Super-Twisting Sliding Research Articles

Adaptive Nonhomogeneous Super-twisting Sliding Mode Control for Aircraft Braking System With Disturbance Compensation

Adaptive Nonhomogeneous Super-twisting Sliding Mode Control for Aircraft Braking System With Disturbance Compensation

A Composite Super-Twisting Sliding Mode Approach for Platform Motion Suppression and Power Regulation of Floating Offshore Wind Turbine

The floating platform motion of an offshore wind turbine system can exacerbate output power fluctuations and increase fatigue loads. This paper proposes a new scheme based on a fast second-order sliding mode (SOSM) control and an adaptive super-twisting extended state observer to suppress the platform motion and power fluctuation. Firstly, an affine nonlinear model of the floating wind turbine pitch system is constructed. Then, a fast SOSM pitch control law is adopted to adjust the blade pitch angle, and a new adaptive super-twisting extended state observer is constructed to achieve total disturbance observation. Finally, simulations are conducted under two cases of wind and wave conditions based on FAST (fatigue, aerodynamics, structures, and turbulence) and MATLAB/Simulink. Compared with the traditional proportional integral (PI) control scheme and standard super-twisting control scheme, the platform roll under the proposed scheme is reduced by 13% and 4%, and pitch is reduced by 16% and 3% in Case 1. Correspondingly, the roll is reduced by 9% and 15%, and pitch is reduced by 7% and 1% in Case 2. For the tower top pitch and yaw moment, load reductions of 7% and 3% or more are achievable compared with those under the PI control scheme. It is indicated that the proposed scheme is more effective in suppressing floating platform motion, stabilizing output power of the wind turbine system, and reducing tower loads.

Particle Swarm Optimization Based Super Twisting Sliding Mode Controller MPPT Algorithm Design for Solar PV System

To maximise energy harvest while keeping the solar photovoltaic (PV) system stable, proper regulation is essential. In this study, we explore the state-of-the-art in solar PV system management and offer a fresh method: Super Twisting Sliding Mode Controller (STSMC) using Particle Swarm Optimisation (PSO). The suggested method enhances control performance by combining the stability of Sliding Mode Control (SMC) with the optimisation skills of PSO. The goal is to improve solar PV systems' fault tolerance, resilience against uncertainties and disturbances, and Maximum Power Point Tracking (MPPT) accuracy. Results show that the suggested PSO-based STSMC is superior to traditional methods of control. Effective maximum power point tracking (MPPT) is made possible by combining PSO optimisation with the SMC structure. This allows for precise MPPT tracking regardless of the sun irradiation or ambient temperature. The technique also improves the system's fault tolerance and robustness by dealing with uncertainty and disturbances efficiently in real time. The study highlights limitations, such as the use of idealised settings, and provides recommendations for further research to enhance the suggested method. The MATLAB/Simulink Simulation Tool is used for the research.

Current Tracking and Circulating Current Suppression Super-Twisting Sliding Mode Control for Parallel Operation of EAST Fast Control Power Supply

To quickly output sufficient current for plasma excitation control, parallel operation of a structure of multiple branches is adopted in the Experimental Advanced Superconducting Tokamak (EAST) fast control power supply. During the process of parallel operation of multiple branches in engineering, a larger inductance current sharing reactor is used to suppress the circulating current for the branches, which reduces the dynamic response speed of the output current and increases economic costs. In order to achieve cost savings and improve the dynamic response speed of the output current, the parallel branch current model of the EAST fast control power supply is analyzed, and the current of each branch is reconstructed into two parts: the current flowing to the load end and the circulating current flowing to other branches. Without changing the circuit structure and increasing the additional complex communication system for each branch, observation of the current flowing to the load end from each branch is achieved. Based on the observed current, a super-twisting sliding mode controller (STSMC) is designed to suppress the circulating current flowing through branches. To realize fast output of the branch current and circulating current suppression for the branches, a new STSMC with a linear term and parameter adaptive structure is designed, speeding up the convergence rate of the whole control system. The linear term and designed parameter adaptive structure based on the sliding mode system status ensure fast convergence speed and excellent control performance of the system. Simulation and experiments show that the designed control method can achieve fast output current control for each branch and that the tracking performance of the total output current is good. While reducing the inductance of the current sharing reactor, the circulating current for the branches is effectively suppressed compared with traditional control methods. The proposed method has great significance in cost savings and performance improvement in engineering practice applications.

