Fuzzy Control Model Research Articles

Electric Bike Range Estimation using Fuzzy Logic Controller

Electric Vehicles (EV) are the prompt solution to significantly lowering the use of fossil fuels and CO2 emissions from the transport industry. There is a continuing growth in the number of EVs in use, but their huge acceptance by customers is associated to the quality they can provide. Nowadays, different types of electric vehicles are moving toward green awareness, and one among them is the electric motorcycle which is a considerable vehicle in India. Though there are many benefits of driving electric motorcycles, because of the limited driving range and inadequate charging stations, it is still not generally accepted in the industry. Range anxiety is the major market concern that is solved by the implementation of an additional range estimating technique that can ease the "range anxiety" caused by the restricted range of EVs. Therefore, this paper proposes a fuzzy logic controller model for the estimation of the EV range based on the battery's state of charge and the load's power usage. In this work, the load power consumption of the vehicle and the status of the battery charge are selected as inputs and the EV range is selected as a Fuzzy Logic Controller output. This model is implemented in the Matlab/Simulink environment.

Investigation of type 1 and type 2 fuzzy logic controllers performance: application of speed control of BLDC motor

In this paper, a simulation study enhanced to model that the speed control of brushless direct current (BLDC) motors used in electric vehicles with intelligent control methods. The simulation study was prepared in Matlab/Simulink environment. The first control method is Type-1 fuzzy logic control (T1FLC), and the second control method is the Intermittent Type-2 fuzzy logic control (IT2FLC) model. Membership functions for different membership numbers have been created for both types of FLC models. These are 3×3, 5×5, 7×7. Control methods are prepared in Matlab/M-file environment. The model is defined as the input variable of the error, which is the difference between the reference speed and the motor speed, and the output variable of the Pulse Width Modulation (PWM) signal applied to the motor. The simulation study maintains the speed of the BLDC motor up to the reference speed with T1FLC and IT2FLC controllers, depending on the reference speed and applied load values. Depending on the number of different memberships, the effects of controller performances on the control of motor speed have been observed. The graphs and findings of the experiment are shown in the results and discussion section.

Design of a deviation detection sensor and an auto-follow row system for corn harvesters

The auto-follow row technology is the basic technology to realize the uncrewed operation of corn harvesters, which helps to improve the quality and efficiency of corn harvest. A deviation detection sensor and an auto-follow row system are designed to address the difficulty for corn harvesters to harvest the corn sowing with different row spacing. The auto-follow row system is comprised of the auto-follow row controller, two deviation detection sensors, sensors for acquiring the state of the corn harvester, and the hydraulic steering system. The deviation detection sensor uses the touch rod to detect the lateral deviation and sends it to the auto-follow row controller through the Controller Area Network (CAN) bus. The auto-follow row controller obtained the lateral deviation, calculated the target steering angle according to the pure pursuit method based on the yaw angle estimation model and fuzzy control model, and adjusted the posture of the harvester by controlling the steering actuator. Two types of corn harvesters were reformed and mounted the auto-follow row system for simulation and field tests. The average errors of the auto-follow row system in the simulation tests and the field tests were 0.040 m and 0.046 m, respectively. The relative deviations within ±0.075 m were 98.33% and 97.22%, respectively, which met the operating requirements of corn harvesters. The test results show that the auto-follow row system can be applied to many kinds of corn harvesters and provide a reference for designing a completely unmanned system for corn harvesters.

Membership Function, Time Delay-Dependent $\eta$-Exponential Stabilization of the Positive Discrete-Time Polynomial Fuzzy Model Control System

This article investigates the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\eta$</tex-math></inline-formula> -exponential stabilization and positivity of the controlled discrete-time polynomial fuzzy model (DPFM) system with time delay. <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\eta$</tex-math></inline-formula> -exponential polynomial copositive Lyapunov candidate is proposed to detect the system stability under the convergence (decay) rate. Static output feedback fuzzy controller is then synthesized to assure the DPFM closed-loop system with time delay. We propose Chebyshev membership functions (CMFs) to approach the primary membership function and attenuate the conservativeness of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\eta$</tex-math></inline-formula> -exponential stability formulation and positivity analysis. Chebyshev norm approximation error is utilized to introduce the error between CMFs and primary membership functions using slack matrices. A numerical example is presented to validate the proposed methods.

