Fuzzy PI Controller Research Articles

Co-simulation of self-adjusting fuzzy PI controller for the robot with two-axes system

This paper presents the co-simulation of the self-adjusting fuzzy PI controller to control a two-axes system. Each axis was driven by a permanent magnet linear synchronous motor (PMLSM). The position and speed controller used the fuzzy PI algorithm with parameters adjusted by a radial basis function neural network (RBFNN). The vector control was applied to the decoupled effect of the PMLSM. The field programmable gate array (FPGA) was used to control both axes of the system. The very high-speed integrated circuit-hardware description language (VHDL) was developed in the Quartus II software environment, provided by Altera, to analyze and synthesize designs. Firstly, the mathematical model of PMLSM and fuzzy PI was introduced. Secondly, the RBFNN adjusted the knowledge base of the fuzzy PI. Thirdly, the motion trajectory was introduced for testing the control algorithm. Fourthly, the implementation of the controller based on FPGA with the FSM method and the structure of co-simulation between Matlab/Simulink and ModelSim were set up. Finally, discussion about the results proved the effectiveness of the control system, determining the exact position and trajectory of the XY axis system. This research was successful in implementing a two-motor controller within one chip.

Co-simulation of self-adjusting fuzzy PI controller for the robot with two-axes system

Co-simulation of self-adjusting fuzzy PI controller for the robot with two-axes system

A Pitch Angle Controller Based on Novel Fuzzy-PI Control for Wind Turbine Load Reduction

A novel fuzzy rule is proposed to adopt a positive pitch strategy when the error between the measured and rated generator speed becomes large and continues to increase, and to adopt a negative pitch strategy when the error is small. The improved approach is introduced into the normal Fuzzy-Proportional-Integral (Fuzzy-PI) control strategy by dividing the fuzzy rules into four areas and analyzing the design method for each area. Furthermore, a low pass filter is used to reduce the ultimate loads of the pitch driver caused by the novel fuzzy rules. The modeling of the wind turbine load under turbulent wind conditions is conducted in GH Bladed, and MATLAB/Simulink is used to interact with the modeling to verify the novel Fuzzy-PI control. The results show that, compared with normal Fuzzy-PI control, the novel Fuzzy-PI control can greatly reduce the ultimate loads and fatigue loads of the pitch driver. The novel Fuzzy-PI control not only reduces the extremum of power deviation, but also decreases some ultimate loads and fatigue loads of the tower base and the blade root. It can reduce these loads by up to 21.53% under the normal turbulent wind condition and by up to 18.14% under the extreme turbulent wind condition.

Fuzzy-PI Controller based Modulated Multilevel UPQC under Faulty Conditions

Background: This paper presents voltage and current quality improvement in high/medium electrical distribution system using modulated multilevel unified power quality conditioner (MM-UPQC). Nowadays, power quality is one of the major issues due to the increase in usage of more non-linear loads in agricultural, commercial, industrial sectors. The industrial loads produce large amount of harmonics and power imbalances, which cause various power quality related issues like poor power factor, voltage sag, voltage swell, voltage interruption etc. Methods: The prime objective of this work is to design fuzzy-PI based controller based modulated multilevel UPQC for mitigation of issues related to power quality under unsymmetrical fault conditions such as LG fault and LLG fault. Results: This paper uses Instantaneous Reactive Power Theory (IRP) for phase angle adjustment with PI-fuzzy controller scheme to generate accurate reference signal for shunt and series controller of MM-UPQC. The detailed comparative analysis results of simultaneous voltage sag, swell, harmonics compensation and unsymmetrical faults mitigation are presented alongwith the MATLAB/SIMULINK software. Conclusion: Total harmonic distortion analysis is tabulated with PI and fuzzy-PI controller based MM-UPQC for different operating conditions in 4.16 KV distribution system.