Advanced Control Approach Based on a Super-Twisting Sliding Mode for a Cascade Voltage and Current Control Topologies of a Grid-Supporting Grid-Forming Power Converters

Advanced Control Approach Based on a Super-Twisting Sliding Mode for a Cascade Voltage and Current Control Topologies of a Grid-Supporting Grid-Forming Power Converters

Super-Twisting Sliding Mode Active Disturbance Rejection Control of Permanent Magnet Synchronous Motor Based on Vector Control

The vector control speed regulation system of the permanent magnet synchronous motor founded on traditional control has some weaknesses of poor rapidity and robustness, and it is hard to satisfy the current control requirements. A new control method combining active disturbance rejection control technology with improved super-twisting sliding mode control technology (STSM-ADRC) comes up. In active disturbance rejection control, the tracking differentiator is simplified; the extended state observer is designed to observe all internal and exterior disturbances of the system; the improved super-twisting sliding mode control law is going to take the place of the error feedback control law; and the simulation is carried out. Compared with the existing control algorithms, this new control method can overcome the influence of load disturbances and track the given speed effectively. And the responsiveness and robustness of the system are strengthened.

Adaptive Super-Twisting Sliding Mode Control of Underwater Mechanical Leg with Extended State Observer

Underwater manipulation is one of the most significant functions of the deep-sea crawling and swimming robot (DCSR), which relies on the high-accuracy control of the body posture. As the actuator of body posture control, the position control performance of the underwater mechanical leg (UWML) thus determines the performance of the underwater manipulation. An adaptive super-twisting sliding mode control method based on the extended state observer (ASTSMC-ESO) is proposed to enhance the position control performance of the UWML by taking into account the system’s inherent nonlinear dynamics, uncertainties, and the external disturbances from hydrodynamics, dynamic seal resistance, and compensation oil viscous resistance. This newly designed controller incorporates sliding mode (SMC) feedback control with feedforward compensation of the system uncertainties estimated by the ESO, and the external disturbances of the hydrodynamics by fitting the parameters, the dynamic seal resistance, and the compensation oil viscous resistance to the tested results. Additionally, an adaptive super-twisting algorithm (AST) with integral action is introduced to eliminate the SMC’s chattering phenomenon and reduce the system’s steady-state error. The stability of the proposed controller is proved via the Lyapunov method, and the effectiveness is verified via simulation and comparative experimental studies with SMC and the adaptive fuzzy sliding mode control method (AFSMC).

A novel optimally tuned super twisting sliding mode controller for active and reactive power control in grid‐interfaced photovoltaic system

AbstractIn photovoltaic (PV) systems, inverters play a crucial role for supplying electricity to meet the demand while maintaining power quality. For a local load connected to a grid‐interfaced photovoltaic (GIPV) system, active and reactive power control is necessary at the distribution level. Thus, the foremost purpose of this article is to get the best optimally designed robust controller for control of active and reactive power. A GIPV system with Improved Arithmetic Optimisation Algorithm (IAOA)‐based Super Twisting Sliding Mode Controller (ST‐SMC) methodology has been proposed in this article for active and reactive power management. The conventional PI controller in the GIPV system that is most frequently used has considerable undershoot and a long settling period. PI controller tuning parameters were also changed to account for the wide change in the reference pattern. Therefore, STSMC and SMC are used for ensuring robustness against external disturbances. The conventional SMC comes out to have a chattering issue. Furthermore, the proposed IAOA technique is validated through some benchmark functions. The proposed IAOA technique outperforms Particle Swarm Optimisation (PSO), Forensic Based Investigation (FBI), and Traditional Arithmetic Optimisation Algorithm (TAOA) in terms of the number of iterations and accurately achieving optimal solutions for active and reactive power control. The results show that the proposed IAOA‐based STSMC technique has an improved performance of settling time and undershoot for active and reactive power control. This article also presents stability analysis and robustness test of the above mentioned controllers to illustrate the effectiveness of each optimally designed controller. A 40 kW GIPV system performance is evaluated using the MATLAB environment, and the results are validated in a real‐time simulator platform OPAL‐RT 4510.

