Simulation Of Fuzzy Controller Research Articles

The coordinated fuzzy control of hierarchical power balance of new energy microgrid

In order to improve the coordinated control effect of hierarchical power balance of new energy microgrid, this paper applies fuzzy control method to this system, and proposes a hierarchical control strategy based on event-triggered communication. Each DG is regarded as a proxy, and the continuous actual value of output is replaced by the state prediction value. Moreover, two different event trigger condition functions for frequency and voltage are designed based on Lyapunov method respectively. At the same time, each DG only communicates with its neighbor DG aperiodic at the event trigger time, and finally all DG are restored to the reference value provided by the virtual leader. Finally, this paper constructs a coordinated fuzzy control simulation system for hierarchical power balance of new energy microgrid. Combined with the simulation results, the method proposed in this paper is feasible.

基于模糊PID的排种器试验台控制系统设计

In order to better detect the working parameters of the seed metering device and further improve the performance of the seed metering device, a set of test-bed control system of seed metering device based on fuzzy PID was designed. The PID parameters are mainly used for on-line fuzzy self-tuning to realize the dynamic adjustment of the speed of the seed metering shaft, so as to ensure the fast adjustment of the speed of the seed metering device. Through the simulation of conventional PID control and fuzzy PID control by matlab, the response curve of the control system is obtained. From the response curve, it can be seen that the step response time of fuzzy PID control is about 0.15s, which is much higher than that of conventional PID control, and the work is stable and there is no overshoot. The bench test is carried out in the laboratory and the fuzzy PID control system is applied to the test-bed, and the actual speed regulation effect of the motor and the performance indexes of the seed metering device test-bed are obtained. The bench test shows that when the fuzzy control system is used to carry out the seed metering device test on the test bench, the qualified grain distance index of the seed metering device is maintained above 91%, the coefficient of variation of the qualified grain distance is less than 4%, the missing sowing index is less than 7%, and the replay index is less than 5%. All meet the national standard requirements of single-grain precision seed metering device.

Novel Design and Simulation of Fuzzy Controller for Turn-On & Turn-Off Angle in Coordination with SRM Speed Control for Electric Vehicles

In current scenario the Switch Reluctance Motor (SRM) are powerful alternative for Electric vehicles applications, due to its simple and rugged structure, high speed, its fault tolerance ability and Magnet free design these attributes make SRM superior to other conventional machines. This motor is a reluctance torque-driven stepper motor that can be used for bi-directional control and self-starting applications. In This paper novel control strategy proposed is to minimizing the Multiobjective function for accurate speed control of SRM by using Mamdani based two input two output fuzzy controller for optimal evaluation of α and β angle by designing closed loop system for accurate speed control of SRM and the corresponding error indices ITAE, IAE, ISE for with and without controller is analysed and compared modelling and simulation is done using MATLAB 2020a.

Fuzzy Logic-Based Controller for Bipedal Robot

In this paper, the problem of controlling a human-like bipedal robot while walking is studied. The control method commonly applied when controlling robots in general and bipedal robots in particular, was based on a dynamical model. This led to the need to accurately define the dynamical model of the robot. The activities of bipedal robots to replace humans, serve humans, or interact with humans are diverse and ever-changing. Accurate determination of the dynamical model of the robot is difficult because it is difficult to fully and accurately determine the dynamical quantities in the differential equations of motion of the robot. Additionally, another difficulty is that because the robot’s operation is always changing, the dynamical quantities also change. There have been a number of works applying fuzzy logic-based controllers and neural networks to control bipedal robots. These methods can overcome to some extent the uncertainties mentioned above. However, it is a challenge to build appropriate rule systems that ensure the control quality as well as the controller’s ability to perform easily and flexibly. In this paper, a method for building a fuzzy rule system suitable for bipedal robot control is proposed. The design of the motion trajectory for the robot according to the human gait and the analysis of dynamical factors affecting the equilibrium condition and the tracking trajectory were performed to provide informational data as well as parameters. Based on that, a fuzzy rule system and fuzzy controller was proposed and built, allowing a determination of the control force/moment without relying on the dynamical model of the robot. For evaluation, an exact controller based on the assumption of an accurate dynamical model, which was a two-feedback loop controller based on integrated inverse dynamics with proportional integral derivative, is also proposed. To confirm the validity of the proposed fuzzy rule system and fuzzy controller, computation and numerical simulation were performed for both types of controllers. Comparison of numerical simulation results showed that the fuzzy rule system and the fuzzy controller worked well. The proposed fuzzy rule system is simple and easy to apply.