Modeling, simulation and experimental realization of a new nonlinear fuzzy PID controller using Center of Gravity defuzzification

Though Center of Gravity (CoG) defuzzification is a well-known and long-standing method in the history of fuzzy systems, because of its computational complexity, its use in the field of modeling of fuzzy controllers is almost nil. From literature, it appears that modeling of fuzzy Proportional Integral Derivative (FPID) controllers is rarely attempted using CoG defuzzification. In fact, none of the FPID controller models are obtained using both two-dimensional input space and CoG defuzzification. The available mathematical models of fuzzy Proportional Integral (FPI) and fuzzy Proportional Derivative (FPD) controllers using two-dimensional input space and CoG defuzzification were due to Arun and Mohan (2017). In this paper, the authors make an attempt to model and design an FPID controller using two-dimensional input space and CoG defuzzification. The incremental control effort produced by the newly developed FPID controller is found by combining the individual control efforts produced by incremental FPI and incremental FPD controllers. The incremental FPI and incremental FPD controller structures are unveiled using two-dimensional input space, CoG defuzzification, Min t-norm, Max t-conorm, and Larsen Product (LP) inference. The applicability and usefulness of the newly obtained FPID controller are depicted with simulation and real-time experimentation.

Bi-directional Power Flow Control with Improved Power Quality using Intelligent Controller for AC-DC Coupled Hybrid Micro-Grid System

Grid connected renewable sources are affected by harmonic distortion due to non-linear power electronics interfaces. Depending upon the nature of the load, many conversion stages are incorporated in the micro-grid which leads to increase in the losses. Therefore, in this paper, the AC-DC coupled hybrid micro-grid system is presented in which AC renewable sources and AC loads are connected to AC sub-grid and DC renewable sources and DC loads are connected to DC sub-grid. The shunt active power filter based 3φ 4-leg bi-directional intermediate converter using d-q load current technique is proposed to achieve the power flow between AC and DC sub-grids. The MATLAB simulation is carried out, and the performance of the proposed system is analysed using the d-q load current technique based fuzzy logic and PI controller. The obtained results demonstrate that the overall system performance is improved with the d-q load current technique based fuzzy logic controller.

Design and investigations of MPPT strategies for a wind energy conversion system based on doubly fed induction generator

The purpose of this work is to design and to discuss various strategies to optimize the production of a wind energy conversion chain based on the doubly fed induction generator (DFIG), by capturing the maximum power at the wind turbine, using maximum power point tracking (MPPT) and pitch control. The proposed controls allow the generator to monitor the optimal operating points of the turbines regardless of wind speed variations, system parameters disturbance, and parameters variation. Simulation of WECS based on a 1.5 MW wound rotor induction generator under MATLAB/SIMULINK is carried out using the PI controller (PIC), RST controller and fuzzy logic controller (FLC). Analysis and comparisons are made for different operating scenarios: Reference tracking, robustness under variable wind speed conditions and parameters variation. The application of FLC provides a very interesting outcome for the robustness and the dynamic challenges.

Design and building of an automated heat-treatment system for industrial applications

The aim of this study is to suggest a reliable low cost smart system for automating the heat treatment process. This system will reduce human direct interaction. The Fuzzy-PI control algorithm combined with ON/OFF algorithm was designed to automate the whole system operations. The overall automated furnace system includes control unit, algorithm, sensor and drive circuit was simulated in MATLAB/Simulink, tested for different heat treatment techniques. Normalizing and annealing heat treatments were performed on AISI1040 steel using a traditional method as well the embedded system with Fuzzy-PI control. The results show that the suggested Fuzzy-PI control algorithm and whole furnace system can reproduce any desired and arbitrary heat treatment technique curve with minimum error. Heat treatments resulted in significant changes in microstructure and hardness of the AISI1040 steel under investigation. In addition, there is no significant differing between the microstructure and hardness of AISI1040 steel after traditional and automated heat treatment processes. Where the hardness after traditional normalizing heat treatment was 234 HV, while it was 232 HV after automated normalizing heat treatment system. This confirms that the proposed automatic control system for heat treatment processes is reliable and useful in reducing the cost and time spent in these processes.