Torque Ripple Reduction of Nonsinusoidal Brushless DC Motor Based on Super-Twisting Sliding Mode Direct Power Control

In this paper, a second-order sliding mode control, based on super-twisting algorithm, is proposed for direct power control of the brushless DC (BLDC) motor. The proposed controller uses a super-twisting scheme that requires only sliding surface information and can handle system uncertainties and external disturbances, well. This scheme can improve the BLDC motor torque ripple by solving the disadvantages of the conventional sliding mode control method, such as the chattering effect and high frequency switching control. This method is simple and robust for the BLDC motor’s biggest challenge, torque ripple, which does not require any voltage and current control loops or complex reference frame transformations. The simulation results of the proposed method are compared with the direct power control (DPC) and model predictive control (MPC) methods, which indicate the superiority of the proposed method in both the steady- and transient-states. Moreover, the motor parameters variation in the tracking of active and reactive power are discussed. In addition, the practical results of the proposed method in both cases of speed and load variation show the effectiveness of this method in reducing power (torque) ripple and current total harmonic distortion (THD) and increasing the system’s efficiency compared to other methods.

Neural Network Approach Super-Twisting Sliding Mode Control for Path-Tracking of Autonomous Vehicles

This paper proposes a neural network approach adaptive super-twisting sliding mode control algorithm for autonomous vehicles. An adaptive and robust control algorithm in autonomous vehicles is needed to compensate for disturbance and parametric uncertainty from the variable environment and vehicle conditions. The sliding mode control (SMC) is a robust controller that compensates for robust and reasonable control performance against disturbance and parametric uncertainty. However, the inherent limitation of the sliding mode control, namely the chattering phenomenon, has a negative effect on the system. Additionally, when the disturbance exceeds the defined boundaries, the control stability is compromised. To overcome these limitations, this study incorporates the radial basis function neural network (RBFNN) and Lyapunov function to estimate disturbance and parametric uncertainty. The estimated disturbance is reflected in the super-twisting sliding mode control (STSMC) to reduce the chattering phenomenon and achieve enhanced robust performance. The performance evaluation of the proposed neural network approach control algorithm is conducted using the double lane change (DLC) scenario and rapid path-tracking (RPT) scenario, implemented in the CarMaker and Matlab/Simulink environments, respectively.

Design of improved direct torque control based on a five level torque controller and a new Sugeno-Takagi fuzzy super-twisting controller applied to an induction machine

This paper deals with the design and Hardware In Loop (HIL) verification of a novel intelligent Super-twisting Sliding Mode Speed Controller (STSMSC) based on a Sugeno-Takagi Fuzzy Logic System (STFLS) for an induction motor controlled by an improved Modified Direct Torque Control (MDTC) approach. Indeed, Conventional Super-twisting Sliding Mode Speed Controller (CSTSMSC) is chosen as an alternative solution for attenuating the chattering phenomenon brought on by the discontinuous control law of the First Order Sliding Mode Speed Control (FOSMSC) and maintaining its advantages like simplicity, robustness and accuracy. Moreover, the STSMSC is a type of a second order sliding mode control technique that needs only the information about the sliding surface, not its time derivative. However, the choice parameters of the CSTSMSC existing in the control law effects the tracking accuracy, the overshoot and the amplitude of the chattering. Therefore, an STSMSC based on an STFLS (STFLS-STSMSC) is proposed for online tuning of these parameters in transient and steady state operations. In fact, the STFLS is considered as an intelligent supervisor to adjust these parameter values according to the system state, which consequently provides excellent performance consisting in a higher robustness rate under disturbances and uncertainties, tracking with excellent accuracy and reduced chattering in steady state and transient operations. Numerous simulation and HIL co-simulation results are presented to demonstrate the effectiveness of the suggested STFLS-STSMSC based MDTC implemented on a Xilinx FPGA Zynq 7000 SoC ZC702.

