Fuzzy Control Technique Research Articles

Development of the intelligent traffic light system based on image processing and fuzzy control techniques

In Viet Nam's current traffic conditions, congestion and jams—especially at intersections during peak hours—present major challenges. Traditional traffic light systems, which rely on fixed timing principles, often fail to manage traffic flow efficiently, particularly when vehicle density varies significantly across different directions. This research aims to develop an intelligent traffic light system where the signal timings automatically adjust based on the vehicle density at intersections. The study uses an object recognition algorithm to identify, classify, and count vehicles. The data was then fed into a fuzzy logic model to calculate the optimal signal timings. Experimental results demonstrate an accuracy of approximately 88% in vehicle detection. The fuzzy logic model and the programmable logic controller were able to effectively compute reasonable signal timings based on real-time vehicle density. Future developments include expanding the system's functionalities, creating a user-friendly interface, and developing a management application for mobile devices.

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.

Adaptive Fuzzy Tracking Control With Global Prescribed-Time Prescribed Performance for Uncertain Strict-Feedback Nonlinear Systems.

For strict-feedback systems with mismatched uncertainties, adaptive fuzzy control techniques are developed to provide global prescribed performance with prescribed-time convergence. First, a class of prescribed-time prescribed performance functions are designed to quantify the performance constraints of the tracking error. Additionally, a novel error transformation function is provided to eliminate the initial value limitations and resolve the singularity issue in previous research. To ensure the convergence of the tracking error into a prescribed bounded region within a prescribed time and satisfactory transient performance, controllers with or without approximating structures are established. Notably, the settling time and initial condition of the prescribed performance function are completely independent of the initial tracking error and system parameters, thereby improving upon existing results. Furthermore, the disadvantage of the semi-global boundedness of tracking error induced by dynamic surface control can be eliminated through the use of a novel Lyapunov-like energy function. Finally, the effectiveness of the proposed strategies is validated through numerical simulations performed on practical examples.

Frequency control using fuzzy active disturbance rejection control and machine learning in a two‐area microgrid under cyberattacks

AbstractThere is a change in the traditional power system structure as a result of the increased incorporation of microgrids (MGs) into the grid. Multi‐area MGs will emerge as a result, and issues related to them will need to be addressed. Load frequency control (LFC) is a challenge in such structures, which are more complicated due to variations in demand and the stochastic characteristics of renewable energy sources. This paper presents a cascade fuzzy active disturbance rejection control technique to deal with the LFC problem. In order to tune different parameters of controllers, a newly developed heuristic algorithm called the Gazelle optimization algorithm (GOA) is also employed. Moreover, due to the fact that multi‐area MGs are regarded as cyber‐physical systems (CPSs), a relatively new concern for LFC problems is their resilience to cyberattacks such as false data injection (FDI) and denial of service (DoS) attacks. Therefore, this research also presents a novel machine learning approach called parallel attack resilience detection system (PARDS) to deal with the LFC problem in the presence of cyberattacks. The efficiency of the proposed strategy is investigated under different scenarios, such as non‐linearities in the power system or server cyberattacks.

Fuzzy-based thermal management control analysis of vehicle air conditioning system

Automotive air conditioning (ACC) system designers faced unique challenges in satisfying customer demands for efficient and comfortable operation across a broad temperature range. The AAC system's effective or fixed temperature setting makes this possible. Many climate control models, like the state flow switching controller in MATLAB control, must handle heating and cooling due to a single Simulink/environment software and frequently contain steady-state error (SSE). The present research proposes to design an automatic temperature control scheme and fuzzy logic control (FLC) for an automotive air-conditioned (AAC) system. In addition, it employs MATLAB Simulink/environment software to model the fuzzy control technique to analyze the AAC system's response. A simulation has been set up to evaluate the suggested system performance to match a selection of user-specified reference temperatures and compressor speeds. Compared with a PID-controlled AAC system, the proposed FLC-based system reduced the undershoot to only 2.30% from 33.30% respectively, and was robust, quicker, and better at controlling temperature.