Adaptive Hybrid Fuzzy PI-LQR Optimal Control using Artificial Immune System via Clonal Selection for Two-Area Load Frequency Control

Load frequency control (LFC) problem has been a foremost issue in electrical power system operation and is becoming more important recently with growing size, changing structure, and complexity in interconnected power systems. In general, LFC system utilizes simple proportional integral (PI) controller. However, due to the PI control parameters are commonly adjusted based on classical or trial-error method (TEM), it is incapable of obtaining good dynamic performance for a wide range of operating conditions and various load change scenarios in a multi-area power system. This paper introduces a novel control scheme for load frequency control (LFC) using hybrid fuzzy proportional integral (fuzzy PI) and linear quadratic regulator (LQR) optimal control, where fuzzy logic control (FLC) is used to adjust the gains KP and KI of PI controller which called fuzzy PI in this paper, while the LQR optimal control method is employed to obtain the feedback gain KOP through Algebraic Riccati Equation (ARE). The merit of both control strategies is to tune their control feedback gains, which are KP, KI and KOP, regarding various system operating conditions. Artificial immune system (AIS) via clonal selection is utilized to optimize the membership function (MF) of fuzzy PI and weighting matrices Q and R of LQR optimal control in order to obtain their optimal feedback gains. To examine the efficacy of the proposed method, LFC of two-area power system model is utilized as a test system. The amalgamation of fuzzy PI-LQR is applied to improve the dynamic performance of two-area LFC. Other control schemes such as PI controller, hybrid PI controllerLQR, and hybrid fuzzy PI-LQR are also investigated to the studied a test system. The obtained simulation results show that the proposed method could compress the settling time and decrease the overshoot of LFC which is better than other approaches that are also employed to the tested system in this study.

Experimental PI Fuzzy Controller to Control Pinch-roll Pressure via Hydraulic servo-valves in Continuous Casting Machines in Mobarakeh Steel Company

In this paper, the Fuzzy PI controller is used to control the hydraulic servo-valves in Saba iron casting facility in Mobarakeh Steel Company. The electronic and control circuitry in the hydraulic servo-valves was damaged and the oil pressure sensor was not working anymore. The roles were bending and slabs were occasionally broken. Any replacement of the whole servo-valve system was not an option. Therefore, a pressure sensor for the oil outlet is installed and using the input to the control unit, the pressure is controlled. Fuzzy membership functions were defined in PLC to implement a PI Fuzzy Controller. The servo-valve was modeled and simulation results shows good controllability of the process in presence of disturbance. The experimental measurements of the slab pressure proved promising application of Fuzzy PI controller for the system under consideration.

Analysis of Proportional Integral Controller and Fuzzy Logic Controller for Single Phase Induction Motor

Nowadays problems are occurring due to power quality issues which causes major impression in power system. Most of the times problem is of harmonics in the supply system. It occurs due to Non-Linear Load and it could be rectified by using filters, controllers and artificial intelligence. In this paper, the Fuzzy logic controller is designed using Takagi- Sugeno fuzzy inference system to eliminate harmonics and uncertainty in the supply system thereby improving the response of whole system. The Selective Harmonic Elimination Pulse Width Modulation (SHEPWM) control scheme is used to generate the necessary pulses to eliminate the lower order harmonics. The results for both fuzzy logic controller (FLC) and PI Controller are compared, and the simulation is designed in MATLAB Simulink.

Design of the footprints of uncertainty for a class of typical interval type-2 fuzzy PI and PD controllers

The Footprint of Uncertainty (FOU) for an Interval type-2 (IT2) input fuzzy set is a key parameter of IT2 fuzzy PI and PD (FPI and FPD) controllers because it provides additional options to handle uncertainties. Because the effects of FOUs on IT2 FPI and FPD controller are still unclear, it is necessary to provide a guideline for designing the FOUs. In this study, from the input–output relationship of IT2 FPI and FPD controllers using typical and popular configuration, the variation trend of variable gains, in relation to the increase of FOUs, was analyzed. It is mathematically proven that variable gains of typical controllers decrease or not increase as the FOUs increase. These theoretical results can be extended to provide a guideline for designing FOUs of typical IT2 FPI and FPD controllers. Real-time response of linear voice coil motor is used to show the effectiveness of our results.

