Conventional Sliding Mode Research Articles

Sliding mode-based direct power control of unified power quality conditioner

The Unified Power Quality Conditioner (UPQC) is a promising solution for mitigating multiple Power Quality(PQ) issues in distribution systems, including harmonics, poor power factor, voltage sag/swell and voltage imbalance. The conventional Sliding Mode Controller (SMC) in UPQCs suffers from wide switching frequency variations, chattering problems, and inherent active and reactive power coupling. This study proposes a nonlinear control method, Sliding Mode-based Direct Power Control (SMC-DPC), for the simultaneous regulation of the shunt and series compensators in a UPQC. By optimizing voltage vector selection based on real-time power errors, the proposed method effectively mitigates chattering, and reduces switching frequency variations, and ensures precise tracking of instantaneous active and reactive powers even in the presence of coupling effects. The proposed approach simplifies the system design and improves steady state and dynamic power tracking. The simulation results in MATLAB Simulink® on a 20 kVA system demonstrate that the proposed method achieves a source current THD of 1.52%, compared to 2.31% for conventional SMC and 5.11% for linear controller. Furthermore, the method is robust to grid parameter variations and demonstrates satisfactory performance in both strong and weak grids. In weak grids, the proposed method reduces the line losses by 13.71% and 14.54% compared to SMC and linear controller respectively. The reported results comply with international standards such as IEEE-519 and IEC 61000-3-12, confirming effectiveness of SMC-DPC for enhancing PQ in distribution system.

Super-twisting sliding mode controller for energy storage system of a novel multisource hybrid electric vehicle: Simulation and hardware validation

The rapid depletion of fossil fuels, oil, and natural gas due to their excessive use is a source of motivation for finding alternative solutions. Global warming is also an important issue caused by the emission of harmful gases. Consequently, extensive research has been conducted on Fuel Cell-based Hybrid Electric Vehicles (FCEVs) to address the twin challenges of resource depletion and harmful emissions. This study presents a novel model comprising of four sources i.e. Fuel Cell (FC), Photo Voltaic panel (PV), battery, and Supercapacitor (SC). The Proton exchange membrane fuel cell (PEMFC) in which hydrogen is used as fuel is the primary source whereas the other three sources act as secondary sources. These sources are interconnected via DC converters to a DC bus. To regulate this Hybrid Energy Storage System (HESS), a Super-Twisting Sliding Mode Controller (ST-SMC) has been developed, ensuring global stability through the application of Lyapunov criteria. In addition, the proposed controller is compared with a conventional Sliding Mode Controller (SMC) and Integral Sliding Mode Controller (ISMC). Both simulation (MATLAB/Simulink 2021b) and hardware tests (MicroLabBox dSPACE RTI1202) confirm the system's stability, resilience, and efficient functionality across various dynamic scenarios. Comparative analysis between the three demonstrates the superiority of ST-SMC over the SMC and ISMC, as verified by the results.

Synthesis of Adaptive Sliding Mode Control for Twin Rotor MIMO System with Mass Uncertainty based on Synergetic Control Theory

In this paper, the authors present a new method to synthesize an adaptive sliding controller for Twin Rotor MIMO System (TRMS) based on Synergetic Control Theory (SCT). This system represents a prototype of a helicopter with two degrees of freedom and is widely used in automatic control laboratories. The complexity of the control problem is due to the nonlinear cross-coupling between the main and tail rotors. Uncertainty in system parameters further increases the complexity of the control problem. In Synergetic Control Theory, manifolds are designed for each channel. The control law is found based on sequential manifolds and the Analytical Design of Aggregated Regulators (ADAR) method. The adaptive law when the parameters are uncertain is given based on the analysis of system stability thanks to the Lyapunov function of the first manifold. Finally, the effectiveness of the proposed controller is demonstrated by numerical simulation results and comparison with conventional Sliding Mode Control (SMC).

Robust trajectory tracking of a 3-DOF robotic arm using a Super-Twisting Fast finite time Non-singular Terminal Sliding Mode Control in the presence of perturbations