Development and simulation of fuzzy adaptive PID control for time variant and invariant systems

Development and simulation of fuzzy adaptive PID control for time variant and invariant systems

Application Research of Fuzzy PID Control Optimized by Genetic Algorithm in Medium and Low Speed Maglev Train Charger

China’s railways are developing rapidly, and the charger of the auxiliary power supply system is an indispensable part of the maglev train. The existing maglev train charger usually uses the traditional PID controller to control the output of the charger which has a simple structure. To meet the requirements of the maglev train charger output performance which is getting higher and higher, it is necessary to optimize the control strategies which can achieve adaptive control of the system. In this paper, taking Qingyuan maglev train as the research object, the control principle of the charger of the maglev train is studied, and the simulation model of the charger of Qingyuan speed maglev train is built in MATLAB/Simulink. After verifying its feasibility, the paper proposes fuzzy PID control to improve the charger control method, and uses genetic algorithm to optimize fuzzy control membership function and fuzzy rules. Finally, the paper builds a fuzzy PID controller simulation model, compares the output performance of the charger under different control methods, and verifies the superiority of the new control method.

ATP-Based Fuzzy Logic Controller Model for Electromagnetic Transients Studies

Alternative Transient Program (ATP) has been steadily developed and improved in the last decades. Today, ATP Draw, a graphical framework, is available, which has improved the use of the program significantly. However, fuzzy controller is one of complex models for which a simplified model can be a very useful tool. This paper introduces a model for fuzzy logic controller in ATP for electromagnetic transients' studies of power system. The proposed model introduces a first model of fuzzy logic in ATP. This facilitates power system and fuzzy logic controller simulation in ATP without needing any other programs. The proposed model is implemented in ATPDraw environment. The proposed fuzzy logic controller model consists of three parts; Membership functions (triangular and trapezoidal shapes), Rules and Defuzzification. The proposed model is verified using numerical examples. The membership degree, the rules, and the defuzzification are calculated using both numerical calculation and the proposed model. The proposed model verification shows acceptable results.

Simulation of Fuzzy Control of Oxygen Flow in PEM Fuel Cells

This paper presents an alternative approach to the flow control of an oxidizer in a proton exchange membrane (PEM) fuel cell system in which pure oxygen is the gas supplied to the cathode channel of the stack. The proposed oxygen flow control is implemented based on information about the current drawn from the fuel cell stack and the voltage variation in the stack. This information and a fuzzy-logic-based control algorithm are used to increase oxygen utilization in a PEM fuel cell system without a recirculation system in relation to the control, in which the oxygen flow rate is determined only in proportion to the current drawn from the stack. To verify the validity of the adopted assumptions, simulation tests of the proposed fuzzy control algorithm were conducted, for which parameters were adopted arbitrarily and determined with help of genetic algorithms. For simulation research, the proposed empirical mathematical model was used, which describes the mathematical relationship between voltage variation in the stack and the stoichiometry of oxygen flow through the cathode of a fuel cell stack. The simulation results confirm that the proposed control method leads to an increase in the oxygen utilization in the system without oxygen recirculation compared to an open system with cathode stoichiometry set to a constant level.

Hardware-in-the-loop simulation of flight mach fuzzy controller

An efficient and reliable flight Mach controller is specially needed for an aircraft. A Flight mach fuzzy controller based on modern fuzzy control theory is designed for an unmanned aerial vehicle (UAV) using a turbojet engine. The theory and process of designing control law are introduced and its control performance is optimized by changing the scale factors. In order to evaluate the control performance, a mathematical simulation system and a hardware-in-the-loop (HIL) simulation platform are built. The mathematical simulation and HIL simulation are carried out respectively, and the simulation results are compared. The evaluation shows good control performance to stabilize the UAV flight Mach number to the target one quickly by controlling the engine work condition.