Application of Fuzzy Controller for Improvement of Power Quality using UPQC

Generally, one of the custom power device in FACTS called unified power quality conditioner, which is used to compensate the voltage and current-related Power Quality issues in the distribution systems. The proposed UPQC technology have an advantage of reduced dc-link voltage without compromising its compensation capability. This new method also helps to meet the requirement of dc-link voltage for the shunt and series active filters of the UPQC. This type of topology has a capacitor in series with the interfacing inductor across the shunt active filter for filtering purpose, and the system neutral is also considered and directly connected to neutral of distribution system avoid the requirement of the fourth leg in the voltage source inverter. This paper also presents a concept for improving power quality of a power distribution system such as an FUZZY logic controller along with the UPQC control strategy. The simulation results are compared for both conventional PI controller and FUZZY controller.

Bus Voltage Stabilization Control of Photovoltaic DC Microgrid Based on Fuzzy-PI Dual-Mode Controller

The photovoltaic DC microgrid has strong nonlinearity and time variation. Therefore, traditional dual closed-loop control strategy of voltage and current based on PI controller cannot effectively restrain the fluctuation and impact of DC bus voltage when the dynamic response of the system is improved. Under this situation, in this paper, the fuzzy-PI dual-mode controller is designed to upgrade the traditional dual closed-loop control, taking voltage outer ring into consideration, which is adopted to achieve good transient performance while the bus voltage deviation is large. While the bus voltage deviation is small, the PI controller is utilized for good steady-state performance. Hence, simulation and experimental results show that the fuzzy-PI dual-mode controller has the same advantages with both fuzzy control and PI control; in other words, it has the features of speedy response, low overshoot, good robustness, and strong anti-interference under different working conditions.

Analysis and Design of a Double Fuzzy PI Controller of a Voltage Outer Loop in a Reversible Three-Phase PWM Converter

Aiming at the application of a reversible three-phase pulse width modulation (PWM) converter with a wide range of AC side voltage and DC side voltage, a double fuzzy proportional integral (PI) controller for voltage outer loop was proposed. The structures of the proposed controller were motivated by the problems that either the traditional PI controller or single fuzzy PI controller cannot achieve high performance in a wide range of AC and DC voltage conditions. The presented double fuzzy controller studied in this paper is a sub fuzzy controller in addition to the traditional fuzzy PI controller; in particular, the sub fuzzy controller can get the auxiliary correction of PI control parameters according to the AC side voltage and the DC side given voltage variation of PWM converter after the reasoning of the sub fuzzy controller, while the traditional fuzzy PI controller outputs the correction of PI control parameters according to the DC voltage error and its error change rate. In this paper, the traditional fuzzy PI controller can be called the main fuzzy controller, and the adaptive adjustment of PI control parameters of the voltage outer loop is the sum of the PI parameter correction output by the main fuzzy controller and the auxiliary PI parameter correction output by the sub fuzzy controller. Finally, the experimental results show that the reversible three-phase PWM converter can achieve excellent dynamic and static performance in a wide range of AC voltage and DC voltage applications by using the proposed double fuzzy PI controller.

The Fuzzy PI Controller for PMSM’s Speed to Track the Standard Model

This paper proposes a fuzzy PI controller to control the speed of a permanent magnet synchronous motor (PMSM). The structure of the system includes the speed loop controller (SLC) and the current loop controller (CLC). The speed loop controller is the fuzzy PI and standard model (SM). The CLC includes vector control and the space vector pulse width modulation (SVPWM). It compiles two closed-loop control systems for the PMSM. This research uses a very high-speed integrated circuit hardware description language (VHDL) to implement the proposed algorithm and embed it into Matlab/Simulink for simulation. Based on the PMSM parameter, this article tests the controller’s correctness with some of the load cases by changing the combined inertia and viscous friction of rotor and load. After success in simulation, the system is tested again by experiment on the FPGA kit. The simulation and experiment results show that when the load changes, the PMSM speed is still stable. The novelty of this research is that it compares two kinds of controllers between simulation and experiment results.