Grid Tied Electric Vehicle Charging Station by Bidirectional PV Based High Gain Integrated DC-DC Converter

In the escalating concerns over carbon emissions from traditional fossil fuel vehicles, which contribute significantly to environmental degradation through phenomena like global warming and climate change, electric vehicles (EVs) have emerged as a compelling solution due to their emission free nature. This paper proposes the establishment of a grid connected electric vehicle charging station, powered by a cutting edge bidirectional PV based high-gain integrated DC-DC Cuk Landsman converter which shows 93.4 % efficiency, aimed at ensuring swift and efficient EV charging processes. To enhance dynamic performance, a Cascaded Fuzzy Controller (CFC) technique is implemented which shows 89.4 % accuracy. This technique enables rapid responses for error compensation, thereby improving the converter’s dynamic performance indices. The converter plays a pivotal role by adeptly converting the low and unregulated DC voltage generated by the PV system into a stable output at the requisite voltage level, all while attaining unity power factor and minimizing total harmonic distortion (THD). Grid synchronization is meticulously ensured through the implementation of an adaptive proportional integral (PI) controller for a single phase voltage source inverter (VSI), which meticulously compares reference power with actual power. This process ensures seamless integration with the grid, meeting stringent standards. Furthermore, an LC filter is deployed to effectively mitigate current harmonics around the switching frequency, ensuring compliance with grid regulations.

BLDC Motor speed control with dynamic adjustment of PID coefficients: Comparison of fuzzy and classic PID

Brushless DC (BLDC) motors, which have small volumes, are widely used in many areas from the aviation industry to industrial applications due to their high efficiency and torque. In parallel with the development of technology, the field of use continues to expand with the development of BLDC engine (BLDCM) control strategies and the decrease in control costs. In this thesis study, it is aimed to minimize the observed changes in rotor speed compared to the reference speed. To achieve this, PID parameters were tried to be changed simultaneously with fuzzy control techniques, taking the error value as a reference. The control system of the BLDC engine was designed in the MATLAB/Simulink environment. In the simulation, the operating stability of classical PID and PID with updated fuzzy-based parameters on two engines with the same features was compared at different speeds. As a result of the research, it was concluded that the correction of the speed observed in the rotor of the PID-controlled motor, whose fuzzy logic-based coefficients were updated, based on the reference speed was more stable and the percentage of exceedance for the reference value was lower, compared to the classical PID controlled motor.

Nonlinear control of two fingers model for movement coordination

This research introduces an innovative methodology for the integrated modeling, simulation, and analysis of two fingers, with particular emphasis on their fundamental roles in everyday tasks. In this study, we provide two nonlinear control strategies, specifically Sliding Mode Control (SMC) and Feedback Linearization Control (FLC), to achieve accurate and stable finger movements. As mentioned earlier, the controllers are utilized in the context of a biomechanical model consisting of two fingers, each possessing two degrees of freedom. These controllers enable the coordination of flexion and extension movements. The research conducted in our study emphasizes the coordinated regulation of finger movements, enabling the achievement of flexion through the utilization of two nonlinear controllers. By implementing these sophisticated control mechanisms, we can effectively showcase our model’s fidelity in adhering to the physiological limitations inherent to human fingers in their natural state. In addition, the proposed controllers demonstrate sound mitigation of non-linearities, such as load variations, different velocities, positional changes, and damping forces. This approach presents several advantages, such as handling non-linearities, guaranteeing robustness, choosing suitable parameters, and conducting comparative analysis. In order to substantiate our findings, we develop the nonlinear model utilizing the MATLAB/Simulink software. The findings of our study demonstrate effective regulation and control of the two-finger model’s position. In our study, we were able to get flexion angles of [Formula: see text] rad and [Formula: see text] rad using the sliding mode control (SMC) technique, and flexion angles of [Formula: see text] rad and [Formula: see text] rad using the fuzzy logic control (FLC) technique, all within a time frame of 5 s. These results serve to illustrate the applicability and significance of our proposed methodology.

