Fuzzy Control Research Articles

Relaxed conditions for parameterized linear matrix inequality in the form of nested fuzzy summations

The aim of this study is to investigate less conservative conditions for parameterized linear matrix inequalities (PLMIs) that are formulated as nested fuzzy summations. Such PLMIs are commonly encountered in stability analysis and control design problems for Takagi-Sugeno (T-S) fuzzy systems. Utilizing the weighted inequality of arithmetic and geometric means (AM-GM inequality), we develop new, less conservative linear matrix inequalities for the PLMIs. This methodology enables us to efficiently handle the product of membership functions that have intersecting indices. Through empirical case studies, we demonstrate that our proposed conditions produce less conservative results compared to existing approaches in the literature.

Adaptive event-triggered stochastic estimator-based sampled-data fuzzy control for fractional-order permanent magnet synchronous generator-based wind energy systems

This study aims to develop an adaptive event-triggered estimation sampled-data control method for a fractional-order permanent magnet synchronous generator (PMSG)-based wind energy system (WES) using the Takagi–Sugeno (T–S) fuzzy approach. Unlike existing PMSG-based WES control schemes, we propose an aperiodic event-triggered communication scheme with an adaptive mechanism to reduce data transmission. To address the challenge of limited communication resources, we introduce an adaptive event-triggered (AET) estimation method with aperiodic sampling, significantly reducing communication overhead while maintaining stable WES performance. This method employs transmission techniques based on absolute errors, resulting in high data transmission efficiency. First, the fractional-order model for the PMSG-based WES is represented using linear sub-models through the T–S fuzzy approach. Next, a fuzzy aperiodic AET mechanism is proposed for the PMSG model with augmented estimation error systems. Then, the fractional Lyapunov function theory is employed to derive linear matrix inequalities (LMIs), ensuring bounded mean-square stability for the PMSG-based WES with the augmented estimation error systems. Additionally, the desired estimator control gains are determined through solvable LMIs. Finally, simulation studies are presented to demonstrate the superiority and feasibility of the proposed control scheme.

System modeling and fuzzy adaptive PI sliding-mode control of a novel ship-mounted cable-based bulkhead cleaning robot

Ship cleaning poses significant risks and consumes considerable time, primarily due to the irregular hull surface geometry and the unpredictable ship motion induced by wave excitation. To address this challenge and achieve efficient, autonomous cleaning, a spatial variable structure cable-based bulkhead cleaning robot (SC-R) is proposed. Unlike traditional cable-driven parallel mechanisms (CDPMs), SC-R operates in a non-inertial frame, whereby its motion is subject to continuous and unpredictable ship motion. Consequently, SC-R exhibits intricate dynamic characteristics, posing a significant hurdle for controller design. To solve this problem, a sliding-mode controller is proposed for SC-R. Specifically, the dynamic model of SC-R, which includes ship motion and various disturbing forces, is systematically established using the Newton-Euler method. Furthermore, a fuzzy adaptive PI sliding-mode controller (FAPI-SMC) is specifically proposed to enhance the convergence speed and robustness of SC-R. The stability of the closed-loop system is analyzed using Lyapunov theory, and the tracking error is proved to be eventually bounded. Finally, the availability of this theory is confirmed by simulation and experiment. It is shown that FAPI-SMC can achieve a satisfactory control effect in practice.

Implementation of a low-cost current perturbation-based improved PO MPPT approach using Arduino board for photovoltaic systems

