Intelligent Fuzzy Controller Research Articles

Cognitive intellignet control. Pt. 2: Quantum fuzzy inference algorithm in intelligent cognitive robotics

In the first part of the article, a system for assessing operator emotions using deep machine learning based on soft computing and designing a cognitive control system was discussed. This work develops the approach of cognitive intelligent control, describing the strategy for designing intelligent cognitive control systems based on quantum and soft computing. The synergetic effect of quantum self-organization of the knowledge base, extracted from non-robust knowledge bases of an intelligent fuzzy controller, is demonstrated. The information-thermodynamic law of quantum self-organization of the optimal distribution of the basic qualities of control (stability, controllability and robustness) and the law of quantum information thermodynamics on the possibility of extracting additional useful work based on the extracted quantum information hidden in classical states are applied. Formed (without violating the second law of quantum thermodynamics) on the basis of the extracted amount of hidden quantum information, the «thermodynamic» control force allows the robot (as a control object) to perform quantitatively more useful work compared to the amount of work expended (on extracting quantum hidden information). The guaranteed achievement of the goal of controlling the robot is carried out on the basis of the designed intelligent cognitive con-trol system using the tools of the QCOptKBTM quantum knowledge base optimizer, the structure of which includes quantum fuzzy inference (QFI). The quantum algorithm for self-organization of non-robust knowledge bases of QFI structurally relies on synergistic effects from hidden quantum information to implement the implementation of the optimal distribution of control qualities. This technology makes it possible to increase the reliability of intelligent cognitive control systems in situations of control under conditions of danger, described using a cognitive neural interface and various types of interaction with robots. The examples have demonstrated the effectiveness of intro-ducing the QFI scheme as a ready-made programmable algorithmic solution for embedded intelligent control sys-tems. The possibility of using a neural interface based on a cognitive helmet with a quantum fuzzy controller to control a vehicle is shown.

Ultimately Bounded TSK Fuzzy Management in Urban Traffic Stream Mechanism: Multi-Agent Modeling Approach

In this paper, a novel control approach based on the selection of the type of traffic light and the period of the green phase to accomplish an optimum balance at intersections is proposed. This balance should be flexible to the static behavior of time, and randomness in a traffic situation, which the goal of the proposed method is to reduce traffic volume in transportation, the average delay for each vehicle, and control over the crash of cars. The proposed method was specifically investigated at the intersection through an appropriate timing of traffic lights by sampling a multi-agent system. It consists of a large numbers of intersections, each of which is considered as an independent agent that exchanges information with each other, and the stability of each agent is provided separately. The robustness against uncertainties, scalability, and stability of the closed-loop overall system are the main merits of the proposed methodology. The simulation results show that the fuzzy intelligent controller in this multi-factor system which is a TS fuzzy is more useful than scheduling in the fixed-time method and it reduces the lengths of vehicles queuing.

Design of an intelligent fuzzy controller optimised using extended grey wolf algorithm to handle chaos in the industrial gear system

Chaos is an unpredictable phenomenon that has received attention in nonlinear dynamic systems. Empirical investigations of the gear’s dynamic response demonstrate the existence of chaos and bifurcation in this system. Chaotic behaviour is an unfavourable phenomenon in the vibrations of gear systems. Thus, designing a smooth and optimal gear system, controlling or eliminating this chaotic behaviour is of great importance. Therefore, this paper presents an improved fuzzy control using the extended grey wolf optimiser to control the chaotic behaviour of a gear transmission system. To evaluate this method, the results are studied in the presence and absence of white noise. Finally, the proposed method is compared with adaptive sliding mode control, indicating the superiority of the proposed method in minimising the square error and increasing the speed of eliminating chaotic behaviour.

An Experimental Testing of Optimized Fuzzy Logic‐Based MPPT for a Standalone PV System Using Genetic Algorithms

The choice and the dimensioning of the controller for the maximum power point tracking (MPPT) are determined for the ideal energy efficiency of the photovoltaic (PV) systems. Many works have been developed in the field of MPPT methods, especially fuzzy logic controllers. However, these are robust if the parameters of the membership functions have been well designed. In this paper, the performances of an intelligent fuzzy logic controller (FLC)‐based MPPT method have been optimized by an evolutionary genetic algorithm (GA). The works presented in the literature have shown the efficiency of the proposed method compared to classical methods. In our paper, the validation of the experimental results obtained is given with respect to a reference signal. The control of the simulated PV source and the proposed method are built using the Simulink/Matlab environment and implemented on the dSPACE DS1104 controller to validate the practical execution of the suggested method. The standalone PV system has been tested in an emulated test bench experimentation. Experimental results confirm the efficiency of the proposed method and its high accuracy in handling fast varying load conditions.