Extensive research has focused on enhancing the efficiency and stability of robotic arms. Sliding mode control (SMC) is commonly used in industrial robots due to its robustness and simplicity. However, SMC approaches have challenges such as chattering and slow convergence rates which can compromise tracking accuracy. To address these issues, this paper proposes a novel Super-Twisting Fast Non-singular Terminal Sliding Mode Control (ST-FNTSMC) strategy for a 3-DOF arm robot. The proposed approach significantly improves trajectory tracking accuracy, robustness, and convergence time and eliminates chattering. The proposed controller was tested in the presence of model mismatches and external disturbances. The super-twisting methodology avoided chattering effects and increased robustness against perturbations. Two Lyapunov functions ensure closed system stability and finite-time convergence. The designed ST-FNTSMC controller is implemented in real-time using a Smart Man Robot manipulator. Its performance is compared to other sliding mode controllers, such as conventional PID Sliding Mode Control (PID-SMC), Non-singular Terminal Sliding Mode Control (NTSMC), and Fast Non-singular Terminal Sliding Mode Control (FNTSMC). Experimental results demonstrate the superior performance of the proposed controller, highlighting its effectiveness in improving the efficiency and stability of industrial robots.

Adaptive fuzzy sliding mode control of magnetic levitation system based on Interval Type-2 Fuzzy Neural Network Identification with an Extended Kalman–Bucy filter

The electromagnet levitation control system (EMS) is the core component of the magnetic levitation train. However, its nature is characterized by an inherent instability and strongly nonlinear dynamic. Additionally, modeling uncertainties and numerous exogenous perturbations make the design of the controller even more difficult for this system. To overcome these limitations, a Fuzzy Sliding Mode Control based on Interval Type-2 Fuzzy Neural Network Identification (FSMCF2NN) is designed for the EMS system. The suggested controller has a unique feature that allows the sliding surface and controller effort to be designed by using model-free techniques. As a result, the drawbacks of the conventional SMC schemes, such as the chattering effect, requiring prior knowledge of the system’s uncertainties bounds, and selection of the appropriate control gains, are eliminated. Also, the FSMCF2NN method, since it is designed based on intelligence methods not only minimizes the accumulation of state errors, but also generates the optimum control efforts of the EMS system to ensure stability in different situations. Furthermore, since the measurement of all EMS states is not achievable in reality, a qualified observer is needed to estimate unmeasured states. For this purpose, the Extended Kalman-Bucy filter (EKBF) is selected because of its superiority in managing disturbances for nonlinear systems. Furthermore, to confirm the robustness of the FSMCF2NN scheme in different scenarios, the results of the controller based on Linear Matrix Inequality (LMI), Linear Quadratic Regulation (LQR) controller, and the conventional Sliding Mode Controller (SMC) are compared with the proposed controller. Analyzing the results of simulation demonstrates that maintaining the levitated object is achievable with the proposed control scheme, effectively. At the same time, it attempts to adequately compensate for the uncertainties and disturbances present in the EMS structure.

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.

Adaptive Sliding Mode Feedback Control Algorithm for a Nonlinear Knee Extension Model

Functional electrical stimulation (FES) has been widely used to treat spinal cord injury (SCI) patients. Many research studies employ a closed-loop FES system to monitor the stimulated muscle response and provide a precise amount of charge to the muscle. However, most closed-loop FES devices perform poorly and sometimes fail when muscle nonlinearity effects such as fatigue, time delay response, stiffness, spasticity, and subject change happen. The poor performance of the closed-loop FES device is mainly due to discrepancies in the feedback control algorithms. Most of the existing feedback control algorithms were not designed to adapt to new changes in patients with different nonlinearity effects, resulting in early muscle fatigue. Therefore, this research proposes an adaptive sliding mode (SM) feedback control algorithm that could adapt and fine-tune internal control settings in real-time according to the current subject’s nonlinear and time-varying muscle response during the rehabilitation (knee extension) exercise. The adaptive SM feedback controller consists mainly of system identification, direct torque control, and tunable feedback control settings. Employing the system identification unit in the feedback control algorithm enables real-time self-tuning and adjusting of the SM internal control settings according to the current patient’s condition. Initially, the patient’s knee trajectory response was identified by extracting meaningful information, which included time delay, rise time, overshoot, and steady-state error. The extracted information was used to fine-tune and update the internal SM control settings. Finally, the performance of the proposed adaptive SM feedback control algorithm in terms of system response time, stability, and rehabilitation time was analysed using a nonlinear knee model. The findings from the simulation results indicate that the adaptive SM feedback controller demonstrated the best control performance in accurately tracking the desired knee angle movement by having faster response times, smaller overshoots, and lower steady-state errors when compared with the conventional SM across four reference angle settings (20°, 30°, 40°, and 76°). The adaptive feedback SM controller was also observed to compensate for muscle nonlinearities, including fatigue, stiffness, spasticity, time delay, and other disturbances.