In photovoltaic (PV) systems, the conversion of solar energy into electrical energy by the PV module is influenced by various factors, including sunlight intensity and temperature. To achieve optimal performance, it is crucial to accurately track the maximum power point (MPP) of the PV module. Among the numerous MPP tracking (MPPT) techniques that have been developed, the perturbation and observation (PO) method has gained significant attention due to its simplicity and reliability. However, the fixed perturbation step size used in the traditional PO algorithm can result in poor tracking capability, excessive ripple, and drift, leading to high power loss and low tracking efficiency. To address these limitations, this paper proposes an improved version of the PO strategy that introduces an adaptable step-magnitude mechanism, utilizing current perturbation instead of the voltage perturbation typically employed in the classical PO method. This enhanced indirect PO approach maintains low complexity and can be easily implemented on a cost-effective Arduino Uno board. A streamlined C++ code integrates the improved PO MPPT strategy with a Proportional Integral Derivative (PID) controller, eliminating the need for separate PID blocks and further reducing system complexity. To evaluate the effectiveness of the proposed technique, comparative analyses are conducted against the traditional PO algorithm, particle swarm optimization (PSO), fuzzy logic control (FLC), and a recently introduced approach, the zone voltage (ZV) method. Simulation results using Proteus software demonstrate that the improved PO approach outperforms the other techniques in various aspects, including achieving the highest static and dynamic tracking efficiencies of 99.38% and 99.88%, respectively, negligible power loss and fluctuations, the fastest convergence speed, and the shortest tracking time of 0.19 seconds.

Dynamic Programming-Based ANFIS Energy Management System for Fuel Cell Hybrid Electric Vehicles

Reducing reliance on fossil fuels has driven the development of innovative technologies in recent years due to the increasing levels of greenhouse gases in the atmosphere. Since the automotive industry is one of the main contributors of high CO2 emissions, the introduction of more sustainable solutions in this sector is fundamental. This paper presents a novel energy management system for fuel cell hybrid electric vehicles based on dynamic programming and adaptive neuro fuzzy inference system methodologies to optimize energy distribution between battery and fuel cell, therefore enhancing powertrain efficiency and reducing hydrogen consumption. Three different approaches have been considered for performance assessment through a simulation platform developed in MATLAB/Simulink 2023a. Further validation has been conducted via a rapid control prototyping device, showcasing significant improvements in hydrogen usage and operational efficiency across different drive cycles. Results manifest that the developed controllers successfully replicate the optimal control trajectory, providing a robust and computationally feasible solution for real-world applications. This research highlights the potential of combining advanced control strategies to meet performance and environmental demands of modern heavy-duty vehicles.

Fertigation control system based on the mariotte siphon

This paper adopts the Mariotte siphon to simplify the nutrient solution preparation structure while improving the liquid mixing accuracy for nutrient solution. A liquid mixing model suitable for EC and pH regulation was constructed by combining fuzzy control algorithms, and a set of fertigation nutrient solution control equipment was designed and developed. During the experiment, comparison was made between the Fuzzy-PID algorithm and the traditional PID algorithm in terms of nutrient solution configuration. The results show that the Fuzzy-PID algorithm is smoother and more stable compared to the PID algorithm. Through analysis of the liquid mixing accuracy with the venturi type fertigation machine, it was found that the fertigation machine designed with the Mariotte structure is more accurate for liquid mixing, and can better meet the needs of crop growth.

Adaptive Fixed-Time Command-Filtered Fuzzy Control for Nonlinear Stochastic Systems Subject to Saturating Input and Its Application to the Electromechanical System

Adaptive Fixed-Time Command-Filtered Fuzzy Control for Nonlinear Stochastic Systems Subject to Saturating Input and Its Application to the Electromechanical System

Application of deep learning-based fuzzy systems to analyze the overall risk of mortality in glioblastoma multiforme

Abstract Glioblastoma multiforme (GBM) is the most aggressive brain cancer in adults, with 3.2-3.4 cases per 100 thousand. In the US, brain cancer does not rank in the top 10 causes of death, but it remains in the top 15. Therefore, this research proposes a fuzzy-based GRUCoxPH model to identify missense variants associated with a high risk of all-cause mortality in GBM. The study combines various models, including fuzzy logic, Gated Recurrent Units (GRUs), and Cox
Proportional Hazards Regression (CoxPh), to identify potential risk factors. The dataset is derived from TCGA-GBM clinicopathological information and mutations to create four risk score models: GRU, CoxPH, GRUCoxPHAddition, and GRUCoxPHMultiplication, analyzing 9 risk factors of the dataset. The Fuzzy-based GRUCoxPH model achieves an average accuracy of
86.97%, outperforming other models. This model demonstrates its ability to classify and identify missense variants associated with mortality in GBM, potentially advancing cancer research.