A Novel Fuzzy-SAE Control Method for an Improved Test Wind Tunnel Simulating Sand/Dust Environment

The sand/dust environment is an important cause of aircraft failure. A sand/dust environment simulation experiment must be devised to meet the standard technical requirements. Therefore, this article designs the control system for a sand/dust environment test tunnel, including a wind speed control system and a pneumatic conveying and concentration control system. A fuzzy intelligent control method and a deep neural network are used to track and control experimental parameters. Compared to the classic PID algorithm, this method achieves smaller overshoot, faster response speed, no steady error and a better dynamic response curve, as demonstrated by both the test result in the wind tunnel and a simulation result. Both the classic PID control method and the high-precision fuzzy control method are fast, stable, and robust. The fuzzy-SAE intelligent control method not only has the high accuracy of the classic PID control method but also has the high speed, stability, and robustness of fuzzy control, which can meet the intelligent control requirements of the sand/dust environment test equipment.

A Multi-Objective Fuzzy Optimization Model for Electricity Generation and Consumption Management in a Micro Smart Grid

This manuscript proposes an intelligent supply and demand management system in a complete network of electricity production and consumption. A micro smart grid (MSG), which includes a solar cell, a wind turbine, a diesel generator, and battery storage system capable of trading energy with the smart gride (SG), connected to smart buildings with different types of loads is modelled. Different types of intelligent fuzzy controllers for distributed management were proposed and optimized via the non-dominated sorting genetic algorithm-II (NSGAII), which is a multi-objective optimization method. Maximum user comfort, the amount of renewable energy employment, minimum total power consumption cost, total energy consumption at peak time, and MSG loss of power supply probability are the five objective functions of the optimization process. Various uncertainties of the real world have also been considered. The most crucial distinguishing feature of this proposed method is the design of controllers to manage the demand and supply of electricity without the need for daily optimization. Comparison experiments with other methods presented in the field of electricity supply and demand management are conducted to show the superiority of this method in terms of optimality of the results, low processing volume required to implement real controllers, and its resilience to changing conditions.

Experimental validation and intelligent control of a stand-alone solar energy conversion system using dSPACE platform

This paper presents the performances of an artificial intelligent fuzzy logic controller (FLC) based maximum power point tracking (MPPT) and a conventional perturb and observe (P&O) based MPPT controller is presented for a stand-alone PV system and tested in an emulated test bench experimentation. The studied system is composed of a DC power supply emulating the PV panel, a DC/DC boost converter, a variable resistive load and a real-time MPPT controller implemented in the dSPACE DS1104 controller. To verify the performance of the FLC proposed, several simulations have been performed in Matlab/Simulink environment. The proposed method outperforms the P&O method in terms of global search capability and dynamic performance, according to the comparison with the P&O method. To verify the practical implementation of the proposed method, the control of the emulated PV source and the MPPT algorithms are designed using the simulink/Matlab environment and implemented on dSPACE DS1104 controller. Experimental results confirm the efficiency of the proposed method and its high accuracy to handle the resistance varying.

Adaptive control and supply chain management of intelligent agricultural greenhouse by intelligent fuzzy auxiliary cognitive system

AbstractThis work aims to avoid the complex control process caused by complex coupling and serious interference of control variables in traditional greenhouse environmental control and achieve the adaptive control of agricultural greenhouse. This work studies smart agricultural greenhouses' adaptive control and supply chain management based on the intelligent fuzzy auxiliary cognitive system (IFACS). This work firstly expresses the intelligent fuzzy control theory and analyses data according to the fuzzy control set. Secondly, the adaptive control and supply chain management of the intelligent agricultural greenhouse are studied by using an intelligent fuzzy aided cognitive system. Finally, the light intensity and carbon dioxide content of the intelligent agricultural greenhouse are analysed; the temperature and humidity content of the intelligent agricultural greenhouse of the traditional control method and adaptive control are compared; the impact of carbon dioxide emissions on the environment under supply chain management is analysed. The research results indicate that the adaptive control of the intelligent agricultural greenhouse based on the IFACS can keep the greenhouse temperature within a specific range and reduce the impact of temperature on the development of agricultural greenhouses. The IFACS is effectively managed in the adaptive control of the intelligent agricultural greenhouse. From 2015 to 2020, the smart agricultural greenhouse supply chain management gradually reduced the content of greenhouse gas (GHG) emitted by the production and suppliers through the IFACS. The content of greenhouse gases emitted by product transporters did not change significantly, and the greenhouse gases emitted by non‐manufacturing suppliers gradually decreased. In 2020, supply chain GHG emission intensity decreased by 20% compared with 2015, and GHG emissions from product transportation also decreased by 6% compared with the previous year. This research has reference significance for the adaptive control of intelligent agricultural greenhouse by IFACS and the environmental control of agricultural greenhouse by supply chain management.