Joint Modeling of a Fuzzy Two-Connected Longitudinal Turning Control System in MSC. Adams and Matlab

Purpose of research. The article considers the possibility of developing and modeling a fuzzy bipartite control system of the turning process based on the joint application of the program for creating virtual models MSC.Adams and mathematical analysis package Matlab. The relevance of the research topic is related to the trend of application of artificial intelligence functions in domestic and foreign machine tool industry for compensation of temperature deformations, force and vibration disturbances, tool condition monitoring, adaptive control taking into account the actual state of the cutting process in real time. In designing new control systems of the turning process, an important task is to create mathematical and virtual models and synthesis of intelligent control algorithms, providing solutions to problems under conditions of uncertain perturbations.Methods. For the development of the mathematical model and its analysis basics of the theory of fuzzy sets in the problems of control, theory of metalworking, methods of mathematical modeling of control systems have been applied. The synthesis of fuzzy controller and virtual model have been developed with the help of modern applied software packages Matlab and MSC.Adams.Results. The fuzzy cutting control algorithm and its implementation in the Simulink environment with the data transfer to MSC.Adams, as well as the virtual prototype of the lathe in MSC.Adams are presented in the article. Graphs of the vibration movements of the cutting edge of the tool, changes in temperature and cutting force are given and analyzed.Conclusion. The results of model testing show that the use of joint modeling of fuzzy two-connected turning control system is possible to solve the problem of improving the efficiency of machining on the operating equipment under the influence of uncertain disturbances.

Driving support by type-2 fuzzy logic control model

Advanced models of Artificial Intelligence enable systems of IoT to work with great flexibility to the needs of users. In this article we present our developed IoT system for driving support by the use of type-2 fuzzy logic control module. We have developed the IoT system to collect the data about driving conditions and evaluate them adjusting to the needs of user. Applied module of fuzzy logic of the second type was used in analysis of accelerometers signals to flexibly adjust to uncertainty of evaluation of driving expectations of each driver. Our developed system was tested in different cars by driving on various roads and results show excellent efficiency.

A Novel Decision Support Methodology for Autonomous Collision Avoidance Based on Deduction of Manoeuvring Process

In the last few years, autonomous ships have attracted increasing attention in the maritime industry. Autonomous ships with an autonomous collision avoidance capability are the development trend for future ships. In this study, a ship manoeuvring process deduction-based dynamic adaptive autonomous collision avoidance decision support method for autonomous ships is presented. Firstly, the dynamic motion parameters of the own ship relative to the target ship are calculated by using the dynamic mathematical model. Then the fuzzy set theory is adopted to construct collision risk models, which combine the spatial collision risk index (SCRI) and time collision risk index (TCRI) in different encountered situations. After that, the ship movement model and fuzzy adaptive PID method are used to derive the ships’ manoeuvre motion process. On this basis, the feasible avoidance range and the optimal steering angle for ship collision avoidance are calculated by deducting the manoeuvring process and the modified velocity obstacle (VO) method. Moreover, to address the issue of resuming sailing after the ship collision avoidance is completed, the Line of Sight (LOS) guidance system is adopted to resume normal navigation for the own ship in this study. Finally, the dynamic adaptive autonomous collision avoidance model is developed by combining the ship movement model, the fuzzy adaptive PID control model, the modified VO method and the resume-sailing model. The results of the simulation show that the proposed methodology can effectively avoid collisions between the own ship and the moving TSs for situations involving two or multiple ships, and the own ship can resume its original route after collision avoidance is completed. Additionally, it is also proved that this method can be applied to complex situations with various encountered ships, and it exhibits excellent adaptability and effectiveness when encountering multiple objects and complex situations.

