Traditional Sliding Mode Control Research Articles

Fuzzy sliding mode control with adaptive exponential reaching law for inverters in the photovoltaic microgrid

Photovoltaic inverters are widely utilized in microgrid systems working as the key equipment for converting solar energy into usable electricity. This paper presents a fuzzy sliding mode control (FSMC) method for the photovoltaic inverter in a microgrid. The inverter module uses voltage control to achieve stable AC output voltage. Moreover, to deal with system uncertainty and non-linearity in the photovoltaic inverter, a fuzzy controller was designed to realize real-time adaptation of the gains of the constant term and the reaching term in the sliding mode control law, which serves as a compensating controller for traditional sliding mode control. The gains of the exponential reaching law can be adjusted according to the system state instead of fixed gains, which can effectively reduce the chattering phenomenon and improve the robustness of the photovoltaic microgrid. Finally, the Lyapunov stability theory was used to ensure the stability of the entire control system, achieving a high-performance independent power supply for loads in a microgrid. Simulation results show that the designed control system is more robust to load disturbances and has superior dynamic performance.

An adaptive integral sliding mode control strategy for medical linear accelerator vacuum system

Medical linear accelerators (MLAs) are critical components for radiation therapy, providing a state-of-the-art treatment platform for cancer therapy. The vacuum system is one of the most important MLA subsystems and its stable operation is necessary to generate high-quality beams. For vacuum system pressure control, traditional proportional-integral-derivative (PID) strategies have disadvantages such as inaccurate and imprecise control response due to its simple calculation. This paper presents an innovative adaptive integral sliding mode control (AISMC) strategy aimed at enhancing the response time, precision of control, and capability to reduce disturbances within the MLA vacuum system. In addition, a nonlinear MLA vacuum system mathematical model is established based on mechanism method. Stability of the developed vacuum control system is validated using Lyapunov stability theory. Simulation results illustrate that the proposed AISMC strategy has better response speed and accuracy than traditional PID-based systems, achieving better pressure tracking performance than traditional sliding mode control strategy with PID control. Most important for the proposed controller, system chattering is effectively mitigated.

Adaptive Neural Network Finite-Time Prescribed Performance Consensus Control for a Class of Second-Order Multi-Agent Systems

This research investigates the semi-global practical finite-time prescribed performance consensus control issue for a class of second-order multi-agent systems with unknown nonlinear functions. Unlike the previous finite-time control set by a finite-time performance function, we give finite-time control by constraining the terminal sliding manifold in a performance function. In addition, an adaptive neural network control scheme is designed, which simplifies the controller and avoids the chattering issue existing in traditional sliding mode control. Eventually, a novel adaptive finite-time prescribed performance consensus control strategy is designed, which ensures that all system variables are semi-globally practical finite-time stable and consensus errors of the multi-agent systems converge within the prescribed region in finite time. The effectiveness and practicality of the presented control strategy are evaluated by conducting simulation cases.

Trajectory tracking control of the pushing ore truck manipulator based on adaptive sliding mode control with interference observer

An improved sliding mode controller was designed to solve the problem of low tracking accuracy caused by various disturbances and errors existing in the hydraulic-driven manipulator for pushing ore truck. The state space equation of the hydraulic system of the pushing ore truck manipulator was modeled and derived, and a sliding mode function was designed. For interference signals, use interference observers for observation. For the unobserved parts, adaptive laws are used for compensation to reduce system chattering, and system’s stability is verified using Lyapunov functions. The simulation results show that, compared with the traditional Sliding mode control and adaptive sliding mode control, the adaptive sliding mode control strategy based on interference observer proposed in this paper can effectively weaken the system chattering, reduce the uncertainty caused by external interference and modeling error, and improve the trajectory tracking accuracy of the pushing ore truck manipulator.