A Novel Convexification Method for Control Synthesis Analysis of Continuous-Time Saturated Positive Polynomial Fuzzy Systems Under Imperfect Premise Matching

A Novel Convexification Method for Control Synthesis Analysis of Continuous-Time Saturated Positive Polynomial Fuzzy Systems Under Imperfect Premise Matching

Takagi-Sugeno fuzzy gain controller for Vehicle-to-Grid (V2G) load frequency control

Load Frequency Control (LFC) has gained more importance with the introduction of deregulated Renewable Energy Sources (RES) connectivity with the grid. Electrical Vehicles (EVs) can feed electricity back into the grid in Vehicle-to-Grid (V2G) mode to maintain stability. However, the increasing number of EVs penetrating the grid causes frequency instability in the power system. If required, EVs may utilize bi-directional chargers to transfer power back to the grid in the V2G mode while they are charging or in a grid-connected state, restoring the frequency instability of the grid. The frequency restoration response time is important to reset the grid frequency fluctuations in the shortest time possible to avoid shutting down the power system. This paper presents a Takagi-Sugeno (T-S) fuzzy linear output controller for LFC in two-area systems with tie-line control. This work models EV batteries as a single lump of large-capacity battery energy storage systems. The EV's battery system provides ancillary power to the two-area power system to reset it to a steady state after a load disturbance. The T-S fuzzy controller's linear output dependency on its inputs enables it to respond efficiently to load variations in the nonlinear two-area power systems. The proposed controller parameters are evaluated from stability analyses and its robustness is tested with sensitivity analysis. It is compared with other fuzzy controllers, and it demonstrates a fast-settling time and reduced frequency deviation response.

Generalized Ulam-Hyer stability results for fuzzy fractional stochastic system under Caputo fractional generalized Hukuhara differentiability concept

Generalized Ulam-Hyer stability results for fuzzy fractional stochastic system under Caputo fractional generalized Hukuhara differentiability concept

An MPPT Controller with a Modified Four-Leg Interleaved DC/DC Boost Converter for Fuel Cell Applications

A fuel cell system can produce electricity and water more efficiently while emitting near-zero emissions. Internal constraints and operating parameters such as hydrogen, temperature, humidity levels, and oxygen gas partial pressures trigger a nonlinear power characteristic in a typical fuel cell stack, resulting in reduced overall system efficiency. Consequently, it's critical to get the most power out of the fuel cell stack while minimizing fuel use. This study examines and proposes a radial basis function network (RBFN) based maximum power point tracking technique (MPPT) for a 6-kW proton exchange membrane fuel cell (PEMFC) system. The proposed MPPT algorithm modulates the duty cycle of the modified four-leg interleaved DC/DC boost converter (MFLIBC) to extricate the maximum power from the fuel cell system. To validate the execution of the proposed controller, the outcome is related to the various MPPT control strategies such as PID & Mamdani fuzzy inference systems. Finally, it was observed that the proposed RBFN controller has achieved an enhanced efficiency of 83.2 % relative to the PID and fuzzy logic controllers of 75.5 % and 77.4 % respectively. The efficiency of the proposed configuration is analysed using the MATLAB/Simulink platform.

Motion Trajectory Based Position Control Scheme for Switched Reluctance Machines

Switched reluctance machines (SRMs) are widely used in electric drive areas. However, due to the machine structure, the large stride angle makes it hard to achieve accurate position control for SRMs. In this paper, a position control scheme based on motion trajectory is proposed for SRM position control. In the proposed position control scheme, the SRM control is divided into the accelerating process, the steady operating process and the regenerating process. Development of the proposed position control strategy contains two parts. First, a Fuzzy logic controller is designed for rotor speed control in the accelerating process and steady operating process. Then, an aim speed regulator is proposed for the regenerating process, in which the aim speed is regulated according to the difference between aim position and real‐time rotor position. In the end, both simulation and experimental results are given to show the effectiveness of proposed motion trajectory based position control scheme. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Electronic brake system-based hierarchical motion control of commercial vehicle for autonomous obstacle avoidance