Problems of constructing logical-linguistic models of complex management dynamic objects

Objective. The aim of the study is to solve the problems of constructing logicallinguistic (fuzzy) models for controlling the trajectory movement of complex dynamic objects.Method. To build logical-linguistic (fuzzy) control models, theories of fuzzy sets and algorithms of L. Zadeh were used.Result. The process of constructing logical-linguistic (fuzzy) models for controlling the trajectory movement of complex dynamic objects using the theory of fuzzy sets and fuzzy algorithms is given. A marine mobile object (MMO) operating in an uncertain environment was used as a control object in the work. It is shown that logical-linguistic (fuzzy) MPO control models can be built taking into account the rich practical experience of a navigator who does not have special knowledge, expressed by them in a qualitative form for building fuzzy control models.Conclusion. Logicallinguistic control models make it possible to ensure the movement of a marine moving object along a given trajectory with a quality of the controlled process sufficient for practical purposes under conditions of various external disturbances and drift of the parameters of the controlled object. The use of fuzzy control algorithms can significantly reduce the cost of computer time compared to the optimal search algorithm with an acceptable deterioration in the quality of the process and ensure the implementation of the control process in real time.

Real-Time Travel Time Prediction Based on Evolving Fuzzy Participatory Learning Model

Urban expressways take on rapid and external transport in the city due to their fast, safe, and large capacity. Implementing intelligent and active traffic control can effectively improve the performance of urban traffic and mitigate the urban traffic congestion problem. Real-time traffic guidance is one critical way of intelligent active traffic control, and travel time is the most important input for real-time traffic guidance. We employed and improved a machine learning method called the evolving fuzzy participatory learning (ePL) model to predict the freeway travel time online in this paper. The ePL model has a promising nonlinear mapping potential, which is well suitable for the traffic prediction. We used generalized recursive least square (GRLS) to improve the estimation accuracy of the model’s parameters. This model is a fuzzy control model. Its output is the forecasting result which is also the fuzzy reasoning result. We tested this model by comparing it to other travel time prediction approaches, with the freeway data from the Caltrans Performance Measurement System. The results from the improved ePL model showed mean absolute error of 5.941 seconds, mean absolute percentage error of 1.316%, and root mean square error of 10.923 s. The performances are better than those of the baseline models including ARIMA and BPN. This model can be used to predict the travel time in the field to be used for active traffic control and traffic guidance.

A path planning control for a vessel dynamic positioning system based on robust adaptive fuzzy strategy

The thrusters and propulsion propellers systems, as well as the operating situations, are all well-known nonlinearities which are caused less accuracy of the dynamic positioning system (DPS) of vessels in the path planning control process. In this study, to enhance the robust performance of the DPS, we proposed a robust adaptive fuzzy control model to reduce the effect of uncertainty problems and disturbances on the DPS. Firstly, the adaptive fuzzy controller with adaptive law is designed to adjust the membership function of the fuzzy controller to minimize the error in path planning control of the vessel. Secondly, the performance of robust tracking is proved by the Lyapunov theory. Moreover, compared to the other controller, a simulation experiment comprising two case studies confirmed the efficiency of the approach. Finally, the results showed that the proposed controller reaches control quality, performance and stability.

Application of Fuzzy PID Position Control Algorithm in Motion Control System Design of Palletizing Robot

In view of the low efficiency in traditional palletizing robot problem of poor control precision, this paper introduces fuzzy PID position control algorithm, based on the actual operation situation of palletizing robot; determined as palletizing robot FPGA hardware platform, hardware platform based on this fuzzy PID position control algorithm is applied to implement palletizing robot motion control system design. The simulation model of fuzzy PID motion control was established by MATLAB software for testing to determine that the fuzzy PID position control algorithm reflects the time quickly in the motion control of palletizing robot, and the actual overshooting is small, which is more suitable for the motion control algorithm of palletizing robot. Under this condition, the modular method is adopted to complete the system application design on the FPGA hardware platform.

Study on application of fuzzy control model in phosphate removal system of sewage water

The precise input of flocculant and coagulant aid into phosphate wastewater is a key problem to degrade the phosphate content in sewage water and make the total phosphates and suspended pollutants in sewage water reach the discharged standard. In this paper, a new method combining fuzzy control technology with wastewater treatment control system was proposed to establish an automatic control system suitable for phosphates removal treatment. Based on experiment data and current experience, the membership function and fuzzy rules were set up, and the fuzzy control technology was obtained with simulation analysis, and the efficient treatment of sewage water was realized. It can be seen from the results that the fuzzy control technology can be applied to the phosphate removal control system for treating sewage water, and the standard of stable, accurate and efficient sewage treatment system can be established.