Performance improvement of predictive voltage control for interlinking converters of integrated microgrid

Voltage quality in Renewable Energy System may be harmed by varying power demand and fluctuating energy source outputs. The model predictive voltage (MPV) control approach is suggested in this study. The proposed control strategy enables appropriate power interactions between the AC and DC subgrids while sharing power fluctuations in a coordinated way. Both the AC and DC subgrids support each other in accordance with their normalized relative changes in AC frequency and DC voltage, respectively. A typical photovoltaic (PV) wind-battery-based hybrid AC/DC microgrid is modelled and investigated, and the usefulness of the proposed scheme is validated under the renewable energy variations and load perturbations. MATLAB/Simulink has validated the efficacy of the suggested strategy. A set of comparative tests was conducted between conventional Proportional-Integral (PI) control, Sliding Mode Control (SMC) strategies, and a novel Model Predictive Voltage Control (MPV) approach under diverse conditions. The aim was to comprehensively assess the advantages of the proposed MPV method. Results demonstrate that, in contrast to traditional PI and SMC control strategies, the MPV technique exhibits distinct benefits in terms of superior control performance and achieves exceptionally low Total Harmonic Distortion (THD) values. Ultimately, the proposed solution not only raises the performance standard but also ensures the stable operation of the Renewable Energy System (RES).

Model Prediction and Optimization of Belt Drive System Vibration Suppression Based on Industrial Robot Joints

Addressing the vibration issues caused by torsional angle changes in the belt transmission systems of industrial robots during practical applications, this paper introduces a control strategy that integrates a Model Predictive Control (MPC) compensation mechanism. By applying the Lagrangian method, a dynamic mathematical model correlating torsional angle and torque was established, and an algorithm design combining MPC with its compensatory controller was developed. This strategy was validated in a MATLAB simulation environment. Simulation results demonstrate that, compared to traditional sliding mode control, the newly proposed controller significantly improved response speed in tracking the torsional angle's position and angular velocity, achieving enhancements of approximately 2 seconds and 1 second, respectively. This led to higher tracking accuracy and faster convergence speed, effectively enhancing the vibration suppression performance of industrial robot joint belt transmission systems.

Multi-motor average deviation coupling control strategy based on hyperbolic convergence law high-order sliding mode control using extended state sliding mode observer

This paper aims to develop a multi-motor synchronous drive control system for the direction of the width of the lifting arm of a catamaran. Firstly, we establish a mathematical model of permanent magnet synchronous motor and introduce the composition of AC servo control systems. We then analyze the sources of control interference in multi-motor drive systems. Based on the mean deviation coupling control strategy of multi-motor synchronous control, we design a speed tracking controller and a speed synchronization controller using hyperbolic convergence law high-order sliding mode control. Then, we design an extended state sliding mode observer to estimate the unmodeled dynamics of the system in real time. We prove the global stability of the controller using Lyapunov stability theory. Finally, the control model is simulated, and the results confirm the effectiveness of the multi-motor synchronization control method based on the hyperbolic convergence law of higher-order sliding mode control. The control method effectively mitigates the jitter associated with traditional sliding mode control and demonstrates improved synchronization and tracking performance.

Evaluation of Interval Type-2 Fuzzy Neural Super-Twisting Control Applied to Single-Phase Active Power Filters

This research introduces an improved control strategy for an active power filter (APF) system. It utilizes an adaptive super-twisting sliding mode control (STSMC) scheme. The proposed approach integrates an interval type-2 fuzzy neural network with a self-feedback recursive structure (IT2FNN-SFR) to enhance the overall performance of the APF system. The IT2FNN with STSMC proposed here consists of two components, with one being IT2FNN-SFR, which demonstrates robustness for uncertain systems and the ability to utilize historical information. The IT2FNN-SFR estimator is used to approximate the unknown nonlinear function within the APF. Simultaneously, the STSMC component is integrated to reduce system chattering, improving control precision and overall system performance. STSMC combines the robustness and simplicity of traditional sliding mode control, effectively addressing the chattering problem. To mitigate inaccuracies and complexities associated with manual parameter setting, an adaptive law of sliding mode gain is formulated to achieve optimal gain solutions. This adaptive law is designed within the STSMC framework, facilitating parameter optimization. Experimental validation is conducted to verify the harmonic suppression capability of the control strategy. The THD corresponding to the designed control algorithm is 4.16%, which is improved by 1.24% and 0.55% compared to ASMC and STSMC, respectively, which is below the international standard requirement of 5%. Similarly, the designed controller also demonstrates advantages in dynamic performance: when the load decreases, it is 4.72%, outperforming ASMC and STSMC by 1.15% and 0.38%, respectively; when the load increases, it is 3.87%, surpassing ASMC and STSMC by 1.07% and 0.36%, respectively.