Robust Hierarchical Control Design for the Power Sharing in Hybrid Shipboard Microgrids

Hybrid microgrid optimization, integration, and control are becoming increasingly important. Renewable energy source integrations are being used more often in shipping ports, as well as on short-distance cruises and ferries. Several seaports presently lack cold ironing services, which are shore-based power stations that provide electricity to ships from the main utility grids. Furthermore, diesel engines and diesel generator-based shipboards must be continuously running and on-line when docking to provide additional loads of ships due to the absence of cold-ironing services at many ports. In this research, we analytically presented the robustness of our proposed hierarchical control design for the hybrid shipboard Microgrid system containing multiple DGs and renewable energy resource (RES) integrations. The performance comparison of the conventional proportional integral (PI) vs. Sliding Mode Controller (SMC)-based control design is validated with simulation tests under different static and dynamical load conditions for both AC and DC types of loads. We further considered multi-DGs and RES integrations into our system to validate our design’s robustness against noise and unwanted faulty load conditions. The complete system stability analysis and designing of the control law are performed. Mathematical derivations and simulation results prove the robustness of the proposed hierarchical control architecture and compare the performance characteristics of two secondary controllers designed using a MATLAB/Simulink environment.

Robust Differentiator-Based NeuroFuzzy Sliding Mode Control Strategies for PMSG-WECS

A robust control algorithm is always needed to harvest maximum power from a Wind Energy Conversion System (WECS) by operating it consistently at a Maximum Power Point (MPP) in the presence of wind speed variations. In this work, a Maximum Power Point Tracking (MPPT) control algorithm is designed via Conventional Sliding Mode Control (CSMC), the Super Twisting Algorithm (STA), and the Real Twisting Algorithm (RTA) and is applied to a Permanent Magnet Synchronous Generator (PMSG)-based WECS. CSMC is model-based whereas the STA and RTA are model-free controllers. In practice, the unavailability of nonlinear terms and aerodynamic forces deteriorates the performance of these controllers. Thus, an offline NeuroFuzzy algorithm is incorporated to estimate the nonlinear drift and control input channel to improve the robustness of these algorithms. In addition, the generator shaft speed and its missing derivative is recovered via a Uniform Robust Exact Differentiator (URED). In order to carry out a comprehensive comparative study among the three competitors, the overall system is simulated in a closed loop under the action of these controllers at three different operating conditions, i.e., nominal, varying load and inertia, and varying wind speed, using MATLAB/Simulink. The acquired results confirm the superiority of the RTA over the STA and CSMC in terms of robustness and chatter reduction.

ANFIS based double integral sliding mode control for a grid-integrated hybrid power system

Hybrid Power System (HPS), an promising power generation strategy, has gained much popularity in recent years, thanks to its fundamental mechanism that taps the abundantly-available renewable energy sources. HPS comprises of a photovoltaic (PV) panel, a Battery Storage System (BSS) and a Solid Oxide Fuel Cell (SOFC). Here, PV is the chief power source whereas SOFC and BSS are used as backup generators to supply the required power in case when PV energy is deficient. All these sources, with distinct DC-DC converters, are integrated parallel to a same DC bus. A 3-phase Voltage Source Inverter (VSI) is used in this setup to convert DC voltage into AC. Different modes of operations have been demonstrated with conventional Sliding Mode Controller (SMC). The proposed Double Integral Sliding Mode Controller (DI-SMC)-trained Adaptive Neural Fuzzy Inference System (ANFIS) is utilized to enhance the performance and anti-interference ability of the hybrid system. Further, the HPS is also verified and validated in terms of achieving the preferred power supervision between DG sources, grid, and the load. Both modeling and control strategies of the hybrid scheme were simulated in MATLAB/Simulink. The aim of the proposed management scheme is to supply maximum-quality power to the grid under varying loading conditions and solar irradiance with FC state under consideration. Further, the management algorithm was also implemented to stabilize DC bus voltage under load variations.

Adaptive non-singular finite time control of nonlinear disturbed cyber-physical systems with actuator cyber-attacks and time-varying delays

This study presents a novel adaptive non-singular Terminal Sliding Mode (TSM) control procedure for the speedy and finite time stabilization of nonlinear Cyber-Physical Systems (CPSs). By employing the proposed non-linear sliding surface, the reaching phase is eliminated and the entire system’s robust performance is ameliorated. The presented online adaptive laws deal with the unwanted disturbances, actuator cyber-attacks, and time-varying delays in such a way that there is no need to recognize their upper bounds. The designed novel adaptive non-singular TSM control strategy guarantees strong robustness of the non-linear CPSs exposed to unwanted disturbances, actuator cyber-attacks and time-varying delays. It also prepares a steady response, pliable and high-precision performance, smooth control, chattering-free operation, without transient oscillations and appropriate convergence in the finite time. Numerical simulation outcomes show the proposed adaptive non-singular TSM control technique’s effectiveness and success compared to the outcomes of State-Feedback Control (SFC), conventional Sliding Mode Control (SMC) and integral-type SMC techniques.

