Fuzzy Control Model Research Articles

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.

Computing information and control complex for control of road construction machines

The article considers a model of the digging process carried out during the operation of a road-building machine, identifies the controlled and control parameters of the model. Based on the results of numerical simulation, it is shown that a rigid control algorithm is capable of providing effective results only in cases where most of the operating parameters of the machine do not go beyond the optimal area. To improve the efficiency of control and expand the permissible range of parameters of the state of the machine, it is proposed to use control based on fuzzy logic. The structure of synthesis of algorithms for fuzzy control of road-building machines is given. For the purpose of practical implementation of a fuzzy control algorithm, a basic model has been built, which includes 10 linguistic rules, on the basis of which chains of actions are formed during the operation of a machine. When implementing a fuzzy control model for the main parameters of the state of the machine, it is proposed to use triangular membership functions. The fuzzy inference surface for the fuzzy control algorithm is obtained and visualized, which makes it possible to efficiently search for the optimal trajectory of the control parameter function. An agent-based traffic model is considered, which describes the movement of road-building vehicles on a site and uses active and proactive algorithms. The results of numerical modeling confirm the feasibility of considering a variety of road-building machines at the site of joint work execution as a multi-agent system with elements of self-organization (CDPS-system). The maximum effect of self-organization is observed when the distance between the machines is changed, when they are uniformly located on the site. The expediency of combining local systems of automatic control of road-building machines into a single information and control complex is substantiated, a list of information and control tasks to be solved by the complex is formulated, and its structural diagram is synthesized.

Wavelet neural learning-based type-2 fuzzy PID controller for speed regulation in BLDC motor

In brushless direct current (BLDC) motors, speed control is a prominent operation based on which these motors are widely used in higher-end industrial applications including robotics, aeronautics, disk drives, factory automation, consumer electronics, transport and military applications. A novel wavelet neural learning (WNL)-based type-2 fuzzy system controller is devised in this paper to accomplish effective speed control of the BLDC motor with the set specifications. The performance characteristics of the BLDC motor are simulated and analysed based on the wavelet neural learning-based type-2 fuzzy proportional–integral–derivative (PID) controller. This work employs wavelet neural learning to provide inputs to the constructed new type-2 fuzzy PID controller model, and this drives the BLDC motor mechanism. The gain values of the PID controller are tuned with the developed WNL-based type-2 fuzzy system to carry out most operational speed regulation process for the motor mechanism. The developed speed controller model for the BLDC motor is tested for its superiority by performing step variations of the input signal and also for load disturbances. In WNL, to derive the output a wavelet function is applied and this drives the operation of the type-2 fuzzy system PID controller and attains better gain values for speed control operation. The classic slime mould algorithm is used in this contribution to tune the weight coefficients during wavelet neural learning process and due to which the delayed convergence is controlled. Results attained during the simulation process prove the effectiveness and superiority of the proposed speed controller for the design specifications of the BLDC motor in comparison with the earlier literature contributions made.

Simulation of Hydraulic Suspension System of Electric Tractor Based on Matlab-AMESim

For electric tractor engine power output characteristic, a load sensitive hydraulic suspension system in front of the valve is designed. In order to meet the operation requirements of different soil conditions when the electric tractor is plowing, and to improve the dynamic response characteristics of the system and the uniformity of tillage depth, a fuzzy PID based resistance-position combined adjustment tillage depth control strategy is designed. The hydraulic system simulation model is established based on AMESim, and the fuzzy PID controller model is established using Matlab. Through the joint simulation of AMESim and Matlab, the control performance of hydraulic suspension system of electric tractor with different resistance-position comprehensive proportional coefficients is analyzed. The simulation results show that the control strategy of tillage depth based on fuzzy PID is more responsive and anti-interference strong ability, the resistance-position combined adjustment has higher adaptability to different soil changes and operation methods than a single resistance adjustment or position adjustment.

Prediction of Accident Occurrence Possibilityby Fuzzy Rule-Based and Multi-Variable Regression (Case Study: Lift Trucks)

Uncertain and stochastic conditions of accidents could affect the risk and complexity of decisions for managers. Accident prediction methods could be helpful to confront these challenges. Fuzzy inference systems (FIS) have developed a new attitude in this field in recent years. As lift truck accidents are one of the main challenges that industries face worldwide, this paper focuses on predicting the possibility of these types of accidents. At first, the data collection is done by using interviews, questionnaires, and surveys. A FIS approach is proposed to predict the possibility of lift truck accidents in industrial plants. Furthermore, our approach is validated using data from many real cases. The results are approved by the multivariate logistic regression method. Finally, the output of the fuzzy and logit models is compared with each other. The re-validation of the fuzzy control model and high consistent of the output of these two models is presented.