Numerical Simulation of Intelligent Fuzzy Closed-Loop Control Method for Radial–Axial Ring Rolling Process of Super-Large Rings

During the radial–axial ring rolling (RARR) process of super-large rings, abnormal deformation states such as instability and out of circularity often lead to rolling termination and quality fluctuation of ring products. In this work, an intelligent fuzzy closed-loop control method for RARR process of super-large rings is proposed, i.e., the ring’s offset adaptive fuzzy control (ROAFC) based on the regulation of the axial roll’s rotational speed and the ring’s circularity fuzzy control (RCFC) based on the regulation of the mandrel’s feed speed. In addition, a recursive average filtering algorithm is added to smooth the axial roll’s rotational speed and the mandrel’s feed speed according to the actual situation. Using the ABAQUS/Explicit software and its subroutine VUAMP, the intelligent fuzzy controller of the ring’s offset and circularity in the RARR process is designed, and the finite element (FE) model for RARR process of a Φ10 m super-large ring with an integrated intelligent fuzzy control algorithm is established. The variation laws of the ring’s offset and circularity error in the RARR process are studied with regard to different control methods such as conventional planning control (CPC), ROAFC, RCFC, and comprehensive control of ROAFC combined with RCFC (ROAFC + RCFC). The results obtained show that, compared with the CPC, the ring’s offset is reduced by 84.6% and the circularity error is decreased by 51.9% in the RARR process utilizing comprehensive control of ROAFC + RCFC. The research results provide methodological guidance for realizing the intelligent forming of super-large rings.

SYNTHESIS OF A FUZZY INTELLIGENT CONTROL SYSTEM FOR A DYNAMIC OBJECT BASED ON FUZZY LOGIC

This article discusses the synthesis of a fuzzy dynamic object-driven intelligent control system using a fuzzy mathematical apparatus. If the exact description of the control object is rather complicated and there is no a priori information about the operation of the system, it is considered more important to apply fuzzy intelligent control systems to control objects. Unlike classical models, fuzzy models are more flexible and allow taking into account the experience and intuition of specialists in a particular field. The fuzzy model of control and diagnostics is more adequate to the simulated reality, and the correct solution is obtained due to the accuracy of the initial data. Operating principle A fuzzy model of a fuzzy intelligent control system based on a fuzzy logical statement of the type "If A..., then B" has been created.

Intelligent fuzzy control strategy for battery energy storage system considering frequency support, SoC management, and C-rate protection

The penetration of renewable energy resources (RERs) in modern power systems has a significant impact on system frequency. Battery energy storage systems (BESSs) can play a key role to regulate the frequency and improve the system stability considering the low inertia nature of inverter-based DGs. This paper proposes an optimal control strategy based on fuzzy logic control (FLC) to support the microgrid (MG) frequency. In addition to frequency regulation, this strategy includes supplementary control units to protect the battery and manage the battery state of charge (SoC). The first unit protects the battery against high C-rate permanent current which increases battery state of health (SoH) and lifetime. The second unit manages the SoC to increase the BESS availability in transient events and to avoid the negative impact of battery sudden interruption. Moreover, a dynamic objective function in time-domain simulation has been established using DIgSILENT Power Factory which ensures frequency stability improvement and smooth response simultaneously, and particle swarm optimization (PSO) algorithm is used to obtain the optimal parameters of the proposed FLC. To evaluate the stability of the system, the eigenvalue analysis has been employed and the permissible ranges of FLC parameters have been obtained, which were used as acceptable ranges in the optimization process. Finally, the simulation results verified the effectiveness of the proposed controller to regulate the frequency and to enhance the dynamic performance of the MG.