FeedForward Super-Twisting Sliding Mode Control for Robotic Manipulators: Application to PKMs

This article deals with the development and implementation of a novel feedforward super-twisting sliding mode controller for robotic manipulators. A full stability analysis based on a Lyapunov candidate is established showing a local asymptotic finite-time convergence of the proposed controller in the presence of upper bounded disturbances. Its robustness toward parametric uncertainties and system disturbances, thanks to the super-twisting approach, is pointed out. In addition, the feedforward dynamic term of the proposed controller that can compensate for the model nonlinearities is not sensitive to measurement noise. Real-time experiments have been conducted on two parallel manipulators: a 5-DOF SPIDER4 PKM and a 3-DOF Delta PKM. The effectiveness of the proposed controller is validated in different scenarios, including the nominal case and robustness toward parametric variations (payload) and speed changes

Adaptive Super-Twisting Sliding Mode Forward Tracking Guidance Law Considering Missile Body Characteristics

Aiming at the problem of forward tracking guidance munitions with missile body characteristics intercepting maneuvering targets, and to improve the hit accuracy of forward tracking guidance system, this paper proposes an adaptive super-twisting sliding mode forward tracking guidance algorithm considering the impact of first-order overload characteristics of missile body and input saturation limitation. Firstly, the nonlinear model of the guidance system is made by the kinematic relation for projectile-target, the principle for the forward tracking interception, the overload characteristic of the missile body, and the input saturation limitation. Secondly, a finite time interference observer (FTIO) is adopted to accurately estimate the nonlinear interference in the guidance system, furthermore, an adaptive super-twisting algorithm for slide mode control is used to design the forward tracking guidance algorithm considering the characteristics of the projectile body. Finally, the results by simulation indicate the adaptive super-twisting slide mode for forward tracking guidance algorithm considering characteristics of the projectile body designed in this paper has better-hit accuracy.

Sensorless Vector Control for Induction Motor Drive at Very Low and Zero Speeds Based on an Adaptive-Gain Super-Twisting Sliding Mode Observer

To achieve a balance between estimation accuracy and the chattering problem for sensorless induction motor IM drive systems at very low and zero speeds, an adaptive-gain super-twisting sliding mode observer (AGSTSMO) is proposed. The value of the adaptive gains (experimental observer coefficients) in the control law is assigned utilizing Lyapunov stability theory to ensure expeditious convergence of the estimated variables to improve the accuracy and suppress chattering at very low and zero speeds operation. The AGSTSM observer eliminates the need to use a low-pass filter to obtain the equivalent control, as in the traditional first-order sliding-mode observer, which causes a delay in estimating the equivalent control law. Simulation and experimental results are shown under different operating conditions to illustrate the effectiveness of the suggested approach, which achieves excellent estimation precision and chattering elimination capability simultaneously, thus increasing robustness versus deviations of the motor parameters. Moreover, to prove the superiority and efficiency of the proposed observer is compared to that of a super-twisting sliding mode observer (STSMO) under various operating conditions.