Modeling, analysis and real-time implementation of five new simplest fuzzy nonlinear PI/PD controllers

In spite of accuracy, but due to computational complexity, Center of Gravity (CoG) defuzzification remained dormant for the mathematical modeling of fuzzy controllers for a long time in the past. Recently, Arun and Mohan (ISA Trans 70:16–29, 2017) obtained the mathematical models of two simplest fuzzy PI/PD controllers using CoG defuzzification. In this study, the mathematical models of another five simplest fuzzy PI/PD controllers are obtained, and their properties and computational aspects are investigated. Sufficient conditions for Bounded-Input-Bounded-Output stability of a closed-loop system containing one of these controllers in the loop are also established using the Small-Gain theorem. To justify the theoretical developments made in this manuscript, the applicability of the controllers is shown in simulation and real-time.

Observer-based interval type-2 fuzzy friction modeling and compensation control for steer-by-wire system

This paper studies the tracking control of the SbW system with unknown nonlinear friction torque and the unmeasured angular velocity. An observer-based adaptive interval type-2 fuzzy logic system controller is proposed to eliminate the adverse influence of the friction torque on the SbW system. Firstly, the angular velocity of the front wheels is estimated via the observer, such that the system sensitivity to measurement noise, the hardware cost, and the structural complexity are reduced. Then, an interval type-2 fuzzy logic system (IT2 FLS) is used to model the friction torque, in which the model and parameters are not effectively identified. IT2 FLS has a more exceptional ability to deal with uncertainties than the traditional type-1 fuzzy logic system (T1 FLS), so the friction modeling based on IT2 FLS has more satisfactory effect in practical application. Finally, an adaptive interval type-2 fuzzy logic system controller is proposed to achieve excellent tracking performance. The tracking error can be guaranteed to converge asymptotically to zero by the Lyapunov stability theory. The numerical simulations and hardware-in-loop (HIL) experiments verify the effectiveness and superiority of the proposed friction modeling method and control strategy.

Membership-Function-Dependent Control Design and Stability Analysis of Interval Type-2 Sampled-Data Fuzzy-Model-Based Control System

This article investigates the design and stability analysis of the interval type-2 (IT2) sampled-data (SD) fuzzy-model-based (FMB) control system with the optimal guaranteed cost performance. An IT2 Takagi–Sugeno (T-S) fuzzy model is applied to describe the dynamics of the nonlinear systems where the parameter uncertainties are captured by the lower and upper membership functions. To conduct the stability analysis for the SD FMB control system, a looped-functional approach taking the advantage of the information about the sampling periods is employed. Because of the SD control strategy, the state will be sampled at each sampling instant and the control signal generated by the IT2SD fuzzy controller will be kept by the zero-order holder during the sampling period, which will result in mismatched membership grades between IT2 T-S fuzzy model and IT2SD fuzzy controller that leads to the complexity in carrying out stability analysis. Thanks to the imperfect premise matching concept, which allows the difference on the number of rules and the premise membership functions between model and controller, the design of the IT2SD fuzzy controller can be more flexible. To further relax the stability conditions and minimize the upper bound of the guaranteed cost index, the membership-function-dependent stability analysis approach which can make use of the features of the IT2 membership functions is adopted. The performance of the control system can also be adjusted through the choice of the weighting matrices in the cost function. The stability conditions building on the Lyapunov stability theory and the performance conditions building on the concept of the guaranteed cost control in the shape of linear matrix inequalities are established to assure the system stability and acquire the optimal guaranteed cost performance. The proposed IT2SDFMB control design is tested on the inverted pendulum system and the simulation results verify the effectiveness of the proposed approach.

Research on energy control strategy of PHEV

According to the characteristics of Plug-in hybrid electric vehicle, PHEV optimal fuzzy control is proposed. The engine operating points are controlled on optimal fuel consumption curve. Based on the power request and SOC, the fuzzy controller can distribute the engine given power and the condition of SOC drop with insufficient of energy recovery is improved to realize constant SOC control Furthermore. ADVISOR is developed by Fuzzy control model embedding, and make simulation experiment under different driving cycle. The studies on case prove that PHEV optimal fuzzy control can realize the constant SOC control under road conditions, so that it has the well robustness.