Energy Saving Control of Integrated Energy Multi-Energy Complementary Coupling Space Heating System Based on Fuzzy Theory

Because the traditional solar-ground source heat pump heating system uses temperature difference controllers to control most, it causes the problem of large energy consumption of the equipment. In order to improve such problems, the complexity of the space heating control system is considered comprehensively, and the fuzzy strategy is used to improve the previous control system. TRNSYS was used to build a heating system simulation model, MATLAB was used to build a solar heating system fuzzy controller model, and the two models were coupled to jointly simulate the internal temperature change and energy consumption of the space under the same environmental conditions and different controllers. Experiments show that under the design conditions, the solar collector is greatly affected by the light, the temperature of the hot water storage tank is controlled between 58°C and 65°C. Using the traditional temperature difference controller, the system COP (Coefficient Of Performance) is low and the energy consumption is large, and the economic benefits are not high; the use of the fuzzy controller can increase the system COP by 6.1%, reduce the energy consumption by 22.85%, and save 8.71 yuan in the daily economic benefits of the system.

Load forecast and fuzzy control of the air-conditioning systems at the subway stations

The average annual electricity consumption of the subway stations in China is 1.8–2.3 million kWh, of which ventilation and air-conditioning systems account for approximately 46%. Optimizing the ventilation and air-conditioning control system is an important energy-saving method in urban rail transit. This study applied the technologies of neural network and fuzzy control to the load forecast and the control of the air-conditioning system in subway stations to reduce the energy consumption of the air-conditioning system. Firstly, the energy consumption of the air-conditioning system was calculated by TRNSYS software. Then, a load forecast model of the air-conditioning system was established using neural network technology, and the accuracy of the load forecast model was verified through comparative analysis. Finally, the predictive fuzzy control model of the air-conditioning system was established. The temperature and the humidity in the subway station with the predictive fuzzy control and the traditional temperature control were studied, as well as the energy consumption of the air-conditioning system. Results showed that the neural network technology could effectively predict the load of the subway station's air-conditioning system. The predictive fuzzy control could offset the delay of control quantity adjustment of the air-conditioning system to a certain extent. Compared with the traditional temperature control method, the temperature fluctuation of the station hall and platform under predictive fuzzy control is smaller, and the total energy consumption of the air-conditioning system in summer is reduced by 7.13%. This study provides a reference for reducing the energy consumption of the air-conditioning system in the urban rail transit stations.

An Adaptive Fuzzy Control Model for Multi-Joint Manipulators

Multi-joint manipulator systems are subject to nonlinear influences such as frictional characteristics, random disturbances and load variations. To account for uncertain disturbances in the operation of manipulators, we propose an adaptive manipulator control method based on a multi-joint fuzzy system, in which the upper bound information of the fuzzy system is constant and the state variables of the manipulator control system are measurable. The control algorithm of the system is a MIMO (multi-input-multi-output) fuzzy system that can approximate system error by using a robust adaptive control law to eliminate the shadow caused by approximation error. It can ensure the stability of complex manipulator control systems and reduce the number of fuzzy rules required. Comparison of experimental and simulation data shows that the controller designed using this algorithm has highly-precise trajectory-tracking control and can control robotic systems with complex characteristics of non-linearity, coupling and uncertainty. Therefore, the proposed algorithm has good practical application prospects and promotes the development of complex control systems.

A new approach based on controlling the joint movement of drop foot patients

Stepping is one of the significant functions, which needs an appropriate coordination between various joints to be accomplished properly. Drop foot is a gait abnormality that the harmony between joints is disturbed. This paper proposes a new fuzzy control model for controlling joint movement of drop foot patients. This method has two advantages over pervious ones. The first one is based on identifying a kinematic pattern and not just statistical works. The second is independent of any mathematical models and formulas. The controlling method is based on identification of both stable and unstable manifolds of basin attraction of a healthy person in order to and how to properly move his/her defective leg. The results indicate that using the proposed fuzzy controlling approach has lower computations and good convergence.