As a high-level autonomous driving technology, efficient and safe automatic obstacle avoidance on highway is one of the critical and ongoing topics that requires in-depth research for commercial vehicles. Electronic control air pressure brake system, owing to its fast response and active braking ability, is an effective active-chassis technology to function path tracking and stability control in such condition. Yaw motion stability control problem of autonomous driving commercial vehicle based on active braking control with electronic air brake system for automatic obstacle avoidance is investigated. First, a hierarchical yaw motion dynamics control architecture is introduced after vehicle and brake system modeling. The quintic polynomial curve-based trajectory planning method and a Model Predictive Control based trajectory tracking control strategy are formulated. Second, to evade the possible stability losing issue in this circumstance, Cubature Kalman Filter (CKF) is utilized to estimate the vehicle motion states, and a fuzzy PID control-based differential brake stability control strategy is designed for the electronic brake system composed of front-and-rear two sets of dual-channel pressure regulator and ABS solenoid valve. Finally, the proposed hierarchical yaw stability control strategy for a commercial vehicle in typical obstacle avoidance conditions is comparatively verified based on the joint simulation tools. After the yaw stability control, the peak value of the lateral displacement error of trajectory tracking can be reduced by 44.7%. The yaw stability control strategy based on fuzzy PID control can reduce the yaw rate by 46%–57% and the sideslip angle by 60%–73% under different conditions. The results show that the proposed control strategy can effectively improve the yaw stability of the vehicle while avoiding obstacles at high speed.

Barrier function–based adaptive STSMC for steering feel feedback of SBW vehicle with uncertainty

As an essential component of human–computer interaction in steer-by-wire (SBW) vehicles, the steering feel feedback system provides drivers with accurate steering feel by precisely controlling the output torque of the steering feel motor. Meanwhile, the existence of uncertainties makes the accurate control of steering feel feedback torque become an acknowledged challenging issue. This article proposes an adaptive super-torque sliding mode control strategy based on barrier functions for the steering feel feedback system of SBW vehicles with highly complex dynamics in the presence of uncertainties. First, to achieve intelligent modeling under the influence of uncertainty factors, a dynamic system with unknown uncertainty based on adaptive fuzzy logic system (FLS) is proposed. In the framework of this model, the uncertainty factors introduced by unknown external disturbance, parameter uncertainty, and model couplings are considered as a lumped uncertainty function. Furthermore, the membership functions of the FLS are dynamically adjusted based on nearest neighbor clustering, aiming to enhance the modeling accuracy beyond traditional fuzzy modeling. To further offset the system uncertainty and FLS approximation error, an adaptive super-twisting sliding mode control (STSMC) method based on barrier function is proposed. This algorithm retains the advantages of traditional sliding mode control (SMC) methods in dealing with systems with uncertainty and eliminates the need for a priori knowledge of the upper bound of uncertainties by introducing the adaptive law based on barrier function, avoiding the use of complex identification algorithms. Moreover, it eliminates the gain of overestimation and significantly reduces chattering phenomenon in traditional sliding mode control. Finally, based on Lyapunov stability theory, this article proves the proposed system can achieve convergence within a finite time. Compared with STSMC, traditional SMC and proportional–integral–derivative (PID) control, the results verify the effectiveness of the proposed control method. The proposed strategy provides a new solution to reduce the steering feel feedback torque error under the influence of uncertain factors.

A Fuzzy Pure Pursuit for Autonomous UGVs Based on Model Predictive Control and Whole-Body Motion Control

In this paper, we propose an adaptive fuzzy pure pursuit trajectory tracking algorithm for autonomous unmanned ground vehicles (UGVs), addressing the challenges of accurate and stable navigation in complex environments. Traditional pure pursuit methods with fixed look-ahead distances struggle to maintain precision in dynamic and uneven terrains. Our approach uniquely integrates a fuzzy control algorithm that allows for real-time adjustments of the look-ahead distance based on environmental feedback, thereby enhancing tracking accuracy and smoothness. Additionally, we combine this with model predictive control (MPC) and whole-body motion control (WBC), where MPC forecasts future states and optimally adjusts control actions, while WBC ensures coordinated motion of the UGV, maintaining balance and stability, especially in rough terrains. This integration not only improves responsiveness to changing conditions but also enables dynamic balance adjustments during movement. The proposed algorithm was validated through simulations in Gazebo and real-world experiments on physical platforms. In real-world tests, our algorithm reduced the average trajectory tracking error by 45% and the standard deviation by nearly 50%, significantly improving stability and accuracy compared to traditional methods.