Optimal control of fault tolerant machining system with reboot and recovery in fuzzy environment using harmony search algorithm

In this study, fuzzy modeling and service control based on optimal N-policy for fault tolerance system have been investigated. The impacts of failures and recovery along with the reboot process are taken into account for the assessment of performance indices of the system operating under redundancy and provision of maintainability. The non-Markovian M/G/1 model of the fault-tolerant system with vacationing server is developed by using the supplementary variable to represent the remaining time to repair and further employing recursive approach. The parametric non-linear programming is used to derive fuzzy performance metrics. The sensitiveness of parameters concerning system indices is done. The cost function is framed to obtain the optimal descriptors and minimal cost using harmony search.

Wireless positioning and path tracking for a mobile platform in greenhouse

In order to realize intelligent greenhouse, an automatic navigation method for a mobile platform based on ultra-wideband (UWB) positioning technology was proposed and validated in this study. The time difference of arrival (TDOA) approach was used to monitor and track the UWB positioning to obtain the localization information of the mobile platform working in a greenhouse. After applying polynomial fitting for positioning error correction, the system accuracy was within 5 mm. A fuzzy controller model was constructed by incorporating the lateral and heading deviations as input variables and the steering angle of front wheel as the output variable. A fuzzy rule was established based on domain knowledge, as well as the steering angle of front wheel offline query table, which was applied to alleviate the calculative load of the controller. Experimental results confirmed that the automatic navigation method proposed in this study performed satisfactorily, with a steady-state error ranging from 41 mm to 79 mm when tracking straight line, and an average error of 185 mm and an average maximum error of 532 mm when tracking polygon. In addition, the maximum error occurred at the polygonal corner which could meet the needs of driving on the narrow road in the greenhouse. The method proposed in this study provides a new systematic approach for the research of greenhouse automatic navigation. Keywords: wireless positioning, ultra-wideband, path tracking, positioning error correction, fuzzy control, query table, mobile platform, greenhouse DOI: 10.25165/j.ijabe.20211401.5627 Citation: Yao L J, Hu D, Zhao C, Yang Z D, Zhang Z. Wireless positioning and path tracking for a mobile platform in greenhouse. Int J Agric & Biol Eng, 2021; 14(1): 216–223.

A Vehicle Guidance Model with a Close-to-Reality Driver Model and Different Levels of Vehicle Automation

This paper presents a microscopic vehicle guidance model which adapts to different levels of vehicle automation. Independent of the vehicle, the driver model built is different from the common microscopic simulation models that regard the driver and the vehicle as a unit. The term “Vehicle Guidance Model” covers, here, both the human driver as well as a combination of human driver and driver assistance system up to fully autonomously operated vehicles without a (human) driver. Therefore, the vehicle guidance model can be combined with different kinds of vehicle models. As a result, the combination of different types of driver (human/machine) and different types of vehicle (internal combustion engine/electric) can be simulated. Mainly two parts constitute the vehicle guidance model in this paper: the first part is a traditional microscopic car-following model adjusted according to different degrees of automation level. The adjusted model represents the automation level for the present and the near and the more distant future. The second part is a fuzzy control model that describes how humans adjust the pedal position when they want to reach a target speed with their vehicle. An experiment with 34 subjects was carried out with a driving simulator based on the experimental data and the fuzzy control strategy was determined. Finally, when comparing the simulated model data and actual driving data, it is found that the fuzzy model for the human driver can reproduce the behavior of human participants almost accurately.

Fuzzy control mathematical modelling method based on dynamic particle swarm optimisation training

Aiming at the false correlation problems under set number limit condition in the fuzzy control mathematic modelling process, combined with dynamic particle swarm optimisation training algorithm, the traditional fuzzy control mathematical model based on the Nash equilibrium solution method is difficult to converge to the optimal solution of the state space, leading to bad control performance. This paper proposes the fuzzy control mathematical modelling method based on dynamic particle swarm optimisation training, constructs the general structure model of fuzzy control, describes the standard particle algorithm under the constraint of learning samples of random functional, obtains the global optimal solution of control domain of fuzzy control parameters, and conducts particle swarm optimisation training by adopting the position vector fitness updating method. The research results show that the new method can make every step state update get more effective observation information, reduce the error caused by the difficult use of observation data, reduce the computation cost and improve the accuracy of fuzzy control.