Comparative Study of Three Types of Controllers for DFIG in Wind Energy Conversion System

An improved Doubly-Fed Induction Generator (DFIG) control strategy for the Wind Energy Conversion System (WECS) is presented in this paper. To produce the best aerodynamic torque and enhance the WECS's dynamic performance, a robust Super-Twisting (STW) sliding mode control for variable speed wind turbines is developed. To maximize power extraction, the proposed control directly tracks the DFIG's electromagnetic torque. The STW control strategy is contrasted with conventional Sliding Mode (SM) and Proportional-Integral (PI) controllers in terms of performance and efficiency. Chattering reduction, a finite convergence time, and robustness to changes in parameters and system disturbances are among the intriguing characteristics of the proposed STW algorithm.

Sliding-Mode Control Techniques of SEPIC Converter in Continuous Current Mode

Nowadays, direct current (DC) voltage is required for the operation of the machines used in many industrial applications. DC voltage generating photovoltaic (PV) panels or other DC voltage generators need to be converted from the voltage level produced by the DC voltage levels required for the operation of the machines. The conversion of a DC voltage to a different DC voltage is performed by DC-DC converters. The most commonly used DC-DC converters in the literature are buck, boost, buck-boost, CUK and SEPIC converter. In this study, different control methods are used in order to reach the desired voltage level of the output voltage of the equivalent series resistance and ideal SEPIC converter in continuous current mode. Conventional Proportional-Integral (PI) and Sliding Mode Control (SMC) methods are used to ensure that the converter reaches the desired voltage level. PI controller parameter values are calculated according to trial and error method. In order to compare the performances of the controller used in the case ideal and equivalent series resistance cases, the controller parameters are taken the same. SMC is shown to perform better in ideal and equivalent series resistance SEPIC converter compared to the traditional PI controller from simulation results. Besides, modeling and controller applications of SEPIC converter are realized by using MATLAB / SIMULINK program.

Comparison of Quantitative Feedback Theory Dependent Controller with Conventional PID and Sliding Mode Controllers on DC-DC Boost Converter for Microgrid Applications

This paper is discussed towards the issue of variations happening in the voltage control method of DC-DC boost converter. Quantitative feedback theory (QFT) is adjusted to efficiently plan a robust PID regulator, which is acknowledged utilizing only output voltage of the plant sensed by voltage sensor. The benefits of the explained PID configuration by utilizing the QFT design (Tarakanath Kobaku et al. 25) is proved with the fact that it disposes the problem of extra work and ad-hoc tuning of PID gain utilizing the regular PID methods, current measurement isn’t needed, it provides the design for case of non-minimum phase of boost converter even if there is restrictions on the bandwidth and disturbance factors are already included in the design of QFT based controller with the help of QFT bounds and thus disturbance effect is considered in the output of system. Various simulation results are taken in each case i.e. conventional PID controller and sliding mode controller. Here results are compared with the QFT based controller to verify the importance of this controller over other two controllers in terms of output response, reference tracking, line regulation and most importantly on the basis of cost effectiveness.

Neural High Order Sliding Mode Control for Doubly Fed Induction Generator based Wind Turbines

Wind energy has many advantages because it does not pollute and is an inexhaustible source of energy. In this paper Neural High Order Sliding Mode (NHOSM) control is developed for Doubly Fed Induction Generator (DFIG) based Wind Turbine (WT). The stator winding is directly coupled with the main network, whereas a Back-to-Back converter is installed to connect its rotor to the grid. The proposed control scheme is composed of Recurrent High Order Neural Network (RHONN) trained with the Extended Kalman Filter (EKF), which is used to build-up the DFIG models. Based on such identifier, the High Order Sliding Mode (HOSM) using Super-Twisting (ST) algorithm is synthesized. To show the potential of the selected scheme, a comparison study considering the NHOSM, Conventional Sliding mode (CSM), and the HOSM control is done. To ensure maximum power extractions and to protect the system, the Maximum Point Power Tracking (MPPT) algorithm and the h control are also implemented. Simulation results demonstrate the effectiveness of the proposed scheme for enhancing robustness, reducing chattering, and improving quality and quantity of the generated power.