Enhancing Traffic Management in Cyber Physical Systems – A Gradient Based Fuzzy Controller Approach

Traffic forecast is a critical aspect of effective traffic management and planning in cyber-physical systems (CPS). In this study, we present a novel approach to traffic prediction and regulation within cyber-physical systems (CPS), introducing the Gradient Rule based Fuzzy Controller. This innovative methodology utilizes dynamic fuzzy logic control enhanced with gradient-based rules to adapt signal timings in real-time, effectively addressing the variable nature of traffic. Our results demonstrate significant improvements in reducing total queue length and delay at intersections, with reductions of up to 91.23%. Furthermore, extensive simulations and evaluations underscore the superiority of our approach compared to state-of-the-art models, highlighting its flexibility and adaptability to diverse traffic scenarios. This research emphasizes the novelty of integrating gradient-based rules into fuzzy control techniques, offering a promising avenue for advancing traffic management systems in CPS environments.

Application of fuzzy PID control algorithm in hypersonic vehicle transpiration cooling control

As an efficient active cooling method, transpiration cooling is employed for thermal protection of blunt nose cones. However, most current systems utilize open-loop control for coolant supply. This study establishes a dynamic model of a one-dimensional blunt nose cone transpiration cooling system that concurrently considers aerodynamic heating, internal heat transfer in porous media, and the thermal insulation process of the air film layer formed by injected coolant. The findings indicate that the coolant film's thermal insulation effect significantly impacts the nose cone cooling system, with flow in the porous media causing a time-delay in the dynamic insulation effect. After implementing a closed-loop feedback controller, the fuzzy PID control algorithm demonstrates superiority over the conventional PID control algorithm in mitigating positive and negative feedback misalignment issues caused by time-delay, resulting in reduced temperature oscillation time and amplitude. Additionally, the fuzzy PID control algorithm achieves faster response and shorter stabilization time when external interference from varying Mach numbers occurs.

A Hybrid Spatial Fuzzy Inference and CNN Approach for Forecasting Changes in Remote Sensing

Satellite cloud images, captured by Earth observation satellites, provide crucial information for weather forecasting, aviation, natural resource management, and disaster response. This study presents a novel method for detecting changes in satellite cloud images, combining convolutional neural networks (CNNs) for feature extraction with a spatial complex fuzzy inference system (CFIS). The CNNs effectively capture color channel features in the image blurring and deblurring processes, while the CFIS models the spatiotemporal relationships in the data. Experiments conducted on a US Navy satellite image dataset demonstrate the effectiveness and stability of our proposed method, outperforming related studies in terms of both root mean square error (RMSE) and R-squared (R2) metrics.

Adaptive fuzzy coordinated control design for wind turbine using gray wolf optimization algorithm

Due to the randomness and intermittency of wind speed, the actual output power curve of a wind turbine (WT) deviates greatly from the theoretical power curve, thereby reducing the power generation capacity of the WT. An adaptive fuzzy coordinated control (AFCC) of WT is presented in this study to improve the power generation of WT. Firstly, a multi-objective optimization model (MOOM) for WT output power, generator speed and pitch angle is established, and its optimal solution set is used as the input eigenvector of a novel effective wind speed soft sensor (NEWSSS) model, which is modeled with kernel extreme learning machine (KELM). Secondly, a novel improved gray wolf optimization (NIGWO) algorithm is presented by improving the convergence factor and adaptive weights, which is used to solve MOOM and optimize the parameters of KELM. A variable pitch control (VPC) is designed by estimating the effective wind speed. Finally, an adaptive fuzzy control (AFC) is presented for WT. Based on the AFC and VPC, an AFCC for pitch angle and generator torque is designed for WT. The high measuring precision of NEWSSS and the good robustness and dynamic performance of AFCC are demonstrated by the simulation results.