Fuzzy Control‐Based Energy‐Aware Routing Protocol for Wireless Body Area Networks

Advances in medical and communication technologies have empowered the development of Wireless Body Area Networks (WBANs). WBANs interconnect with miniature sensors placed on the human body to enable medical monitoring of patient health. However, the limited battery capacity, delay, and reliability of data transmission have brought challenges to the wider application of WBAN. Minimum consumption of energy and maximum satisfaction with the QoS requirements are essential design aims of the WBAN schemes. Therefore, a fuzzy control‐based energy‐aware routing protocol (EARP) is proposed in this paper, the proposed protocol establishes a fuzzy control model composed of remaining node energy and link quality, and the best forwarder node is determined by the processes of fuzzification, fuzzy inference, and defuzzification. The simulation results showed that compared with the performance of the existing EERDT and M‐TSIMPLE protocols, the proposed EARP has better performance, including extending network lifetime and improving the reliability of data transmission.

Neural network model of decision making in electric power facilities under conditions of uncertainty

The article is devoted to the issue of creating a mathematical model of the problem of making management decisions in electric power facilities based on modern intelligent technologies, which makes it possible to take into account the influence of various factors on the operating modes of the power system. A systematic approach to describing processes in the mathematical language of the theory of fuzzy sets is proposed. To solve the problem of controlling the operating modes of the power system, a neurofuzzy network has been developed that combines the algorithms of Takagi-Sugeno fuzzy inference, as well as a recurrent neural network. An adaptive learning algorithm based on the Frechet method is proposed for training a neural network. The analysis of the efficiency of the fuzzy control model under the conditions of various modes of functioning of the local power system is carried out.

STUDI PEMODELAN SISTEM PENGONTROLAN SUHU RUANGAN BERBASIS LOGIKA FUZZY SUGENO

Fuzzy logic is part of an artificial intelligence system (Artificial Intelligence) that emulates the human ability to think in the form of an algorithm which is then run by a machine. Fuzzy logic can also be applied to control room temperature, so that the temperature becomes stable. One of the fuzzy logic controller models that is applied in solving the problem is Fuzzy Sugeno. Making this prototype aims to collect Sugeno fuzzy logic data with this tool so that the temperature in a room becomes stable as desired, this prototype work system is based on the temperature and humidity values read by the sensor then the fan will rotate according to the temperature and humidity. This research uses Sugeno fuzzy logic with temperature and humidity input and the output is fan rotation. The series of modeling system for room temperature control based on Sugeno fuzzy logic, which is designed to be able to regulate room temperature conditions for each temperature and humidity condition read by the sensor. Experimental results for the comparison between manual calculation and Matlab achieved 100% success. While the experimental results for the comparison between matlab and prototype have an average error of 36% from five experiments.

Analysis of Nonlinear Active Noise Behavior of Fuzzy Controller Using Non-Perturbation Methods

In this paper, a Fuzzy controller model has been converted into a time-dependent nonlinear model and then quadratic Riccati differential equation was analyzed to satisfy the solution of the nonlinear active noise behavior of Fuzzy controller. Further, the approximate solutions of this equation using non-perturbation methods i.e., adomian decomposition method (ADM), variational iterational method (VIM) and homotopy perturbation method (HPM) were investigated. A comparison of these methods has also been given with tabular and graphical presentations. Our results reveal that VIM provides the closest approximate solution and fast convergence for the proposed model as compare to ADM and HPM.

Takagi–Sugeno fuzzy sampled‐data model‐based less conservative stability criterion for a Francis hydropower unit's governing system

In this study, the stability analysis and controller design of a hydropower unit's governing system (HUGS) were studied based on a Takagi–Sugeno (T–S) fuzzy model and constant sampled-data control. First, according to the T–S fuzzy theory, the non-linear Francis hydro-turbine governing system under rigid water hammer is linearised and then the approximate linear system was obtained. Second, a fuzzy sampled-data controller was designed in the case of periodic sampling, and the closed-loop sampling system was discretised. After constructing the Lyapunov function and using the forward difference method, the stabilisation condition was given with less conservatism in a symmetrical linear matrix inequality form. Finally, the numerical simulation results showed that under four different sampling periods, the HUGS can quickly achieve stability and that it has different stability performance. In addition, the superiority of the controller was verified in comparison with traditional proportional–integral–derivative control and fuzzy control techniques.