Takagi-Sugeno Fuzzy Logic Controller Research Articles

Takagi-Sugeno Fuzzy Perpose as Speed Controller in Indirect Field Oriented Control of Induction Motor Drive

This paper deal with the problem in speed controller for Indirect Field Oriented Control of Induction Motor. The problem cause decrease performance of Induction Motor where it widely used in high-performance applications. In order decrease the fault of speed induction motor, Takagi-Sugeno type Fuzzy logic control is used as the speed controller. For this, a model of indirect field oriented control of induction motor is built and simulating using MATLAB simulink. Secondly, error of speed and derivative error as the input and change of torque command as the output for speed control is applied in simulation. Lastly, from the simulation result overshoot is zero persent, rise time is 0.4s and settling time is 0.4s. The important data is steady state error is 0.01 percent show that the speed can follow reference speed. From that simulation result illustrate the effectiveness of the proposed approach.

Fuzzy Logic Voltage Regulator for Improving Transient Stability and Fault Ride Through Capability of DFIG Wind Turbines

With large penetration of DFIG wind turbines, fault ride through capability and transient stability improvement issues have been rapidly interested to investigate in terms of DFIG controller design. This paper proposes a fuzzy logic voltage regulator to improve transient stability and fault ride through capability of DFIG wind turbines. To compose the proposed controller, utilized for the Rotor Side Converter (RSC), the Takagi-Sugeno (TS) fuzzy logic controller is instantly implemented for enhancing DFIG ability. The simulation results have been compared with a lead-lag compensator revealing that the proposed controller is able to increase DFIG transient stability with robustness and it shows also a superior performance for fault ride through capability enhancement with Irish grid code as a standard, under disparate circumstances.

Design and Implementation of Takagi–Sugeno Fuzzy Logic Controller for Shunt Compensator

This paper describes the application of Takagi–Sugeno (TS) type fuzzy logic controller to a three-phase shunt compensator in power distribution system. The shunt compensator is used for power quality improvement and has the ability to provide reactive power compensation, reduce the level of harmonics in supply currents, power factor correction and load balancing. Additionally, it can also be used to regulate voltage at the point of common coupling (PCC). The paper discusses the design of TS fuzzy logic controller and its implementation based on only four rules. The smaller number of rules makes it suitable for experimental verification as compared to Mamdani fuzzy controller. A small laboratory prototype of the system is developed and the control algorithm is verified experimentally. The TS fuzzy controller is compared with the proportional integral based industrial controller and their performance is compared under a wide variation of dynamic load changes.

Modified two-phase model with hybrid control for gas phase propylene copolymerization in fluidized bed reactors

In order to explore the dynamic behavior and process control of reactor temperature, a modified two-phase dynamic model for gas phase propylene copolymerization in a fluidized bed reactor is developed in which the entrainment of solid particles is considered. The modified model was compared with well-mixed and two-phase models in order to investigate the dynamic modeling response. The modified two-phase model shows close dynamic response to the well-mixed and two-phase models at the start of the polymerization, but begins to diverge with time. The proposed modified two-phase and two-phase models were validated with actual plant data. It was shown that the predicted steady state temperature by the modified two-phase model was closer to actual plant data compared to those obtained by the two-phase model. Advanced control system using a hybrid controller (a simple designed Takagi–Sugeno fuzzy logic controller (FLC)) integrated with the adaptive neuro-fuzzy inference system (ANFIS) controller was implemented to control the reactor temperature and compared with the FLC and conventional PID controller. The results show that the hybrid controller (ANFIS and FLC controller) performed better in terms of set point tracking and disturbance rejection compared to the FLC and conventional PID controllers.

Automated Implementation from Modeling to FPGA of Vehicle Autopilot

This paper demonstrates a new strategy for accelerating FPGA realization which is integrated design from model to automated HDL code generation, and it’s applied to a design of Takagi-Sugeno fuzzy logic control (FLC) Systems on vehicle autopilot based on FPGA. The system designed by this strategy accurately guides vehicle to the destination by controlling size and angle of speed.

