Fuzzy Sliding Mode Control Research Articles

Adaptive Fuzzy Sliding Mode Controller Design Using a High-Gain Observer for a Nonlinear Aeroelastic System under Unsteady Aerodynamics

Adaptive Fuzzy Sliding Mode Controller Design Using a High-Gain Observer for a Nonlinear Aeroelastic System under Unsteady Aerodynamics

Stability coordinated control strategy of autonomous unmanned 4WID-4WIS vehicle based on APD-LQR and AFSMC

To improve the four-wheel independent driving and four-wheel independent steering (4WID-4WIS) autonomous unmanned vehicles’ active safety performance at their maximum operating capacity, the control of path following, four-wheel steering (4WS) and lateral stability should be coordinated. This paper introduces a hierarchical coordinated control framework, in which several independent controllers collaborate to enhance the overall performance of the vehicle system. Initially, in the upper controller, an adaptive preview distance linear quadratic regulator (APD-LQR) path following algorithm is proposed, considering the dynamic characteristics of the tire. It aimed at making the following error converge and obtaining the optimal front wheel steering angle. Meanwhile, an adaptive fuzzy sliding mode control (AFSMC) algorithm is implemented for obtaining additional yaw moment. It aimed at coordinating and improving effectively path following performance and lateral stability. Using the phase plane approach, the sliding surface is dynamically modified. Subsequently, in the lower controller, a rear wheel angle controller based on vehicle speed is proposed. It aimed at further enhancing vehicle’s maneuverability and stability by fully utilizing its steering redundancy features. Moreover, a torque distribution controller establishes a comprehensive cost function and then the NSGA-III algorithm is employed to optimize the torque distribution coefficients. Finally, simulation and Hardware-in-Loop (HIL) test results demonstrates that the proposed coordination algorithm can fully utilize the redundancy features of the 4WID-4WIS vehicle, and significantly enhances the vehicle’s path following accuracy and lateral stability, especially under extreme driving conditions.

Enhancing trajectory tracking accuracy in three-wheeled mobile robots using backstepping fuzzy sliding mode control

The rise in robotics technology has increased interest in ThreeWheeled Mobile Robots (TWMRs) due to their agility and adaptability across various applications. However, effectively controlling TWMRs presents a significant challenge owing to their inherent nonholonomic constraints, which restrict independent movement in all directions. Factors like sensor noise, nonlinear system dynamics, and uncertain system parameters also add to the complexity of controlling TWMRs. This research endeavors to enhance the precision of trajectory tracking in TWMRs. Specifically, it employs Backstepping Fuzzy Sliding Mode Control (BFSMC) with parameters optimized through Particle Swarm Optimization (PSO), coupled with the Extended Kalman Filter (EKF) for state estimation. The study conducts a comprehensive performance comparison between Backstepping Sliding Mode Control (BSMC) and Backstepping Fuzzy Sliding Mode Control(BFSMC) across various trajectory patterns, revealing substantial improvements in trajectory tracking accuracy with BFSMC. BFSMC demonstrates improvements in performance across various trajectory types when considering the integral time absolute error (IAE). Specifically, it achieves a 51.97% improvement for circular trajectories, an 82.09% improvement for infinity trajectories, and an 84.073% improvement for spiral trajectories. Moreover, BFSMC demonstrates superior robustness in the presence of disturbances, noise, parameter variations, and unmodeled dynamics compared to BSMC. Integrating the Extended Kalman Filter further improves accuracy, particularly in noisy conditions.

Event-triggered sliding mode control for networked T–S fuzzy delayed systems against random injection attacks

This paper investigates the event-triggered sliding mode control (SMC) problem for networked T–S fuzzy delayed systems subject to random injection attacks, where the event-triggered mechanism (ETM) is introduced to alleviate data congestion and improve network efficiency. Under an open network environment, malicious attackers may randomly inject false information into the controller–actuator channel to compromise the integrity of the data. A new fuzzy sliding mode controller is constructed based on a non-parallel distributed compensation strategy, which introduces the distribution information of the attack probability. Then, the membership function dependent (MFD) analysis technique is adopted to reconstruct the membership functions in order to reduce the conservatism caused by the mismatch between the membership functions of fuzzy model and controller. Furthermore, the co-design conditions for the ETM and the fuzzy sliding mode controller are provided, which can ensure that the resulting closed-loop fuzzy system is mean-square exponentially ultimately bounded (EUB) and the sliding surface is reachable. Finally, a simulation experiment with tunnel diode circuit application is utilized to demonstrate the validity of the proposed SMC scheme.

