Fuzzy Tuning Research Articles

A fuzzy tuning approach for controller parameters of a parallel manipulator based on clustering analysis

A fuzzy tuning approach for controller parameters of a parallel manipulator based on clustering analysis

Novel Battery Management with Fuzzy Tuned Low Voltage Chopper and Machine Learning Controlled Drive for Electric Vehicle Battery Management: A Pathway Towards SDG

Novel Battery Management with Fuzzy Tuned Low Voltage Chopper and Machine Learning Controlled Drive for Electric Vehicle Battery Management: A Pathway Towards SDG

The primary frequency modulation control strategy based on fuzzy active disturbance rejection control for gas turbines

Gas turbines play a core role in clean energy supply and construction of comprehensive energy system, and the control performance of primary frequency modulation (PFM) of gas turbine has a great impact on the frequency control of the power grid. However, there are some control difficulties in the PFM control of gas turbines, such as the coupling effect of fuel control loop and speed control loop, slow tracking speed and large variation of operating conditions. To relieve the above difficulties, a control strategy based on the fuzzy-modified active disturbance rejection control (F-MADRC) proposed in this paper. Based on the analyses of the parameter stability region for active disturbance rejection control (ADRC), fuzzy tuning rules of parameters for F-MADRC are designed. Finally, the proposed F-MADRC is applied to the PFM system of MS6001B heavy-duty gas turbine. The simulation results show that the gas turbine unit with the proposed method can obtain the best control performance of the PFM with strong ability to deal with system uncertainties. The proposed method shows good engineering application potential.

Adaptive Fuzzy Tuning of Smart Transformer for Microgrid-Utility Power Interactions

Adaptive Fuzzy Tuning of Smart Transformer for Microgrid-Utility Power Interactions

Modeling of Hyperparameter Tuned Fuzzy Deep Neural Network–Based Human Activity Recognition for Disabled People

Human activity recognition (HAR) for disabled people is a vital research area, which aims to help individuals with disabilities in their daily lives. HAR involves using technology, typically wearable devices or sensors, to automatically identify and classify human activities and movements. HAR using deep learning (DL) is an effective and popular method to automatically classify and identify human activities based on sensor information. This article develops a hyperparameter tuned fuzzy deep neural network–based HAR (HTFDNN‐HAR) method. The objective of the HTFDNN‐HAR method lies in human activities identification and classification. In the presented HTFDNN‐HAR technique, cross‐guided bilateral filtering (CGBF)–based preprocessing is initially applied and MobileNetV3 architecture is applied for the effectual extraction of the feature vectors. In addition, the HTFDNN‐HAR technique makes use of the FDNN method for the efficient detection and classification of human activities. Finally, the HTFDNN‐HAR technique applies the manta ray foraging optimization (MRFO) technique for the optimum hyperparameter selection of the FDNN approach. A wide range of experiments have been carried out on the CAUCAFall dataset comprising 10,000 samples with two classes. The simulation value highlighted that the HTFDNN‐HAR technique reaches an enhanced accuracy of 99.40% over other recent approaches.

Temperature-Controlled Laser Cutting of an Electrical Steel Sheet Using a Novel Fuzzy Logic Controller

A novel PID-type fuzzy logic controller (FLC) with an online fuzzy tuner was created to maintain stable in situ control of the cutting front temperature, aiming to enhance the laser process for thin non-oriented electrical steel sheets. In the developed controller, the output scaling factors and the universe of discourse were initially optimized using a hybrid of the particle swarm optimization and grey wolf optimization methods. The optimal parameters obtained were utilized in experiments involving the laser cutting of thin non-oriented electrical steel sheets, compared to an open-loop control system maintaining a constant cutting speed. The PID-type FLC with an online fuzzy tuner demonstrated a superior cutting quality, generating a smaller roundness and a reduced heat-affected zone (HAZ) through the in situ tuning of control parameters. Particularly, the HAZ width was significantly smaller than that reported in a previous study which used fuzzy gain scheduling for temperature control. Moreover, the cutting time was diminished by optimally adjusting the cutting speed using PID-type FLC with an online fuzzy tuner. Therefore, the accumulated heat in the steel sheet, particularly under high laser pulse frequencies, was effectively reduced, making it suitable for industrial applications.