Evolving Fuzzy Models for Prosthetic Hand Myoelectric-Based Control

This article applies an incremental online identification algorithm to develop a set of evolving fuzzy models (FMs) that characterize the nonlinear finger dynamics of the human hand for the myoelectric (ME)-based control of a prosthetic hand. The FM inputs are the ME signals obtained from eight ME sensors and past inputs and/or outputs. The FM outputs are the finger angles, considered here as the midcarpal joint angles, to ensure their control. The best evolving FMs that characterize each of the five fingers are described with the results validated on real data. Simple second-order linear models are next given to enable the cost-effective controller design. Five separate control loops are proposed, with proportional-integral (PI) controllers separately tuned by a frequency-domain approach. Simple PI-fuzzy controllers are designed starting with the linear PI controllers to ensure the control system performance improvement. The evolving FMs are used to simulate accurately the behavior of the human hand. Digital simulation results are included to show the effectiveness of the PI-fuzzy controllers and the performance improvement in comparison to the initial PI ones.

FUZZY CONTROLLER OF MODEL REDUCTION DISTILLATION COLUMN WITH MINIMAL RULES

In this paper the control of a binary distillation column is described. This control is done with fuzzy logic, one with PI- like fuzzy controller and the other with modified PI fuzzy controller, using the minimal rules for fuzzy processing. This work is focused on model reduction of Wood and Berry binary distillation column to get the best performance. It is desired to minimize the rules in order to reduce the computation time to make a faster decision. Comparisons will be made between two versions of fuzzy controllers utilizing reduced rules to verify the outputs. The controlled variables are top composition with high concentration and bottom composition with low. To demonstrate the performance of the fuzzy PI control schemes, results are compared with a classical PI controller and optimal methods, like Differential Evolution (DE), Invasive Weed Optimization (IWO).The proposed structure is able to quickly track the parameter variation and perform better in load disturbances and also for set point changes. Then all the processes of the distillation column with it? s fuzzy controllers are simulated in MATLAB software as the results are shown.

Power quality control based on voltage sag/swell, unbalancing, frequency, THD and power factor using artificial neural network in PV integrated AC microgrid

Abstract In the present scenario of microgrid system, conversion of electrical energy has initiated a challenge to maintain the power quality within a satisfactory range. It can be influenced by the voltage deviation, sag/swell, unbalancing, frequency, total harmonic distortion (THD) and power factor as per nature of local loads and the condition of distributed energy resources (DERs). The relationship between power quality and the set of these variables are non-linear in nature. The existing literature show that the above mentioned parameters are not considered simultaneously for the assessment and controlling of power quality in PV based AC microgrid. To minimize the effect of these variables, a novel artificial neural network (ANN) based control approach has been proposed which can control the power quality as per IEEE/IEC standards. The proposed method has shown fast, smooth and stable operation while the performance of the same is verified with that of the proportional–integral (PI) and fuzzy-PI controllers using Matlab-Simulink software. The small size microgrid model is tested with the effect of line impedance and communication delay for the assessment of power quality parameters. This model is extended to a large size realistic microgrid structure for the feasibility of control methodology. The realistic microgrid structure is verified under the analysis of line impedance, communication delay, demand response and off-nominal conditions. The proposed control methodology is validated in a realistic microgrid structure and simulation results are presented to show the performance of proposed controller under different test conditions to identify an ANN library.

RETRACTED: A novel cascaded fuzzy PD-PI controller for load frequency study of solar-thermal/wind generator-based interconnected power system using grasshopper optimization algorithm

This work analyses the load-frequency responses of a multi-unit based two-area power system by proposing a novel cascaded fuzzy Proportional Derivative-Proportional Integral (PD-PI) controller tuned with a recently proposed grasshopper optimization algorithm. Performance of the power system comprising of conventional sources like hydro and thermal generating units is evaluated by cascaded fuzzy PD-PI controller optimised by grasshopper optimization algorithm. The potential of grasshopper optimization algorithm is validated by comparing with other algorithms. Further, load-frequency response is studied by penetrating solar-thermal and wind power generating units into the recommended system. The power system integrated with renewable sources puts forth a great stability challenge in the wake of high load perturbation. Hence, a robust secondary controller named cascaded fuzzy PD-PI controller is designed by endorsing a profound grasshopper optimization algorithm technique, to tackle this stability challenge. The credibility of the cascaded fuzzy PD-PI controller with/without nonlinearities presented in the system is validated by comparing the results obtained from proportional–integral–derivative and fuzzy-proportional–integral–derivative controllers. Besides this, the performance of the system under highly perturbed step load variation confers the robustness of the proposed method.