Moving-fuzzy sliding mode control of aircraft wing-rock motion

This work represents a moving fuzzy sliding mode controller (SMC) to suppress the wing-rock motion, a self-sustaining cycle oscillation caused by the nonlinear coupling between the unsteady aerodynamic forces and the dynamic response of the aircraft. Based on fuzzy systems, a moving algorithm is designed to estimate the unknown nonlinear dynamic function of the system in the control topology. The fuzzy algorithm is formulated by taking the width’s value, and based on Lyapunov theory, the membership function’s mean vector is adapted online. Simulation results, for examples that include small and large initial conditions, demonstrate the effectiveness of the proposed fuzzy sliding mode controller.

Improved Control Strategy for Dual-PWM Converter Based on Equivalent Input Disturbance

Aiming at the problems of jittering waveforms and poor power quality caused by external disturbances during the operation of a dual-pulse-width-modulation (PWM) converter, an improved terminal sliding mode control and an improved active disturbance rejection control (ADRC) are investigated. The method is based on mathematical models of grid-side and machine-side converters to design the controllers separately, and the balance between the two sides is maintained by the capacitor voltage. An improved terminal fuzzy sliding mode control and equivalent input disturbance (EID)-error-estimation-based active disturbance rejection control are presented on the grid side to improve the voltage response rate, and an improved support vector modulation (SVM)–direct torque control (DTC)–ADRC method is developed on the motor side to improve the robustness against disturbances. Finally, theoretical simulation experiments are built in MATLAB R2023a/Simulink to verify the effectiveness and superiority of this method.

Disturbance observer-based dynamic fuzzy sliding-mode control strategy for horizontal vibration of high-speed elevator car system

Disturbance observer-based dynamic fuzzy sliding-mode control strategy for horizontal vibration of high-speed elevator car system

Enhanced MPPT in Permanent Magnet Direct-Drive Wind Power Systems via Improved Sliding Mode Control

Addressing the challenges of significant speed overshoot, stability issues, and system oscillations associated with the sliding mode control (SMC) strategy in maximum power point tracking (MPPT) for permanent magnet synchronous wind power systems, this paper introduces a fuzzy sliding mode control (FSMC) method employing an innovative exponential convergence law. By incorporating a velocity adjustment function into the traditional exponential convergence law, a novel convergence law was designed to mitigate oscillations during the sliding phase and expedite the convergence rate. Additionally, a fuzzy controller was developed to implement a fuzzy adaptive SMC strategy, optimizing the MPPT for permanent magnet synchronous wind power generation systems. Simulation results indicated that this approach offered a faster response and superior interference rejection capabilities, compared to conventional and modified SMC strategies. The improved FSMC strategy demonstrated a swift, dynamic response and excellent steady-state performance, improving the efficiency of MPPT, thus confirming the effectiveness of the proposed method.

Control Of a Grid-Connected PMSG-Based Wind Energy System with A Back-To-Back Converter Using a Hybrid Fuzzy Sliding Mode Control

Control Of a Grid-Connected PMSG-Based Wind Energy System with A Back-To-Back Converter Using a Hybrid Fuzzy Sliding Mode Control

A novel approach to wastewater treatment control: A self-organizing fuzzy sliding mode controller

<p>The treatment of wastewater plays a crucial role in protecting the environment and ensuring the sustainable use of resources. This research paper presents a new methodology for managing wastewater treatment operations, utilising Self-Organizing Fuzzy Sliding Mode Controller (SOFSMC) to enhance the efficiency of treatment procedures. MATLAB Simulink functions as a simulation tool that facilitates meticulous analysis. SOFSMC presents a control strategy that is both adaptive and robust. This strategy effectively regulates crucial parameters, including dissolved oxygen levels, pH levels, and flow rates. It achieves this within the challenging and complex framework of wastewater treatment, which is characterised by dynamic and nonlinear dynamics. Using a SOFSMC for wastewater treatment control is novel approach. This novel technique creates a self-learning, dynamic system using fuzzy logic (FL) and sliding mode control (SMC). This unique approach can autonomously adapt to wastewater treatment processes' complex and nonlinear dynamics, improving efficiency, resource optimisation, and system dependability. The results emphasise the potential of SOFSMC as a revolutionary approach for wastewater treatment. This approach can improve treatment effectiveness, conserve resources, and protect the environment. The proposed method SOFSMC, exhibits commendable outcomes, with an integrated absolute error of 0.082 mg/L, an integrated square differential error of 0.091 mg/L, and a response time of 1.85 seconds This study offers a substantial advancement in the field of wastewater treatment regulation, highlighting its significance in the context of sustainable water management and environmental conservation.</p>