Discrete-Time Modeling and Control for LFC Based on Fuzzy Tuned Fractional-Order PDμ Controller in a Sustainable Hybrid Power System

Discrete-Time Modeling and Control for LFC Based on Fuzzy Tuned Fractional-Order PD^μ Controller in a Sustainable Hybrid Power System

Generalized Daily Reference Evapotranspiration Models Based on a Hybrid Optimization Algorithm Tuned Fuzzy Tree Approach

Reference evapotranspiration (ET0) is an important driver in managing scarce water resources and making decisions on real-time and future irrigation scheduling. Therefore, accurate prediction of ET0 is crucial in the water resources management discipline. In this study, the prediction of ET0 was performed by employing several optimization algorithms tuned Fuzzy Inference System (FIS) and Fuzzy Tree (FT) models, for the first time, whose generalization capability was tested using data from other stations. The FISs and FTs were developed through parameters tuning using Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Pattern Search (PS), and their combinations. The FT was developed by combining several FIS objects that received ranked meteorological variables. A total of 50 FIS and FT models were developed and the model ranking was performed utilizing Shannon’s Entropy (SE). Evaluation outcomes revealed the superiority of the hybrid PSO-GA tuned Sugeno type 1 FT model (with R = 0.929, NRMSE = 0.169, accuracy = 0.999, NS = 0.856, and IOA = 0.985) over others. For evaluating the generalization capability of the best model, three different parts of datasets (all-inclusive, 1st half, and 2nd half) of the five test stations were evaluated. The proposed hybrid PSO-GA tuned Sugeno type 1 FT model performed similarly well, according to the findings, on the datasets of the test stations. The study concluded that the hybrid PSO-GA tuned Sugeno type 1 FT approach, which was composed of several standalone FIS objects, was suitable for predicting daily ET0 values.

Active Vibration Control for the Mitigation of Wheel Squeal Noise Based on a Fuzzy Self-Tuning PID Controller

The wheel squeal noise of a train is often made when it passes a tight curve. The noise annoys the passengers and the people living close to railway tracks. According to the research background, wheel vibration, as a result of unstable contact force, is the main source of wheel squeal noise. This study presents a novel method to reduce wheel squeal noise based on the active vibration control of wheels and the use of piezoelectric actuators attached to wheel treads. The proposed method is implemented in an experimentally validated time model involving the linear dynamics of wheel and track and nonlinear contact forces. Then, the model is modified to enhance the effect of the piezoelectric actuators. The relationship between the momentum and the voltage applied to the piezoelectric patch is also considered in modeling. To determine the amplitude and the direction of the applied voltage, a feedback controller is designed based on the fuzzy self-tuning PID controller scheme. This controller is similar to the conventional PID controller, but its coefficients are tuned by the fuzzy tuning mechanism according to the wheel response. The results show that the proposed method is capable of suppressing wheel squeal noise, especially in high frequencies. Furthermore, it is as applicable to worn wheels as to new ones.

Construction and Evaluation of a Control Mechanism for Fuzzy Fractional-Order PID

In this research, a control mechanism for fuzzy fractional-order proportional integral derivatives was suggested (FFOPID). The fractional calculus application has been used in different fields of engineering and science and showed to be improved in the past few years. However, there are few studies on the implementation of the fuzzy fractional-order controller for control in real time. Therefore, for an experimental pressure control model, a fractional order PID controller with intelligent fuzzy tuning was constructed and its results were calculated through simulation. To highlight proposed control scheme advantages, the performances of the controller were inspected under load disturbances and variations in set-point conditions. Furthermore, with classical PID control schemes and fractional order proportional integral derivative (FOPID), a comparative study was made. It is revealed from the results that the suggested control scheme outclasses other categories of the control schemes.

Unmanned Aerial Vehicles Motion Control with Fuzzy Tuning of Cascaded-PID Gains

One of the main challenges of maneuvering an Unmanned Aerial Vehicle (UAV) to keep a stabilized flight is dealing with its fast and highly coupled nonlinear dynamics. There are several solutions in the literature, but most of them require fine-tuning of the parameters. In order to avoid the exhaustive tuning procedures, this work employs a Fuzzy Logic strategy for online tuning of the PID gains of the UAV motion controller. A Cascaded-PID scheme is proposed, in which velocity commands are calculated and sent to the flight control unit from a given target desired position (waypoint). Therefore, the flight control unit is responsible for the lower control loop. The main advantage of the proposed method is that it can be applied to any UAV without the need of its formal mathematical model. Robot Operating System (ROS) is used to integrate the proposed system and the flight control unit. The solution was evaluated through flight tests and simulations, which were conducted using Unreal Engine 4 with the Microsoft AirSim plugin. In the simulations, the proposed method is compared with the traditional Ziegler-Nichols tuning method, another Fuzzy Logic approach, and the ArduPilot built-in PID controller. The simulation results show that the proposed method, compared to the ArduPilot controller, drives the UAV to reach the desired setpoint faster. When compared to Ziegler-Nichols and another different Fuzzy Logic approach, the proposed method demonstrates to provide a faster accommodation and yield smaller errors amplitudes.

