (EFK) and sliding mode observer (SMO). Finally, the fault grade is classified according to the state difference between the battery with ISC and normal battery. The batteries with different degrees of ISC were installed in battery packs and verified by the StarSim hardware-in-the-loop (HIL) experiment. The findings suggest that the proposed approach facilitates rapid identification of ISC in the battery pack and enables categorization of fault severity based on the state estimation outcomes derived from analyzing the battery with ISC.

Sliding Mode Speed Estimation of Induction Motor with MPC Based Flux Weakening Control for Electric Vehicle

It is very important to operate the motors used in electric vehicles above the rated speed. In order to operate electric motors with very high speed, the flux must be decreased in a controlled way. The Model Predictive Control (MPC) approach was employed in this study to reduce the flow. In the flux weakening area, the induction motor's performance without a speed sensor has been evaluated. As a result of the study, both the speed sensorless performance of the MPC based flux weakening control has been examined and the performance of the Model Reference Adaptive System (MRAS) and the Sliding Mode Observer (SMO) in very high speed region over nominal value has been evaluated. It has been successfully demonstrated that MPC-based flux weakening control with two speed observers can be achieved based on the current, speed, flux, and torque data collected through the simulation study. In addition, as a result of the study, it is seen that SMO has given better results.

Study of the double closed-loop active disturbance rejection control strategy for the longitudinal motion of fully submerged hydrofoil craft

In the process of high-speed driving, the longitudinal motion of the hydrofoil craft has the characteristics of parameter uncertainty and strong coupling, which leads to poor stability of the hydrofoil craft and the problem of high precision requirement for disturbance wave data. Therefore, a control method based on active disturbance rejection control (ADRC) and double closed loop is proposed. The algorithm is under the condition of decoupling the ship’s motion attitude and realizes the internal and external double-loop active disturbance rejection control of pitch angle and heave displacement based on the high-order sliding mode observer (HSMO) with better adaptability. By designing the speed error integral sliding mode surface, the dynamic characteristics of the system have been improved., and improve overall hydrofoil stability at high speeds. Finally, based on the Unreal Engine 5 platform, a visual longitudinal motion control system of hydrofoil craft is constructed. The simulation results show that compared to the existing sliding control mode, this control method can reduce the heave displacement and pitch angle by about 50%, shorten the response time of real-time control of hydrofoil craft. The superiority and effectiveness of the control system were verified.

An Extend Sliding Mode Disturbance Observer for Optical Inertial Platform Line-of–Sight Stabilized Control

As the imaging distance and focal length of photoelectric systems increase, the requirements for line-of–sight stabilization of optical inertial stabilized platforms (ISPs) become higher. Disturbance rejection directly determines the stability accuracy of optical inertial stabilized platforms. However, the accurate observation and suppression of wide-band and rapidly changing disturbances remains a challenge in current engineering applications. This paper proposes a robust extended sliding mode observer (ESMO) method to improve disturbance estimation performance. First, the linear extended state observer (LESO) is designed by taking the total disturbances as extended states. Then, a sliding mode observer (SMO) is incorporated in the extended states of the extended observer, forming a robust ESMO. Subsequently, the robustness and convergence characteristics of the proposed method are mathematically proved, revealing that it operates robustly without knowing the disturbance’s upper bound and offers faster dynamics and higher accuracy than the LESO. Finally, a series of simulation experimental tests are performed to demonstrate the effectiveness of the proposed method. The proposed method observes wide-band and rapidly changing disturbances utilizing the rapidly switching characteristic of the SMO and smooths the jitter of the SMO by cascading sliding mode estimation to the differentiation term of extended observation, achieving the integral effect of the reaching law. Meanwhile, this method only requires adjusting two parameters, making it suitable for engineering applications. It can be effectively used in optical inertial stabilized platform control systems for disturbance estimation and compensation.

Actuator and sensor fault estimation for T–S fuzzy fractional-order systems based on adaptive and sliding mode observers

Actuator and sensor fault estimation for T–S fuzzy fractional-order systems based on adaptive and sliding mode observers

Convergence rate sliding mode control of permanent magnet synchronous motor based on expanded sliding mode observer

The aim is to address the limitations of conventional sliding mode control for PMSM, namely the problems of sliding mode oscillation, load disturbance, and poor control accuracy. An improved sliding mode control strategy for PMSM is proposed based on the novel convergence rate and extended state observer. Firstly, an integral sliding mode velocity loop controller is designed based on the novel convergence rate to solve the sliding mode vibration problem. The stability of the proposed control strategy is derived by the Lyapunov criterion. Secondly, an extended state observer capable of estimating the disturbance torque is designed for the purpose of disturbance compensation. The disturbance torque estimate is then used as a feed-forward signal for the sliding mode velocity loop controller. A fast second-order sliding mode controller is used for the current loop to avoid the potential introduction of noise and disturbances. The simulation results show that compared to alternative control strategies, the proposed control strategy enables the PMSM to respond effectively to start-up and load disturbances, significantly reduces the sliding mode jitter, and improves both the torque estimation accuracy and the system control accuracy.

Improved beluga whale optimization-based variable universe fuzzy controller for brushless direct current motors of electric tractors

Brushless direct current (BLDC) motors are widely used in electric tractor powertrains, but torque ripple remains a challenge. Proportional Integral Derivative (PID) controllers are effective in steady-state regulation but struggle with load-induced uncertainties. A new method for tuning sensorless BLDC motors by integrating improved Beluga Whale Optimization (IBWO) with an optimal variable universe fuzzy (VUF) controller is proposed. The enhanced IBWO addresses limitations in solving nonlinear systems, optimizing the VUF controller for precise torque control. A fast non-singular terminal sliding mode observer is also introduced for accurate state estimation. The IBWO adjusts the VUF controller parameters in real time, enabling adaptive torque and speed regulation, thereby reducing overshoot and torque ripple. To validate the proposed approach, a dual closed-loop control model is designed to simulate motor behavior under no load, variable load, and variable speed conditions during plowing operations. The results show that the proposed controller reduces torque ripple by at least 75 % and 60 % compared to PID and fuzzy controllers, respectively, and improves speed regulation time by over 26 %, with steady-state errors of 0.6, 0.7, and 0.12 rpm (rpm) under different conditions.