Simultaneous active control of tower lateral vibration and power control of wind turbine: A novel multivariable approach

Lateral vibrations of the tower of a horizontal axis wind turbine is lightly damped and is vulnerable to induced vibration loads from wind. Such induced vibration load can decrease the fatigue life and increase the maintenance costs. Active vibration control using generator torque has been studied in previous research works but, there is a trade-off between reducing the lateral oscillations of the tower and fluctuations of output power, since any fluctuations in generator torque, in turn, causes fluctuations in the output power In this research, we illustrate a way that actively controls the lateral vibrations of the tower by using the generator torque but also simultaneously reduces fluctuations in the output power. We first, introduced a novel intelligent fuzzy torque control by measuring the lateral displacements of the tower and would receive feedback in the form of rotor speed error and its time derivative. On the basis of using this intelligence torque controller, we then introduced three-pitch control strategies to reduce output power fluctuations by considering the sliding mode theory. The simulation results demonstrate that the second strategy is extremely effective for reducing both lateral tower displacement and output power fluctuation.

Fuzzy Logic-Based Load Frequency Control in an Island Hybrid Power System Model Using Artificial Bee Colony Optimization

This study presents the implementation of Artificial Bee Colony (ABC) optimization in an island hybrid power system model using fuzzy logic-based load frequency control. The Island Hybrid Power System considered in this study consisted of various generation units and an energy storage system. The optimized control parameters of PID using ABC were used in an intelligent fuzzy logic controller. The profiles (power & Frequency) of isolated hybrid power system were improved using a Super Conducting Magnetic Energy Storage (SMES) System. Individual controllers were used for wind turbine and diesel generators to control the power output for balancing the demand (frequency change control). Comparative analysis of power and frequency with the help of various classical and intelligent control configurations is presented. The outcome of the study shows that a minimum deviation in frequency and power is obtained through the proposed Intelligent Fuzzy Control approach for the considered isolated power system model.

Rule-Based Fuzzy Controller for Solid State Transfer Switch Towards Fast Sensitive Loads Transfer

This article deals with the development of an intelligent fuzzy logic controller for solid-state transfer switch toward fast fault detection and power transfer to the alternative feeder. A set of fuzzy rules are developed for the controller which in turn shift the loads from the preferred feeder to the alternate feeder with quality power resolving fault interruption or voltage sags. The voltage and the current control loop generate a signal to the fuzzy control block to provide an on or off pulse for the switches upon activate fuzzy rules in the interference engine. In this article, a compilation of 35%, 50%, 70%, and 100% faults are created to demonstrate the effectiveness of the proposed fuzzy controller toward detecting fault levels and transferring power to the desired feeder. It was discovered that the proposed fuzzy logic controller detects faults in the range of 0.054 to 0.061 milliseconds and transfers power in the range of 1.742 to 3.107 ms, which is significantly faster and more reliable than the existing literature.

Water-Cycle-Algorithm-Tuned Intelligent Fuzzy Controller for Stability of Multi-Area Multi-Fuel Power System with Time Delays

In this paper, a fuzzy (F) proportional (P)–integral (I)–derivative (D) (PID) (FPID) controller optimized with a water cycle algorithm is proposed for load frequency control of a multi-area multi-fuel (MAMF) power system. The MAMF system has the realistic feature of communication time delays (CTDs), in order to conduct an analysis nearer to realistic practice. Initially, the MAMF system is analyzed when subjected to a step load disturbance (SLD) of 10% on area 1. The superiority of the fuzzy PID controller is revealed upon comparing it with PID plus double derivative (DD) (PIDD) and PID controllers. The MAMF system is investigated with and without CTDs, to demonstrate their impact on system performance. Later, an additional HVDC line is incorporated in parallel with the existing AC line for further enhancement of the system performance. Finally, the MAMF system is targeted with random loading to validate the robustness of the presented control scheme.

Intelligent Fuzzy Control in Stabilizing Solar Sail with Individually Controllable Elements

Fuzzy logical control is a robust and effective control method in industrial fields, which renders it applicable to the attitude control of a solar sail. However, it is hard to apply in black-box and time-varying problem as real solar sail attitude control. Considering the lack of a priori knowledge and the unacceptable manual workload in the design of the fuzzy logical controller (FLC), an intelligent FLC designer (IFLCD) is developed by introducing neural network modelling and automatic design method. Besides, IFLCD also supports self-adaption for better control accuracy. By applying the proposed IFLCD in the attitude stabilization of a solar sail with individually controllable elements (SSICE), an effective solution of unmanned, time-varying, and complex system control method is offered without any mathematical model, which also overcomes the difficulties in FLC design Considering the performance degradation, accident, and distance problems faced by spacecraft, IFLCD can help with more practical problems that are hard be solved by traditional control theory.