Rotary inverted pendulum control using neuro-adaptive robust generalized dynamic inversion

The objective of this research is to design a robust control system for trajectory tracking of the under-actuated Rotary Inverted Pendulum (RIP). The focus is on achieving semi-global asymptotic stability in the absence of knowledge about the RIP geometric and inertia parameters. The control design commences by formulating differential servo constraint dynamics (VCD) that encapsulates the control objectives for the RIP. The baseline generalized dynamic inversion (GDI) control law is derived by inverting the VCD for the control voltage input using the Moore–Penrose generalized inverse. To enhance robustness against modeling uncertainties and exogenous disturbances, a sliding mode control (SMC) element is integrated within the GDI control system, resulting in the robust GDI (RGDI) control system. Furthermore, an adaptive estimator that is based on Radial Basis Function Neural Networks (RBF-NN) is adopted to eliminate the dependency of the RGDI control system on the RIP mathematical model. The weighting matrices of the RBF-NN are updated with the aid of a Lyapunov control function. The proposed control system offers robust solution and guarantees semi-global asymptotic stability to the unstable equilibria of the RIP dynamics in the absence of a mathematical model. The proposed control system’s performance is evaluated through computer simulations and experimental tests on a real RIP system. Comparative analyses with traditional SMC and linear quadratic control methodologies are conducted to show the effectiveness of the proposed approach.

Design of ABS sliding mode control system for in-wheel motor vehicle based on road identification

Aiming at the problem that the current Anti-lock Braking System (ABS) control algorithm can not make full use of the ground friction to complete the braking when emergency braking on complex roads, an ABS sliding mode control method based on road surface identification is proposed. Combined with the in-wheel motor of in-wheel motor electric vehicle, a coordinated control method of motor regenerative braking and mechanical friction braking is designed. Based on the neural network control method, the road friction coefficient is estimated to realize the identification of typical roads and dynamically obtain the optimal slip ratio of different roads. The ABS sliding mode controler is designed with the optimal slip ratio and the actual slip ratio as input, and the saturation function is used to replace the symbol function in the traditional sliding mode control to weaken the chattering problem, and then the ABS controller is designed. The research results show that compared with the traditional ABS sliding mode controller, the designed ABS neural network sliding mode controller has the following advantages: 1. Through the identification of tire-road friction coefficient by neural network, the real-time performance and road adaptability of ABS controller are improved; 2. When the braking simulation is carried out at a given speed, the braking time is reduced by about 1.03 % and the braking distance is shortened by about 1.01%; 3. The identification of tire-road friction coefficient is realized, which weakens the chattering problem of traditional sliding mode control and improves the stability and robustness of ABS controller.

Sliding-mode Active Disturbance Rejection Permanent Magnet Synchronous Motor Control System

Aiming at the permanent magnet synchronous motor with external perturbations and internal uncertainties such as external load, rotational inertia and armature resistance changes, a servo control system based on sliding mode self-immunity controller is designed. By constructing a mathematical model of the servo system of the permanent magnet synchronous motor, considering the internal uncertainty caused by the change of the system parameters and the external random disturbance as the "total disturbance", and designing the transition process and the linear expansion state observer to observe and compensate for it, the system response can track the input signal quickly and without overshooting, and the sliding-mode state feedback makes the closed-loop servo system achieve fast and stable control, and the sliding-mode Active Disturbance Rejection control controller can be used to control the system. The sliding mode state feedback enables the closed-loop servo system to realize fast and stable control, and its consistent stability is proved by the Lyapunov method. Simulations show that this sliding- mode Active Disturbance Rejection control control strategy improves the system response speed and load carrying capability compared with the traditional sliding-mode control.

Trajectory tracking control of wheeled mobile robots with skidding and time-varying delay

This paper tackles the problem of trajectory tracking for wheeled mobile robots subject to time-varying input delay and bounded external disturbances. First, we establish a dynamic model for a wheeled mobile robot under sliding and skidding conditions, and determine the maximum allowable input delay that maintains system stability using Razumikhin-type stability analysis without prior knowledge of the variation in delay. The proposed adaptive robust controller combined with super-twisting sliding mode control is resilient to disturbances such as input delay, system uncertainty, and parameter variation. The proposed adaptive law enables real-time modification of switching gain based on tracking error without predefined knowledge of uncertainty bounds. Compared with traditional sliding mode control strategies, the super-twisting algorithm can eliminate chattering phenomenon while combined robust methods further reduce modeling uncertainties’ influence on system performance. Finally, we select an appropriate Lyapunov function to analyze and prove uniformly ultimately bounded of the closed-loop system. MATLAB simulation comparison results demonstrate that this approach achieves high tracking accuracy, faster response speed, and robustness.