A hybrid priority-based genetic algorithm for simultaneous pickup and delivery problems in reverse logistics with time windows and multiple decision-makers

This article puts forward a hybrid priority-based nested genetic algorithm with fuzzy logic controller and fuzzy random simulation (hpn-GA with FLC–FRS) for solving a variant of the vehicle routing problem. To meet all the complex restrictions contained in practical reverse logistics, a new mathematical model is developed for simultaneous pickup and delivery problems with time windows and multiple decision-makers (SPDTW–MDM). Then, a hpn-GA with FLC–FRS is proposed, where the priority-based initializing method makes the initializing more applicable, a nested procedure structure handles multiple decision-makers, a fuzzy logic controller helps adjust the mutation rate, and a fuzzy random simulation is used to deal with uncertainties. Finally, in the case study, GA parameters are tuned by Taguchi method and result analyses are presented to highlight the performance of the optimization method for the SPDTW–MDM, while algorithm comparisons by instance applications in different scales show its efficiency and effectiveness.

Fuzzy control simulation of hot water supply system for ground source heat pump

Aimed to the hysteresis, time-varying, nonlinear characteristics of ground source heat pump systems, a fuzzy controller has been designed and simulation calculation is implemented with Matlab software. The results indicate that compared to the traditional PID control system, the adjustment time of the fuzzy control method is found shorter by 40%. The system with the fuzzy controller can reduce the steady-state error and enhance the resistance to disturbance.

Space manipulator motion analysis based on mathematical modelling method

:For the defect of the movement trajectory offset caused by the space manipulator which is multi-dimensional, strong coupling and high degree of freedom exists the inaccurate motion trajectory planning in the process of moving, this paper puts forward to use the principle of fuzzy control on the basis of mathematical modeling, and analyzes and researches the motion of space manipulator. Firstly, the four-degree-of-freedom (MT-R) modular manipulator is used as the mathematical modeling model and the kinematic constraint object to explain the positive kinematics and inverse kinematics modeling algorithm; then, the D-H algorithm is used to represent and model the manipulator, and a positive kinematic model with joint angle as variable is obtained, and this paper uses the analytical method of matrix inverse multiplication to solve the complete analytic solution of inverse kinematics of manipulator; finally, the correctness of positive kinematics model and kinematic inverse solution are verified by fuzzy control simulation and experiment. The verification result shows that using the modeling method can carry out precise positioning for the end actuator of the manipulator, which provides a theoretical basis for the motion analysis of the space manipulator.

A Comparative Strategy Using PI & Fuzzy Controller for Optimization of Power Quality Control

This paper explores the analytical study and simulation of fuzzy logic controller and PI controller, to control the dc output voltage of shunt active power filter for harmonic reduction and power quality improvement in case of nonlinear load. Here we have exercised an effort to design and evaluate a converter to compensate the harmonics for 1-phase AC to DC bridge rectifier which is working as the main converter in unregulated mode. The work depends on the scheme where an ancillary converter is linked in shunt with the main or primary converter whose turn on and turn off time is controlled by an appropriate controller (pi/fuzzy logic) for harmonic compensation of the primary converter which is working as nonlinear load. The Model of converter is proposed on MATLAB\SIMULINK Software and the results are analyzed satisfactorily.

A Comparative Strategy Using PI & Fuzzy Controller for Optimization of Power Quality Control

This paper explores the analytical study and simulation of fuzzy logic controller and PI controller, to control the dc output voltage of shunt active power filter for harmonic reduction and power quality improvement in case of nonlinear load. Here we have exercised an effort to design and evaluate a converter to compensate the harmonics for 1-phase AC to DC bridge rectifier which is working as the main converter in unregulated mode. The work depends on the scheme where an ancillary converter is linked in shunt with the main or primary converter whose turn on and turn off time is controlled by an appropriate controller (pi/fuzzy logic) for harmonic compensation of the primary converter which is working as nonlinear load. The Model of converter is proposed on MATLAB\SIMULINK Software and the results are analyzed satisfactorily.