Hybrid Renewable Energy Source Combined Dynamic Voltage Restorer for Power Quality Improvement

In this paper, the hybrid photovoltaic-thermoelectric generator (PV-TEG) combined dynamic voltage restorer (DVR) system is proposed for the power quality disturbances compensation in a single-phase distribution system. The stable and precise level of input voltage is essential for the smooth and trouble-free operation of the electrically sensitive loads which are connected at the utility side to avoid system malfunctions. In this context, the hybrid PV-TEG energy module combined DVR system is proposed in this paper. With the support of the hybrid energy module, the DVR will perform the power quality disturbances compensation effectively with needed voltage and /or power. In the proposed system, the PV and TEG energy sources are connected electrically in series to produce adequate voltage for the DVR operation and the fractional factor-based variable incremental conduction (FFVINC) maximum power point tracking (MPPT) control algorithm is employed to extract the possible maximum power from the PV array. The intelligent fuzzy logic controller (FLC) is chosen for implementing the MPPT control algorithm. The half-bridge voltage source inverter (VSI) circuit and in-phase voltage compensation technique are used in the DVR for better power quality disturbances compensation. The performance and usefulness of the proposed DVR system are investigated by an extensive simulation study with four different modes of operation, the study results are confirmed that the proposed system promptly identifies the power quality disturbances for compensation. Moreover, the investigation proved that the combined PV and TEG energy module can provide better energy efficiency in converting solar irradiation into electricity.

Fuzzy control of active suspension system based on quarter car model

In this paper, the performance of model-free intelligent Fuzzy Controller analyzed with the model-based conventional controller concerning body displacement and body acceleration. The development of a mathematical model for active suspension is quite difficult which increases the difficulty of designing a model-based controller for active suspension. So, the model-free intelligent adaptive fuzzy controller has been proposed for active suspension. The Fuzzy Controller predicts and controls the performance of active suspension better than a conventional controller. The results obtained from the simulation of the road profile show that the proposed fuzzy control performs better than the conventional controller in terms of body displacement and body acceleration.

Gear Shift Coordinated Control Strategy Based on Motor Rotary Velocity Regulation for a Novel Hybrid Electric Vehicle

This paper proposes a novel hybrid power system to improve the shift quality of a hybrid electric vehicle (HEV). After selecting a typical shift scheme, the study focused on the motor rotary velocity control algorithm and coordinated control strategy for the motor and clutch. The effects of various control algorithms on different target rotary velocities were analyzed, and a proportional-integral-derivative (PID)–bang-bang–fuzzy compound intelligent algorithm for a motor rotary velocity control system was investigated. In addition, to address the problems of the long synchronizing time required for the rotary velocity and large sliding friction work, which affect the shift quality during the process of engaging the clutch, a coordinated control strategy for the motor rotary velocity and clutch oil pressure was investigated. The research results showed that, compared with a gear shift coordinated control strategy based on a PID control algorithm, the strategy based on the PID–bang-bang–fuzzy compound intelligent control algorithm proposed here reduced the shift time and clutch slipping friction work by 35.7% and 19.2%, respectively.

Traffic Control Prediction Design Based on Fuzzy Logic and Lyapunov Approaches to Improve the Performance of Road Intersection

Due to the increasing use of private cars for urbanization and urban transport, the travel time of urban transportation is increasing. People spend a lot of time in the streets, and the queue length of waiting increases accordingly; this has direct effects on fuel consumption too. Traffic flow forecasts and traffic light schedules were studied separately in the urban traffic system. This paper presents a new stable TS (Takagi–Sugeno) fuzzy controller for urban traffic. The state-space dynamics are utilized to formulate both the vehicle’s average waiting time at an isolated intersection and the length of queues. A fuzzy intelligent controller is designed for light control based upon the length of the queue, and eventually, the system’s stability is proved using the Lyapunov theorem. Moreover, the input variables are the length of queue and number of input or output vehicles from each lane. The simulation results describe the appearance of the proposed controller. An illustrative example is also given to show the proposed method’s effectiveness; the suggested method is more efficient than both the conventional fuzzy traffic controllers and the fixed time controller.