Design of Intelligent Polyhouse with IOT

A polyhouse, often known as a greenhouse, is a building constructed for the controlled growing of plants. It is often built of translucent materials such as glass or plastic and is intended to offer a regulated temperature for plants, shielding them from harsh weather but allowing sunlight to pass through. The study considers various factors such as affordability and feasibility, combines IoT and fuzzy control techniques, uses GPRS for remote control, and creates a smart greenhouse monitoring system that is user-friendly, simple to operate, and performs well. Moreover, there are automated control mechanisms, such as greenhouse doors and windows that roll on and off in response to soil moisture levels. By using IoT to maintain precise parameters like CO2, soil moisture, temperature, and light in the greenhouse, the technology will help farmers increase productivity while reducing the need for physical field visits. An internet connection and an IoT kit are used for the task. The bell pepper plant's greenhouse conditions include CO2, soil moisture, temperature, and light

Design of PV, Battery, and Supercapacitor-Based Bidirectional DC-DC Converter Using Fuzzy Logic Controller for HESS in DC Microgrid

Renewable energy sources (RES) are becoming more popular globally as a reaction to critical energy concerns. Modern energy management technologies are used to maximize their efficiency while preserving the reliability of the grid. A hybrid energy storage system (HESS) connects to the DC microgrid through the bidirectional converter, allowing energy to be transferred among the battery and supercapacitor (SC). In this paper, a fuzzy logic control (FLC) technique is developed for PV-based DC microgrid systems that use both batteries and SCs. The proposed method uses the unbalanced energy from the battery pack to enhance the overall effectiveness of the HESS. The FLC approach is performed to validate under conditions of variable irradiance using MATLAB Simulink. When sudden changes in irradiance occur, the proposed FLC brings the voltage back to the desired level in terms of transient response like 33 ms settling times and 19% overshoot values. The results exhibit that the proposed method is more efficient in terms of time response, power output, increasing battery life, and ensuring a continuous supply of the PV system.

Robust H2 nonlinear fuzzy decentralized control design for a VSC‐HVDC link

SummaryA Robust Nonlinear Fuzzy Decentralized Controller ( RNFDC) is proposed to improve the overall robustness and tracking ability of a VSC‐HVDC link. The studied system is considered as composed by two overlapping nonlinear subsystems. The nonlinear interaction between the two subsystems is treated as a disturbance rejection problem by fuzzy control techniques. First, the Takagi Sugeno (TS) fuzzy model is adopted for fuzzy modeling of the uncertain nonlinear system. Next, new stability conditions for a generalized class of uncertain HVDC systems are derived from robust control techniques based on Linear Matrix Inequalities (LMIs). The design method employs the so‐called Parallel Distributed Compensation (PDC) to obtain RNFDC gains based on LMIs.The efficiency and robustness of the proposed controllers are analytically proven and tested through validation simulations. The main contributions of this paper are: (i) resilience: in case of failure of one converter or loss of measures or controls, the control of the other converter is not affected; (ii) robustness is improved in order to provide good responses in case of network variations and HVDC line parameters changes; (iii) fuzzy techniques are adopted to handle nonlinearities and changes of operating conditions. The proposed RNFDC is compared with Fuzzy H Decentralized State Feedback Control ( NFDC) and Linear Decentralized Control ( LDC) in simulation to illustrate the control synthesis and its effectiveness.