Application of fuzzy predictive control technology in automatic train operation

In order to better control the train operation system, a typical complex, multi-objective and nonlinear system is discussed. In this study, fuzzy predictive control technology is used to provide high quality control conditions for train operation, which provides great potential for the control of complex system. It is difficult to find the accurate mathematical model and the optimal solution. First, the basic structure and function of train automatic control system are introduced, especially the coordination between automatic train operation (ATO) subsystem and other subsystems. Then, the basic principles of fuzzy logic and predictive control are introduced, and various forms of fuzzy logic and predictive control are analyzed. The application and simulation of fuzzy predictive control in ATO system are deeply studied. Fuzzy predictive control for speed following system of ATO is designed. The fuzzy predictive control technology is compared with the conventional control technology. The simulation results show that the performance of train safety, comfort, parking accuracy and other performance indicators have been improved significantly by using fuzzy predictive controller. In conclusion, the fuzzy predictive controller can realize the control of ATO system better.

Dynamic stabilization of one wheel mobile robot (OWMR): a soft-computing approach

This paper presents application of various Soft-computing control strategies for offline control of one wheel mobile robot (OWMR). The techniques considered for controlling were Fuzzy logic reasoning, Adaptive neuro-fuzzy inference system (ANFIS), and Neural networks (NNs). The study compares the performance of proposed techniques in terms of settling time, maximum overshoot, and steady state error. A Matlab-Simulink model of OWMR system has also been developed. The results obtained from simulation of fuzzy controller were used to train ANFIS and NNs controller. The simulation results showed better performance and learning ability of ANFIS controller as compared to other two controllers. The results are shown with the help of graphs and tables to validate the proposed study.

Modeling and simulation of fuzzy logic controller for optimization of the greenhouse microclimate management

Modeling and simulation of fuzzy logic controller for optimization of the greenhouse microclimate management

Fuzzy Control Model and Simulation for Nonlinear Supply Chain System with Lead Times

A new fuzzy robust control strategy for the nonlinear supply chain system in the presence of lead times is proposed. Based on Takagi-Sugeno fuzzy control system, the fuzzy control model of the nonlinear supply chain system with lead times is constructed. Additionally, we design a fuzzy robust H∞ control strategy taking the definition of maximal overlapped-rules group into consideration to restrain the impacts such as those caused by lead times, switching actions among submodels, and customers’ stochastic demands. This control strategy can not only guarantee that the nonlinear supply chain system is robustly asymptotically stable but also realize soft switching among subsystems of the nonlinear supply chain to make the less fluctuation of the system variables by introducing the membership function of fuzzy system. The comparisons between the proposed fuzzy robust H∞ control strategy and the robust H∞ control strategy are finally illustrated through numerical simulations on a two-stage nonlinear supply chain with lead times.

Fuzzy Logic Control of Soft-Switching DC-DC Converter

The paper presents a novel control technique for a phase-shifted PWM (PS-PWM) soft-switching dc-dc converter. The converter is a full bridge dc-dc converter with controlled output rectifier and an active snubber on the secondary side. To solve the problems with nonlinear nature of the dc-dc converter a fuzzy logic control is used. The fuzzy logic controller design and simulation is presented. The performance of the designed fuzzy logic controller is compared to classical PI controller.DOI: http://dx.doi.org/10.5755/j01.eie.22.5.16334

Simulation of PID and fuzzy logic controller for integrated seat suspension of a quarter car with driver model for different road profiles

Passenger travel comfort is important while analyzing the vibration control of a quarter car model. To achieve the same, various control strategies are employed by the researchers for a 2 Degree of freedom (DOF) quarter car model. We analyzed travel comfort of the passenger by designing and simulating the PID controller and Fuzzy logic controller (FLC) for an 8 DOF quarter car with integrated seat suspension and driver model. While testing the performance of the controllers, the system was subjected to four types of road disturbance individually. The responses were compared with each other along with the passive system. The results show that FLC increases the ride quality better than the PID and passive system.