A Hybrid PSO–GWO Fuzzy Logic Controller with a New Fuzzy Tuner

A Hybrid PSO–GWO Fuzzy Logic Controller with a New Fuzzy Tuner

An optimized fuzzy sliding based active disturbance rejection control for simultaneous cyber‐attack tolerant and demand response participation program

The simultaneous investigation of false data injection cyber-attack (FDICA) and demand response (DR) participation in smart power systems are dealt with in this paper. For this purpose, simultaneous and separate detection of FDICA and determination of the changes in the power consumption for participation in DR (PDR) are necessary. To balance between generation and consumption, PDR must be equal to the load disturbances (LDs). Active-disturbance rejection control (ADRC) with the extended state observer (ESO) is implemented to detect FDICA and LDs. But, ESO only detects the sum of FDICA and LDs without the ability to separate them. Therefore, a nonlinear sliding mode observer (NSMO) is implemented to detach FDICA from LDs. In order to improve the NSMO estimation accuracy, the modified salp swarm algorithm (SSA) is implemented to design NSMO parameters. Moreover, to achieve better performance of ADRC, a fuzzy tuner is applied to the online tuning of controller parameters. The proposed optimized fuzzy sliding-based modified active-disturbance rejection control (FSADRC) is tested for load frequency control of the IEEE 9 bus standard test system and the 10 machines New England test power system considering communications time delay. The results of several simulation scenarios indicate the efficient performance of the proposed method.

Robust fuzzy sliding mode controller for a skid-steered vehicle subjected to friction variations.

Skid-steered vehicles (SSV) are gaining huge importance in the market due to their applications like construction, agricultural work, material handling etc. The accuracy of performing such tasks require a robust control algorithm. The design of such controller is very challenging task due to external disturbances caused by wheel-ground interaction and aerodynamic effects. This paper proposes robust fractional and integral order fuzzy sliding mode controllers (FSMC, FFSMC) for a skid-steered vehicles with varying coefficient of friction and a displaced center of gravity (CG). FFSMC controller reduces the outcome of forces generated as a result of ground tire interaction during skidding and friction variations. The proposed controllers are implemented for a four-wheel SSV under high-speed turning motion. A simulation environment is constructed by implementing the SSV dynamics with wheel-road model and the performance of the proposed algorithms is tested. The simulation test is conducted for a Pioneer-3AT (P-3AT) robot SSV vehicle with displaced CG and variable coefficient of tires friction. Simulation results demonstrate the efficiency of the proposed FFSMC algorithm in term of reduced state errors and minimum chattering. The proposed controller compensates the effect of different responses of the wheels generated as a result of variable CG. The chattering phenomenon generated by conventional SMCs is also minimized by fuzzy tuning approach.

Adaptive fuzzy PI controller for permanent magnet synchronous motor drive based on predictive functional control

The high-performance speed control is hard to achieve for the permanent magnet synchronous motor (PMSM) drive system under uncertain disturbances, input saturation, and system delay. To address this challenge, a novel adaptive fuzzy proportional-integral (AFPI) control scheme is investigated in this paper. An anti-saturation PI (ASPI) controller is proposed serving as the main control unit that drives PMSM to track the desired profiles, which is optimized online by a designed adaptive fuzzy tuner. Specifically, together with the parameter update constraints, an improved anti-integral saturation mechanism is suggested for the ASPI controller. Meanwhile, the adaptive fuzzy tuner with the self-tuning input domain enhances the optimization ability to effectively tackle the system uncertainties. Then, a predictive functional control method is incorporated into the tuner to mitigate the control lag caused by error-driven characteristics, which ensures the dynamic tracing control performance in the presence of system delay. The stability and the H∞ robustness of the AFPI controller are guaranteed based on d-decomposition theory. Simulations and real-time experiments are performed, and the results illustrate that the designed scheme has superior performance in terms of dynamic tracking control and anti-disturbance capabilities as compared to conventional methods.

Fuzzy Tuned PID Controller for Envisioned Agricultural Manipulator

The implementation of image-based phenotyping systems has become an important aspect of crop and plant science research which has shown tremendous growth over the years. Accurate determination of features using images requires stable imaging and very precise processing. By installing a camera on a mechanical arm driven by motor, the maintenance of accuracy and stability becomes non-trivial. As per the state-of-the-art, the issue of external camera shake incurred due to vibration is a great concern in capturing accurate images, which may be induced by the driving motor of the manipulator. So, there is a requirement for a stable active controller for sufficient vibration attenuation of the manipulator. However, there are very few reports in agricultural practices which use control algorithms. Although, many control strategies have been utilized to control the vibration in manipulators associated to various applications, no control strategy with validated stability has been provided to control the vibration in such envisioned agricultural manipulator with simple low-cost hardware devices with the compensation of non-linearities. So, in this work, the combination of proportional-integral-differential (PID) control with type-2 fuzzy logic (T2-F-PID) is implemented for vibration control. The validation of the controller stability using Lyapunov analysis is established. A torsional actuator (TA) is applied for mitigating torsional vibration, which is a new contribution in the area of agricultural manipulators. Also, to prove the effectiveness of the controller, the vibration attenuation results with T2-F-PID is compared with conventional PD/PID controllers, and a type-1 fuzzy PID (T1-F-PID) controller.