Applying fuzzy logic control to analyze real-time control for charging and discharging power of electric vehicles

The disorderly charging of large-scale electric vehicles will aggravate the peak-valley difference of the power grid, and affect the power quality and life of the transformer. The fuzzy logic control strategy for charging and discharging optimization of charging vehicles under the framework of fuzzy logic control from the perspective of the group is considered in this article. A real-time control method based on the clustering characteristics of the charging end time is proposed according to the different charging requirements of the connected electric vehicles and fuzzy logic control is adopted to solve the problem of optimal charging and discharging power of the entire cluster and a single electric vehicle. A fuzzy logic control model considering the charging and discharging of electric vehicles is established orienting at minimize daily load fluctuations and control penalties in the upper layer. The charging and discharging cost of electric vehicle owners is considered to solve the optimal control problem of the charging and discharging power of a single electric vehicle. Taking the data of the typical regional distribution network load as an example, it is verified that the real-time charging optimization strategy under fuzzy logic control through simulation can ensure the reliable operation of the power grid while considering the interests of all parties.

Control System of a Robotic Irrigation Machine Based on the Mamdani Fuzzy Algorithm

The article analyzes the tasks that can be solved by agricultural robots. The purpose of the study was the robotization of the "Fregat" type crop irrigation machine by the fuzzy control of irrigation technological processes, which allows to control the irrigation rate. It is proposed to use in a robotic irrigation machine the analog valve-setter of the speed of the last cart, powered by electricity. It is recommended to include a diagnostic subsystem in the fuzzy control system of the flow control valve, which includes sensors for measuring the soil moisture and the slope of the irrigation machine in various areas of the field. The mathematical model of fuzzy control of the irrigation machine is developed based on the software control of the water supply depending on the terrain of the field, the speed of irrigation machine and soil moisture to reduce water consumption and improve the efficiency and quality of irrigation. The Mamdani algorithm, which is implemented in the MATLAB package, is proposed as a fuzzy inference system. The formalization of the description of the indicators of the irrigation machine is carried out by specifying linguistic variables. The proposed mathematical model can be used in the design of control systems for other robotic agricultural machines.

A Takagi Sugeno based reactor power control of VVER-1000 using linear parameter varying identification of two-point kinetic model

This study aims to design Takagi Sugeno (TS) Fuzzy logic based power control for VVER-1000 PWR to achieve smooth and precise load following situations under various disturbances and model uncertainties. To achieve the purpose, a two-point kinetic model with consideration of neutron diffusion, thermal hydraulics and poison concentration is developed. Model identification for the equivalent linear parameter varying (LPV) system is performed using the fixed functional parameters and reproducing the corresponding LPV subsystems for each of those functional points in the form of SISO transfer function synthesis. Linear heft factors are introduced to compute the weighted output based on the triangular and trapezoidal membership functions of fuzzy logic. Linear interpolation is used to find the controlled power based on heft factors and operational specifications of the nuclear reactor over the entire power range. A TS fuzzy based controller with fixed gain and parameters is designed to efficiently control the reactor power under multiple transient conditions. The LPV subsystems are to be controlled with the fuzzy controller comprising two inputs with effective utilization of unsymmetrical, nonlinear and optimized membership functions and scaled output linear function of fuzzy variables. Stability analysis is performed using the Lyapunov like function and the condition is demonstrated for the two-point kinetic model of the reactor. The performance of the proposed framework is evaluated for load following scenarios with fast and slow ramp changes; along with accident handling with positive reactivity insertion tests. The proposed TS fuzzy power control model found preferably efficient in load tracking applications and disturbance rejection conditions.

Fuzzy logic control theory in clinical anesthesia

AbstractIn the context of big data, the application of fuzzy logic control theory to clinical anesthesia will produce more research directions and new technologies. This article mainly studies the application of fuzzy logic control theory in clinical anesthesia. First, after introducing the basic content of fuzzy logic control theory, the determination method of commonly used membership functions, the relevant knowledge of clinical anesthesia, and the fuzzy logic code rate control model, this article describes in detail the basic principle diagram of the clinical anesthesia control system and the clinical anesthesia process Mechanism model. The mathematical model of clinical anesthesia control system is constructed based on the data fusion technology of the parameters of anesthesia depth monitoring. The parameters in the model are adjusted by the time domain analysis method to measure the dynamic characteristics, and the stability of the system is analyzed by root locus method. The heart rate does not change significantly when it is lower than 1 MAC, and the heart rate increases when it reaches 1.5–2 MAC. Experimental results show that the application of fuzzy logic control theory to clinical anesthesia can reduce the risk of clinical anesthesia.