A new variable step fuzzy logic control technique for minimum emission point tracking of ship microgrid

A new variable step fuzzy logic control technique for minimum emission point tracking of ship microgrid

A Fuzzy Inference-Based TCSC Control Technique to Improve Dynamic Power System Responses

With the expansion of transmission systems, the devices that contribute to the overall power system performance must be adequate to the increasing modeling complexity and requirements. In this sense, the Flexible AC transmission system (FACTS) devices are often employed to improve stability and power quality, while the Thyristor-Controlled Series Compensator (TCSC) is a common example, able to change the equivalent transmission line impedance, improving power flow. This work discusses two established techniques for the control of TCSC devices based on the lead-lag model, whose parameters are defined through metaheuristic techniques, such as Genetic Algorithm and Particle Swarm Optimization. The results found in these publications are implemented in the Matlab/Simulink environment and confronted with the proposed Fuzzy logic application, written with linguistic inference, simplified rules, and simple membership functions. The two published models and the proposed Fuzzy logic performed satisfactorily with very similar results in all scenarios simulated considering a Single Machine Infinite Bus (SMIB) equivalent system model. The big advantage of the use of Fuzzy logic is its modeling simplicity, unlike the heuristic techniques that require much more modeling time and, sometimes, a large number of iterations to achieve acceptable parameters.

Optimization of X-axis servo drive performance using PSO fuzzy control technique for double-axis dicing saw

The dicing saw is a critical piece of equipment in IC processing, primarily used to cut wafers. Due to the high spindle speed, even small errors in the cutting process can result in wafer chipping or cracking. Therefore, the dicing saw requires a high degree of accuracy and stability. In this paper, the accuracy of the X-axis servo response was simulated using an Israeli ADT-8230 dual-axis abrasive wheel dicing saw. The study introduces a novel approach by using a fuzzy controller instead of the traditional position loop proportional integral (PI) controller. In addition, a two-input, two-output fuzzy rule is used for on-line correction of the position loop PI parameters. A heuristic algorithm is used to optimise the position loop fuzzy controller parameters. The quantization and proportionality factors are rectified using Particle Swarm Optimisation (PSO) algorithm and Genetic Algorithm (GA) respectively. By comparing the performance of the PSO fuzzy and GA fuzzy controllers, the optimal control method is derived. The proposed method is validated by simulation in the MATLAB/Simulink development environment using real ADT-8230 servo data. Experimental results show that the PSO-fuzzy structured controller reduces the position control error by 11.8%, improves the tracking performance by 26% and reduces the torque pulsation by 23%. Therefore, in future research, more advanced search algorithms should be further combined to improve the servo accuracy of the dicing saw.

An adaptive fuzzy control technique for a high‐speed vehicular platoon experiencing communication delays

AbstractVehicle platooning is seen as an enabler to improve driving safety, traffic congestion and stress‐free driving. A platoon is a highly non‐linear system and fuzzy control is able to deal with those systems. This paper presents a pioneering solution to the complex problem of vehicle platooning. Our adaptive controller seamlessly integrates fuzzy logic with conventional PID control technology, thereby ensuring safe and efficient travel for vehicles within a platoon. The key objectives of this controller encompass the maintenance of a consistent inter‐vehicle time gap and the consideration of time‐varying parameter uncertainties and communication delays between vehicles. It provides mathematical proof of the stability of the platoon formation using the Routh–Hurwitz theorem and a set of equations that describe the platoon's overall performance. Simulation results show that the proposed controller is effective in controlling vehicle platoons, even in the presence of random communication delays, resulting in improved driving safety and reduced traffic congestion.

Active Adaptive Observer-Based Fault-Tolerant Control Strategy for a Class of T–S Fuzzy Systems With Unmeasurable Premise Variables

This paper addresses the problem of fault-tolerant output tracking control for a class of Takagi-Sugeno (T-S) fuzzy systems with unmeasurable premise variables subject to additive and multiplicative actuator faults and external disturbances. In nominal conditions, utilizing a quadratic Lyapunov function and non-parallel distributed compensation (non-PDC) technique, the suggested strategy delivers linear matrix inequality (LMI)-based constraints. Simultaneously design of the proportional-integral (PI)-like state feedback controller and fuzzy anti-windup compensator is achieved with the aim of output tracking. In the faulty case, by considering the nominal system as a reference model, a direct adaptive projection-based approach is developed using the T-S fuzzy modeling and control techniques to supply the adaptive fault-tolerant controller components. An enhanced proportional-integral (PI) state/fault observer with unmeasurable premise variables is introduced only to provide the estimation of states to be used in the proposed controller. The overall closed-loop system ensures the uniformly ultimately bounded (u.u.b) solutions for error dynamics. Two examples, subsuming an inverted pendulum and a chaotic power system, have been used to present the merits and efficiency of the suggested approach persuasively.