Design of a PID Controller for Microbial Fuel Cells Using Improved Particle Swarm Optimization

The microbial fuel cell (MFC) is a renewable energy technology that utilizes the oxidative decomposition processes of anaerobic microorganisms to convert the chemical energy in organic matter, such as wastewater, sediments, or other biomass, into electrical power. This technology is not only applicable to wastewater treatment but can also be used for resource recovery from various organic wastes. The MFC usually requires an external controller that allows it to operate under controlled conditions to obtain a stable output voltage. Therefore, the application of a PID controller to the MFC is proposed in this paper. The design phase for this controller involves the identification of three parameters. Although the particle swarm optimization (PSO) algorithm is an advanced optimization algorithm based on swarm intelligence, it suffers from issues such as unreasonable population initialization and slow convergence speed. Therefore, this paper proposes an improved particle swarm algorithm based on the Golden Sine Strategy (GSCPSO). Using Circle chaotic mapping to make the distribution of the initial population more uniform, and then using the Golden Sine Strategy to improve the position update formula, not only improves the convergence speed of the population but also enhances convergence precision. The GSCPSO algorithm is applied to execute the described design process. The results of the simulation show that the designed control method exhibits smaller steady-state error, overshoot, and chattering compared with sliding-mode control (SMC), backstepping control, fuzzy SMC (FSMC), PSO-PID, and CPSO-PID.

Underwater manipulator arm control based on Harris Hawk algorithm optimized RBF neural network

This article addresses the control issues of underwater manipulator arms in complex marine environments, proposing a composite control strategy based on the Harris Hawk Optimization (HHO) algorithm and Radial Basis Function (RBF) neural network. Combining the global search capability of the HHO algorithm with the fast approximation characteristics of RBF neural networks, a self-adaptive control method for underwater manipulator arms is designed. By automatically optimizing the parameters of the neural network, the performance and robustness of the control system are enhanced. Through simulation experiments, the effectiveness of the proposed control algorithm is verified. The results show that compared with the traditional RBF neural network control and fuzzy sliding mode control, the optimized control algorithm proposed in this paper has significant improvement compared with both of them, demonstrates good control effect and high practical value, and provides an effective solution for the precise control of the underwater manipulator arm.

Enhancement of unbalanced robot performance using fuzzy sliding mode control

An unbalanced robot faces difficulties in balancing its tilt to an upright position due to its intrinsic instability. Simulation, implementation in real time and performance testing of a fuzzy sliding mode controller for an unbalanced robot are the targets of this research. PID, sliding mode control and fuzzy logic control are designed to compare the proposed controller in both real time and simulation to achieve the robot's balancing. The main development of this technology is to combine the advantages of fuzzy logic and sliding mode in one model, directly controlling the tilt angle to track the desired point and maintain good control of the motor. All controllers have been compared using computational time and integral measures. The proposed controller has been tested under the effect of an external disturbance in real time and on a MATLAB Simulink. The FSMC controller's performance has given better results in speed response and integral measures.

Adaptive Robust Fuzzy Sliding Mode Control for Wastewater Treatment Processes

Adaptive Robust Fuzzy Sliding Mode Control for Wastewater Treatment Processes

Design of an Adaptive T–S Fuzzy Sliding Mode Controller for Robot Arm Tracking

Design of an Adaptive T–S Fuzzy Sliding Mode Controller for Robot Arm Tracking

Performance of Robust Type-2 Fuzzy Sliding Mode Control Compared to Various Conventional Controls of Doubly-Fed Induction Generator for Wind Power Conversion Systems