Closed-Loop Deep Brain Stimulation Using a Type of Fixed-Time Sliding Mode Controller for Avoiding Epileptiform Discharge in a Human Cortical Model

The cortical excitability rate correlates with epileptic seizures, which should be modulated in a finite time to prevent brain damage. Deep brain stimulation (DBS) is a successful treatment for patients with refractory epilepsy. This study proposes a closed-loop DBS control method that uses a Fixed-Time Integral Super-Twisting Sliding Mode Controller (FTISTSMC) to stop simulated epileptiform discharges within a limited time by modifying cortical excitability in the human Cortical Model (CM). First, the simulated stimulus current in the DBS method is injected into the state variable of the excitatory neurons population in CM. Then, the FTISTSMC is applied to the state variable described above to regulate the stimulus current value during the stimulation process to reduce the possible destructive effects of electrical pulses on the behavior of the healthy neurons. The proposed controller has some characteristics causing the simulated epileptic activity to become normal and the tracking error of the healthy state to be completely zero after 4.7[Formula: see text]s. In addition, it is possible to resolve the chattering and singularity problems and completely control epileptic seizures within a fixed and finite time frame.

A New Efficient Cuckoo Search MPPT Algorithm Based on a Super-Twisting Sliding Mode Controller for Partially Shaded Standalone Photovoltaic System

The impact of Partial Shading Conditions (PSCs) significantly influences the output of Photovoltaic Systems (PVSs). Under PSCs, the Power-Voltage (P-V) characteristic of the PVS unveils numerous power peaks, inclusive of local maxima and a global maximum. The latter represents the optimum power point. Traditional Maximum Power Point Tracking (MPPT) algorithms struggle to track the Global Maximum Power Point (GMPP). To address this, our study emphasizes the creation of a novel algorithm capable of identifying the GMPP. This approach combines the Cuckoo Search (CS) MPPT algorithm with an Integral Super-Twisting Sliding Mode Controller (STSMC) using their benefits to enhance the PVS performance under PSCs in terms of high efficiency, low power losses, and high-speed convergence towards the GMPP. The STSMC is a second-order Sliding Mode Control strategy that employs a continuous control action that attenuates the “chattering” phenomenon, caused when the first-order SMC technique is employed. Indeed, the proposed CS-STSMC-MPPT algorithm consists of two parts. The first one is based on the CS algorithm used for scanning the power-voltage curve to identify the GMPP, and subsequently generating the associated optimal voltage reference. The second part aims to track the voltage reference by manipulating the duty cycle of the boost converter. The proposed CS-STSMC-MPPT algorithm is featured by its strength against uncertainties and modeling errors. The obtained simulation results underline a high convergence speed and an excellent precision of the proposed method in identifying and tracking the GMPP with high efficiency under varying shading scenarios. For comparative purposes, this method is set against the hybrid CS-Proportional Integral Derivative, the conventional CS, the Particle Swarm Optimization, and the Perturb and Observe algorithms under different PSCs, including zero, weak, and severe shading. Simulation conducted in the Matlab/Simulink environment confirms the superior performance of the proposed CS-STSMC-MPPT algorithm in terms of precision, convergence speed, efficiency, and resilience.

Online parameters estimation schemes to enhance control performance in DC microgrids

This paper addresses the problem of achieving current sharing and voltage balancing in DC microgrids when the filter parasitic resistances of the Distributed Generation Units (DGUs) are unknown and potentially time-varying. Two schemes are proposed for current sharing and voltage balancing, which use only generated current and voltage measurements. The first scheme is a novel distributed adaptive control utilising the principles of back-stepping and passivity-based control design, intended for the case of constant parasitic resistances. The second scheme alters an existing stabilising controller for current sharing and voltage balancing by incorporating the estimation of the unknown, possibly time-varying, parasitic resistance. A Super-Twisting Sliding Mode Algorithm (STA) estimates the parasitic resistance and its bounded variations in finite-time. The simulation results using a DC microgrid composed of 4 DGUs demonstrate the performance of the proposed schemes.