Fixed-Time Prescribed Performance Consensus Control for Multiagent Systems With Nonaffine Faults

This paper studies the fixed-time consensus tracking control of nonlinear multi-agent systems (NNMASs) suffering from non-affine faults. A fixed-time command filter is constructed to solve the “explosion of complexity” issue, and a novel fixed-time error compensation mechanism is designed to eliminate filtering errors. The adaptive fuzzy control technique is introduced to deal with the unknown nonlinear terms containing non-affine fault functions. A new fixed-time fault-tolerant control scheme based on the modified prescribed performance technology is proposed for the NNMASs, which ensures that the closed-loop system satisfies the practical fixed-time stability. In addition, the consensus tracking errors of the NNMASs converge to a given range within a prescribed performance bound in a fixed time. Finally, comparative simulation results show the effectiveness of the proposed method.

Design of Adaptive Fuzzy Fixed-Time HOSM Controller Subject to Asymmetric Output Constraints

This paper is concerned with the control design of adaptive fuzzy fixed-time high-order sliding mode (HOSM) under asymmetric output constraints. The main objective of the paper is to construct a control scheme to build the HOSM in a fixed time and fulfill the pre-set constraint condition. For this purpose, initially to conquer this obstacle regarding the output constraint, a barrier Lyapunov function is developed to keep the output variable within the predefined constraint during operation. Then, the unknown functional bounds of uncertainties in the considered system are modeled by invoking fuzzy logic systems. With the integrated use of adaptive fuzzy control and backstepping-like techniques, a novel protocol for the fixed-time HOSM control with asymmetric output constraints is developed. Through the strictly theoretical clarification, the HOSM is fixed-time established under the suggested control strategy. The effectiveness of the derived results is eventually testified by some comparative simulation results.

Fault tolerant control of two tanks system using gain-scheduled type-2 fuzzy sliding mode controller

To save the robustness of type 2 fuzzy logic control technique and to avoid the high energy consumption that represents the sliding mode control (SMC) technique control technique, without failing the performance of the system, we propose a new fault tolerant control method based on gain-scheduled sliding mode control with interval type 2 fuzzy logic (FTCGST2FSMC) applied to the hydraulic system (two tanks system) with an actuator fault. The proposed control scheme avoids a difficult modeling, due to the chatter effect of the SMC, guarantees the stability studied by Lyapunov with the robustness of the system. The gains of the control with the SMC controller are modified and changed by an adaptation with a technique based on type 2 fuzzy logic, used to improve the gains of the controller when the fault is added, the proposed FTCGST2FSMC controller has been compared with the sliding mode controller. The results obtained confirm the robustness and the performance of this method, in the presence of the actuator fault effect.

Design and Fuzzy-PI Control of Power Converters for Use in Solar Energy Systems

With the developing technology and increasing population, the need for energy is increasing day by day. Solar energy systems have recently become an important research topic due to their potential to meet this need for energy. In this respect, in this study, a real-time application study was carried out on the design and control of all power converters in a solar energy system in order to supply the loads with the energy obtained from the solar energy systems in a healthy way. First, the design of DC/DC and DC/AC power converters in a solar energy system has been carried out. Then, these power converters were combined in a single system and a controller design was carried out using fuzzy logic-based PI control technique for the control of the system. In addition, fuzzy logic-based MPPT technique was used to obtain maximum efficiency from photovoltaic panels. The accuracy of the designed power system and the controller used in the control of this system has been demonstrated by the experimental results obtained with the application studies.