Application of Soft Computing Techniques for Speed Control of Brushless DC Motors

Nowadays Brushless DC (BLDC) motors are treated as the most popular motors, which are applied widely due to their higher efficiency and excellent torque characteristics. Moreover they are operated with DC supply and without using brushes. But BLDC motor operate with wide speed range and therefore it is required to regulate the speed of DC motor using different advanced techniques. In this work, the fuzzy sliding mode control (FSMC) is employed for controlling the speed of BLDC motor. The proposed method is compared with the conventional Sliding mode control (SMC) and the results are analyzed using MATLAB/ Simulink tool.

Trajectory-tracking control of Mecanum-wheeled omnidirectional mobile robots using adaptive integral terminal sliding mode

Mecanum-wheeled omnidirectional mobile robot (MWOMR) is playing an increasingly significant role in modern transportation and industry due to its high flexibility and maneuverability. This paper presents an adaptive integral terminal sliding mode (AITSM) control algorithm for an MWOMR trajectory-tracking task. Initially, a four-inputs-three-outputs kinematic-and-dynamic model is identified to describe the MWOMR’s trajectory-tracking behavior. Subsequently, an AITSM controller is designed for the MWOMR, where the stability of control system is theoretically validated by means of Lyapunov. For comparison, a conventional sliding mode (CSM) controller and a nonsingular terminal sliding mode (NTSM) controller are also designed as benchmarks. Finally, simulations are executed to test the performance of proposed control laws in various scenarios. The simulation results substantiate that our AITSM control strategy is an effective solution to the MWOMR trajectory-tracking problem, which exhibits remarkable superiority in terms of tracking precision and control robustness compared with the CSM and NTSM control.

Sliding Mode Control for Chaotic Oscillations in SMIB Power System

Abstract: Power systems may revelation the harmful and undesirable chaotic phenomenon in certain conditions. This project describes the control of a chaotic oscillation in power system. Chaos may lead the power system to voltage instability and voltage collapse when voltage stability conditions are broken. Chaotic oscillations are very sensitive to parameter and initial conditions of power system. Many controllers are projected in practical to suppress the chaos and avoid voltage collapse. In this thesis, a Conventional Sliding Mode Control is applied for removal of chaotic oscillations. The aim of the controller is to remove the chaotic oscillations and bring the order to the nonlinear system. It is also shown that the proposed controller assurances the system state convergence to their desired ethics. To demonstrate the effectiveness of the projected controller, MATLAB Programming is done. Keywords: Chaotic oscillations, SMIB, Conventional Sliding Mode Control

An Adaptive Sliding Mode Control for Single Machine Infinite Bus System under Unknown Uncertainties

The inherent uncertainties in a Single Machine Infinite Bus System (SMIBS) are governed by unmodeled dynamics or large disturbances such as the system's faults. The existence of these uncertainties demands robust controllers to guarantee the system's asymptotic stability under such exacting conditions. In this work, we propose an Adaptive Sliding Mode Control (ASMC) design implemented on a fifth-order nonlinear SMIBS to handle those uncertainties without prior knowledge about its upper bounds. We develop the ASMC with gains of two nested adaptive layers to asymptotically stabilize the system's internal states, the machine's terminal voltage, and power angle within a region of unknown bounded uncertainties while mitigating the chattering phenomena associated with conventional Sliding Mode Control (SMC). To verify the design's effectiveness and prove the conducted Lyapunov theoretical stability analysis, we simulate the occurrence of a large disturbance represented by a 3-phase fault at the system's universal bus. The results show that the ASMC can successfully achieve asymptotic stable output errors and stabilizing the SMIBS internal states after the clearance of the fault. Moreover, the ASMC noticeably outperforms the SMC in chattering mitigation, where the ASMC's signal is significantly smoother than that of the SMC.

Equilibrium Optimizer-Based Robust Sliding Mode Control of Magnetic Levitation System

Magnetic Levitation System (MLS) objective is to levitate objects to the desired height without any contact. MLS is highly nonlinear and inherently unstable. Such a system imposes a challenge when designing robust and high-performance controllers. This paper presents the design of a Sliding Mode (SM) controller with an Integral term called SM-I controller to achieve the desired levitation against nonlinearities and uncertainties of the system. The controller parameters are tuned using the Equilibrium Optimizer (EO) algorithm. The Effectiveness of the proposed controller is validated by simulation results. Simulations are performed for servo tracking with and without perturbations in the MLS parameters. The proposed controller is compared with the conventional SM, LQR, and PID controllers to show its superiority. The results prove that the SM-I is more efficient than the other controllers.