A Study on the Vehicle Antilock System Based on Adaptive Neural Network Sliding Mode Control

Vehicle antilock systems play a very important role in the stability and reliability during vehicle braking. Due to the complexity of the braking process, antilock braking system (ABS) usually face the problems such as nonlinearity, time-varying, and uncertain parameter modeling. Thus, aiming at the parameter model uncertainty problem of ABS, an adaptive neural network sliding mode controller (ADRBF-SMC) is designed in this paper. On this basis, establishing the quarter-vehicle model and the seven-degree-of-freedom vehicle model, and treating the difference between the two models as a kind of disturbance, carrying out vehicle braking performance simulation experiments to analyze the variation of braking performance parameters such as vehicle and wheel speeds, slip ratio, braking distance, braking torque, under the three cases of adaptive neural network sliding mode controller, traditional sliding mode controller, and no control. Simulation results show that the adaptive neural network sliding mode controller (ADRBF-SMC) proposed in this paper can play an effective control role in both vehicle dynamics models. In addition, the control method proposed in this paper has stronger anti-interference capability and higher robustness compared with the sliding mode controller (SMC).

Feedback Linearization Sliding Mode Control Strategy for Three-Phase Voltage PWM Rectifier Based on New Variable Speed Reaching Law

Three-phase voltage PWM rectifier is a multivariable, strong coupling, nonlinear multi-input and multi-output system. In the design of rectifier control systems, with PI control it is difficult to achieve the ideal control effect, the dynamic performance is poor, and the parameter computation is complex. Moreover, the traditional sliding mode control voltage outer loop suffers from the problem of chattering, which is difficult to solve. Responding to the above issues, a new type of variable speed reaching law is proposed, which is applied to the voltage outer loop sliding mode control, while the feedback linearization principle is introduced to the current inner loop, and a new type of double closed-loop sliding mode control system is obtained by applying the two theories to the design of the sliding mode controller. A simulation model is established in MATLAB/Simulink to compare the PI control, the SMC control and the V-SMC control strategy proposed in this paper (voltage outer loop V-SMC current inner loop FLC-SMC control), and the simulation results show that the rectifier under the new dual-closed-loop sliding mode control strategy has the advantages of good dynamic performance, strong robustness and strong anti-interference ability. At the same time, compared with the traditional sliding mode control strategy, the vibration suppression effect under the proposed control strategy is obvious. Finally, a 10 kW rectifier control system is built on a semi-physical hardware-in-the-loop experimental platform to further verify the correctness and superiority of the proposed control strategy.

Optimized robust fuzzy sliding mode control for efficient wastewater treatment: a comprehensive study

Wastewater treatment plants (WWTPs) are plagued by nonlinearities, uncertainties, and disturbances that degrade control performance and may even lead to severe instability. The WWTP control issue has received a lot of research and development during the last several decades. One well-known way of designing a resilient control system is called sliding mode control (SMC). The SMC's greatest strength lies in its innate resistance to disturbances and uncertainty. Incorporating fuzzy SMC would eliminate the chattering effect, the primary drawback of traditional sliding-mode controller, without sacrificing robustness against parametric uncertainties, modeling errors, and variable dynamic loads. This article discusses the hybridization of fuzzy logic with sliding mode control to provide highly excellent stability and accuracy in a control system. As a means of optimizing the fuzzy SMC, the gradient-free optimization technique known as the Jaya algorithm is investigated. By repeatedly altering a population of individual solutions, this population-based method can deal with both limited and unbounded optimization issues.

Neural network control of fractional-order chaotic systems with unknown control direction

In this paper, neural network control of fractional order chaotic systems (FOCSs) with input saturation and unknown sign of the controller gain is addressed by employing the Nussbaum function, where neural networks are utilized to model system uncertainties. To get rid of the limitation that reaching phase should be active before sliding motion in the traditional sliding mode control, a stable sliding surface is constructed. Then, by using the integer order Nussbaum gain control method, a novel controller with neural network sliding mode variable structure is designed. Finally, the practicality of the designed method is confirmed by a simulation experiment.