This paper presents a novel hybrid type-2 fuzzy sliding mode control approach for regulating active and reactive power exchanged with the utility grid by a doubly-fed induction generator in a wind energy conversion system. The main objective of this hybridization is to eliminate the steady-state chattering phenomenon inherent in sliding mode control while improving the transient delays caused by type-2 fuzzy controllers. In addition, the proposed control approach has proven to be successful in coping with varying generator parameters and exhibited good reference tracking. An in-depth comparative study with state-of-the-art advanced control techniques is also the focus of the present paper. The comparative study has three objectives, namely: a qualitative comparative study that aims to compare response times and reference tracking capabilities; a quantitative evaluation that takes into account time-integrated performance criteria; and finally, robustness capabilities. The simulation results, carried out in the Matlab/Simulink environment, have demonstrated the effectiveness and best performance of the proposed hybrid type-2 fuzzy sliding mode control with respect to other advanced techniques included in the comparison study.

The improvement of field-oriented control performance for double stator IPMSM based on NFC powered by dual photovoltaic solar panels

This paper presents a proposed method Neuro Fuzzy Control (NFC)-Field Oriente Control (FOC)-Space Vector Modulation (SVM) using IP controller for a five-level NPC inverter-Double Stator Interior Permanent Magnet Synchronous Motor (DSIPMSM) supplied by two photovoltaic solar panels Maximum Power Point Tracking (MPPT) Fuzzy Sliding Mode Control (FSMC). The torque generated and the magnetic flux of the DSIPMSM may be independently controlled thanks to FOC, while IP controllers are used to minimize dq axis current errors to zero. A boost converter is made up of an inductor, a capacitor, and a separate input-output connection. A MPPT approach based on the FSMC algorithm is used to extract the maximum amount of energy from the solar panel while controlling the inverter duty cycle. The NFC tool is an extremely potent instrument that bridges the gaps created by the fuzzy logic controller. The proposed method’s worth was demonstrated by the simulation results in terms of durability, decoupling between the d-q axis, and dynamic performance.

A Digital Twin-Based Adaptive Height Control for a Shearer

The shearer is an important component of the smart mining workface, and its effective control is a key aspect that guarantees safe and high-efficient production in coal mines. To address the issue of autonomous height adjustment during the shearer’s cutting process, a self-adaptive speed control method driven by digital twin technology is proposed. A digital twin-based control architecture for the shearer is first established, which consists of physical and the corresponding virtual entities, as well as reality–virtual interaction between them. Based on the mathematic model formulated for height adjustment system of the shearer, an adaptive fuzzy sliding mode controller (AFSMC) with the displacement estimation is designed for the virtual entity, with the purpose of guiding the operation of the corresponding physical entity. Simulation experiments on MATLAB compares the control performance among the proposed method and four comparative ones, including PID controller, integral sliding mode controller (ISMC), feedback linearization controller (FLC), and fuzzy sliding mode controller (FSMC). The experimental results confirm the effectiveness of the proposed AFSMC. More specifically, its steady-state error is 0.024, the maximum absolute control input is 8.43, and the settling time is 1.74 s. This also proves that the digital twin-based control method enables the precise adaptive height adjustment of the shearer, providing potential reference for the intelligent development of a smart mining workface.

Efficient Wastewater Treatment Optimisation with Solow-Polasky-JAYA Algorithm and Self-Organising Fuzzy Sliding Mode Control

Efficient Wastewater Treatment Optimisation with Solow-Polasky-JAYA Algorithm and Self-Organising Fuzzy Sliding Mode Control

Robust H∞ sliding mode control for delay-dependent uncertain T-S fuzzy descriptor stochastic Markovian jump systems with mode-dependent time-varying delays

In this article, the admissibility analysis and robust H∞ sliding mode control (SMC) are studied for uncertain T-S fuzzy singular stochastic Markovian jump systems (SSMJSs) with mode-dependent time-varying delays. The novelty of the article lies in designing a new fuzzy integral sliding manifold function to eliminate the strict assumptions on the system matrices and the limitations caused by the previous integral sliding mode control methods in practical applications. Sufficient conditions are derived to ensure stochastic admissibility of SSMJSs with a specified H∞ performance. In addition, a suitable fuzzy sliding mode controller is developed to keep the states of SSMJSs on a predetermined fuzzy manifold under the presence of uncertainties. Finally, some numerical and a practical experiment of bio-economic systems are simulated to demonstrate the validity and feasibility of the proposed method.