Robust prescribed-time stabilization for fuzzy sliding mode synchronization for uncertain chaotic systems

This paper focuses on the problem of robust fuzzy sliding mode synchronization of uncertain nonlinear systems. A relevant fuzzy sliding mode control(FSMC) law is designed to force the state errors onto the specified surface. The prescribed-time boundedness (PTB) of the dynamical error is guaranteed by introducing the partitioning strategy. The chattering phenomenon, which is inherent to a conventional sliding mode control, is avoided by proper fuzzy tuning of the reaching law. The control law is deduced from the stability analysis of th closed-loop system in the sense of lyapunov. Furthermore, robustness issue is investigated and a prescribed-time property is obtained with sufficient conditions, established in terms of linear matrix inequalities (LMIs) using Lyapunov and cost functions. The results obtained show the effectiveness of the proposed control scheme and the reachability of the synchronization before the fixed time. The outcomes of this research are illustrated using computer simulations for the control problem of chaotic Chua’s system, in the presence of uncertainties, which clearly reveals that FSMC is better than SMC in terms of robustness. The present proposal provides a consistent procedure to design FSMC for a general class of nonlinear systems.

ALGORITHM OF ADJUSTMENT OF SUB-REGULATOR COEFFICIENTS WITH FUZZY LOGIC METHODS USING

The fuzzy PID tuning algorithm of proportional, integral, and derivative terms based on the fuzzy logic has been developed in the article. The fuzzy logic focuses on logic-linguistic models of representation of knowledges and it is the effective technology for creation of intellectual control systems of complex dynamic objects in the incomplete conditions.
 It has been researched that the fuzzy tuning algorithm of the basic coefficients for the PID controller сompared with the tuning algorithm based on the trial and error method, improved the quality of the of the transient process, namely reduces the overshoot and the settling time. Another advantage of the fuzzy PID tuning algorithm is that the PID controller coefficients can change their values ​​in accordance with changing conditions of operation control object, which improves the adaptive properties of the automatic control system.
 The structure of the automatic control system based on the fuzzy PID controller has been developed, the main components of which are the fuzzy PID controller and the transfer function of the control object. The fuzzy PID controller consists of the following main components: the fazzyfication unit, the rule base and the defazzyfication unit. This structure of the automatic control system does not require special means of identifying the parameters of the turboshaft engine and its implementation using modern microcontrollers is quite simple and affordable.
 The fuzzy PID tuning algorithm has been tested with using the parameters of the turboshaft engine ДГ90Л2 with power 16MW of the compressor 16 ГЦ2-395/53-76С of Dolyna linear production administration of gas transmittal pipelines.

Development of a control system with fuzzy tuning of values for the actuation setpoint

The article discusses the disadvantages of using fuzzy logic to correct the value of the PID controller coefficients in control systems. The disadvantages are related to the actuation setpoint, which knowingly doesn‘t provide optimal control during its implementation at the control object. It is proposed to use a fuzzy tuning of values for the actuation setpoint in the control system circuit based on the deviation between the measured values of the input action at the control object and the values accepted in the calculation by the mathematical model. Fuzzy tuning of values for the actuation setpoint provides that the control objects achieve the desired indicators without re-solving the optimization problem. The effectiveness of the proposed approach is demonstrated by the example of developing a control system for the electroplating process.

A new fuzzy strategy for size and topology optimization of truss structures

Most of the metaheuristic optimization techniques are the general optimization tools with the ability of solving various class of problems. However, in the case of more complex engineering problems, adding extra customizing procedure(s) can improve their performance. In this regard, the current study deals with introducing a new auxiliary fuzzy decision mechanism to enhance these methods’ capability on handling the structural size and topology optimization problems. The proposed mechanism aims to reduce the complexity level of the structural size and topology optimization problems by converting their complex search spaces into simpler fuzzy domains. Introduced fuzzy mechanism, during the optimization process, permanently monitors the population updating process and emphasizes either size or topology search behavior of each agent. Proposed mechanism evaluates agents via two predefined concepts so-called Normalized Objective Function (NOFi) and Normalized Members Density (NMDi). Since the presented fuzzy strategy designed as an independent regulator module, it can be integrated with different optimization algorithms. In the current work, it is combined with the Interactive Search Algorithm (ISA) optimization method and the compound method is named as Fuzzy Tuned Interactive Search Algorithm (FTISA). Eventually, its performance is comparatively assessed on solving a number of structural size and topology optimization problems with dynamic and static constraints. Achieved results demonstrate that the introduced fuzzy strategy not only significantly enhances the computational cost of the process but also improves the accuracy of the solutions and stability of the algorithm.