Research on Super-Twisting Sliding Mode Voltage Regulation Control of Electric Spring

The traditional control of electric spring (ES) is too complex and lack of robustness, as well as the chattering phenomenon and control accuracy of traditional sliding mode control, a second-order terminal sliding mode control based on super twisting algorithm is proposed. In this paper, the phase plane method is used to establish the mathematical model of ES, solve the state space equation, and regard the AC power supply as a simplified error analysis. Then a second-order sliding mode controller is proposed. The controller parameters are solved to obtain the second-order sliding mode control rate, and the stability is verified. Finally, three groups of comparative simulation experiments were set up by Matlab/Simulink, and the effectiveness of the model was verified by considering the voltage mutation caused by the change of power supply and circuit parameters. Compared with the traditional control method, the proposed control strategy not only ensures the voltage stability, but also improves the control accuracy and the performance of suppressing sliding mode chattering, and has better dynamic response ability.

Fractional-Order Sliding-Mode Control and Radial Basis Function Neural Network Adaptive Damping Passivity-Based Control with Application to Modular Multilevel Converters

This paper proposes a hybrid control scheme that combines fractional-order sliding-mode control (FOSMC) with radial basis function neural network adaptive damping passivity-based control (RBFPBC) for modular multilevel converters (MMC) under non-ideal operating conditions. According to the passive control theory, we establish the Euler–Lagrange (EL) models of positive and negative sequences based on the unbalanced grid. A passivity-based controller that satisfies the energy dissipation law is designed. To enable rapid convergence of the system energy storage function, a radial basis function neural network (RBFNN) is introduced to adjust the injection damping adaptively. Additionally, a fractional-order sliding-mode controller (FOSMC) is designed. The fractional-order sliding mode surface used can improve tracking performance, and effectively suppressed the undesirable chattering phenomenon compared to the traditional sliding-mode control (SMC). Finally, combining the two control methods can effectively solve the issue of passivity-based control (PBC) being too dependent on parameters. The proposed hybrid control scheme enhances the ability of the system to resist disturbances, and improves its overall robustness. Simulation results demonstrate the feasibility and effectiveness of this control method.

Enhanced sliding mode controller design via meta‐heuristic algorithm for robust and stable load frequency control in multi‐area power systems

AbstractThis article introduces a novel approach named HBA‐dHoSMO, which combines a continuous decentralized higher‐order sliding mode controller‐based observer (dHoSMO) with the honey badger algorithm (HBA), specifically designed for load frequency control in multi‐area power systems (MAPSs). Traditional sliding mode controllers (SMCs) employed in load frequency control of MAPSs often face challenges related to chattering and oscillations, leading to decreased robustness and stability. Additionally, tuning the parameters for these SMC designs to achieve optimal performance in MAPSs can be challenging. The HBA‐dHoSMO is proposed to address the issues of chattering and oscillations, while the optimal parameters for SMC design are obtained using HBA. The stability analysis of the entire system is conducted using linear matrix inequality and the Lyapunov stability theory, affirming the reliability and feasibility of the approach. A comprehensive set of case studies is performed under various configurations and conditions. Additionally, particle swarm optimization and tuna swarm optimization, in conjunction with SMC‐based and proportional–integral–derivative controllers, are examined for performance comparison. Simulation results demonstrate the superior performance of the proposed controller across all case studies. This is evidenced by the lowest integral time absolute error values recorded as 0.0133, 6.45 × 10−4, and 0.0167 for single‐, two‐, and three‐area power systems, respectively.

Direct yaw moment control of eight-wheeled distributed drive electric vehicles based on super-twisting sliding mode control

This paper proposed a novel direct yaw moment control (DYC) system to enhance vehicle stability and handling performance in various driving conditions and overcome the chattering problem of traditional sliding mode control. Accordingly, a DYC strategy is developed for eight-wheeled DDEVs by utilizing a super-twisting sliding mode (STSM) algorithm. Initially, a three-degrees-of-freedom model, nonlinear tire model, and motor model are established for vehicles. Subsequently, the reference yaw rate is obtained based on the reference model of the vehicle to serve as a control target. The DYC strategy is then established using the error between the actual yaw rate and the reference yaw rate as the input. Moreover, a traditional sliding mode (SM) controller is developed to enhance vehicle stability. A second-order SM controller is designed by incorporating a STSM control algorithm to address the chattering problem associated with traditional SM controllers. The algorithm adaptively adjusts the sliding surface and controls the gains based on the dynamic state of the vehicle. The effectiveness of the proposed control strategy is validated via hardware-in-the-loop simulations.