Distribution Network Voltage Improvement using Dynamic Voltage Restorer with Smooth Super Twisting Sliding Mode Control

Sensitive industrial customers need higher Power Quality (PQ). End-users and electrical utilityfirms are both paying more attention to PQ problems as a result of massive financial losses caused by PQproblems to sensitive industrial loads. The most important and frequently recurring PQ problems in asecondary distribution system are voltage sags and swells. A rapid, adaptable, dynamic Custom PowerDevice called the Dynamic Voltage Restorer can be utilized to reduce voltage sags and swells. The controlapproach selected for switching Voltage Source Converters (VSCs) largely determines its performance. Byusing the Sliding Mode Control (SMC) with Smooth Super Twisting Algorithm (SSTA), this researchsuccessfully removes the effects of voltage sags and swells on the VSC of DVR. When SSTA is used inconjunction with conventional SMC, chattering, a downside of SMC, is reduced while keeping otheradvantages including robustness, shorter response times, and insensitivity to changings in the load. Theefficiency of this technique is evaluated using the MATLAB/Simulink SimPower System toolbox. Thesimulation's findings demonstrate that the Smooth Super Twisting Sliding Mode Controller (SSTSMC) forDVR device can identify and compensate the voltage sags/swells in less than 2 milliseconds (ms), which issignificantly less time than the 20 milliseconds allowed limit of SEMI F-47 standard for sensitive loads andthe values of Total Harmonics Distortion (THD) is less than 5%. The comparative study also performedbetween Real Twisting Algorithm (RTA) and proposed technique Smooth Super Twisting Algorithm(SSTA), which shows that performance of proposed SSTSMC is much better than RTA.

Super-Twisting Sliding Mode Control to Improve Performances and Robustness of a Switched Reluctance Machine for an Electric Vehicle Drivetrain Application

In electric vehicles, performances of electric vehicle drivetrains depend on the electric machine and the control. Switched Reluctance Machines (SRMs) are today an alternative to rare earth magnets machines such as Permanent Magnet Synchronous Machine (PMSM), which is used in the vehicle drivetrain. Because of its high nonlinear behavior, the classical control designed for SRMs is not sufficient to obtain good performances. The objective of this paper is to make performance and robustness comparisons of the designed robust controllers considering the high nonlinear behavior of SRMs. Sliding Mode Control (SMC) and Super-Twisting Sliding Mode Control (STSMC) are developed and validated by simulation for the velocity control loop and the current control loops of the control strategy. However, an evaluation of their performances compared to classical control based on PI controllers is carried out. For a robustness comparison, a variation of SRM parameters is carried out by simulation using the three controllers. Finally, an experimental validation on a developed test bench using the three controllers is conducted to show that Super-Twisting Sliding Mode Control (STSMC) is the best in terms of performances and robustness for an electric vehicle application.

Kalman Filter-Based Super-Twisting Sliding Mode Control of Shunt Active Power Filter for Electric Vehicle Charging Station Applications

Electric vehicle (EV) charging stations draw nonlinear currents, which makes the distribution network unbalanced, distorted, and results in power quality (PQ) issues. These PQ issues are mitigated in this work through high-performance control of the shunt active power filter (SAPF). In the proposed method, a linear Kalman filter (LKF) has been applied to grid voltage and load current signals for harmonic and disturbance robust estimation purpose. Unlike the conventionally used orthogonal vector model, in this work, phase angle vector model together with a simple to tune phase-locked loop (PLL) has been considered for the LKF implementation. DC-link voltage regulation and charging of the DC-link capacitor has been obtained by proposing a Luenberger observer-based super twisting sliding mode control (ST-SMC), which has fast dynamic response and lower voltage ripples compared to similar other existing control methods. This results in significant reduction in size, cost and loss together with lifetime enhancement of the DC-link capacitor. Rigorous sensitivity analysis is conducted to analyze the robustness of the developed method. The proposed control technique achieves fast response time and satisfy the harmonic requirements as specified in the IEEE Std. 519 under various grid and load disturbances. Comparative quasi-real time validation results are presented by using digital signal processor (DSP) based processor-in-the-loop (PIL) with another recently proposed control strategy to verify the performance enhancement by the developed method.