Harmonics Mitigation Under Various Loading Conditions of PWM Current Controlled APF Using Fuzzy Logic Controller

The production of electric energy is basic pillar for normal functioning of every modern society. But in present day’s power distribution system is suffering from severe power quality problems. To reduce such problems we are using advanced technologies in power electronic devices (semi conductor devices). However these power equipments which include Adjustable Speed motor Drives (ASDs), electronic power supplies, direct current (DC) motor drives, battery chargers, electronic ballasts are responsible for the rise in related PQ problems. These non-linear loads are constructed by nonlinear devices. But these non-linear loads appear to be prime sources of harmonic distortion in power distribution system. Harmonic currents produced by non-linear loads are injected back into power distribution systems through the Point of Common Coupling (PCC). These harmonic currents can interact adversely with a wide range of power system equipment, most notably capacitors, transformers and motors causing additional losses, overheating and overloading. And also it can decrease the power quality in power distribution system. Traditionally, current harmonics caused by non-linear loads have been dealt with using passive filters consisting of capacitors, inductors and damping resistors. This paper mainly concentrates on proposing a new PWM current controlled Shunt Active Power Filter, to propose a simple current reference estimation method for the proposed topology, to design a Takagi-Sugeno (TS) type Fuzzy Logic Controller (FLC) to a three phase Shunt Active Power Filter which can decrease the harmonics so that the quality of power increases in power distribution systems. The first objective is achieved by designing a PWM current controlled Voltage Source Inverter which acts as a Shunt Active Filter for a three phase three wire system. For the second objective, this paper proposes the application of the extension instantaneous reactive-power (p-q) theorem to estimate the compensation current reference. The third objective is achieved by designing a Takagi-Sugeno (TS) Fuzzy Logic Controller with seven fuzzy sets for each input and output, triangular membership functions for simplicity, fuzzification using continuous universe of discourse and defuzzification using the centroid method. This paper will give the MATLAB/SIMULINK circuits of above proposals which interm gives the solutions to improve the power quality in power distribution systems.

Stability Analysis of Fuzzy Logic Control Systems for a Class of Nonlinear SISO Discrete-Time Systems

AbstractThis paper suggests a new stability analysis theorem dedicated to a class of fuzzy logic control systems. The fuzzy logic control systems consist of nonlinear Single Input-Single Output (SISO) discrete-time processes controlled by Takagi-Sugeno fuzzy logic controllers. The stability analysis is conducted on the basis of Lyapunov's direct method with quadratic Lyapunov function candidates. The theorem proves that if the derivative of the Lyapunov function candidate is negative definite in the active region of each fuzzy rule then the fuzzy logic control system will be asymptotically stable in the sense of Lyapunov. Sufficient stability conditions are offered. An example related to the design of a stable fuzzy logic control system for the discrete-time Lorenz chaotic system and simulation results are included.

Reconfigurable Takagi-Sugeno Fuzzy Logic Control for a Class of Nonlinear System considering Communication Time Delays on Peripheral Elements

Nowadays the study of faults and their consequences becomes an issue into highly safety critical computer network systems. How to bound the effects of a fault and how to tackle them into a dynamic system is still an open field. In here an approach to tackle this problem is presented. The use of Takagi-Sugeno fuzzy logic structure is given in order to accomplish two challenges, the presence of local faults and the respective time delays within a realtime distributed system. This approach is pursued as reconfigurable strategy according to communication time delays within a real-time distributed system. This approach is pursued as reconfigurable strategy according to communication delays.

CMOS CIRCUIT DESIGN OF A TAKAGI-SUGENO FUZZY LOGIC CONTROLLER

This paper presents a low power CMOS analog integrated circuit of a Takagi–Sugeno fuzzy logic controller with voltage/voltage interface, small chip area, relatively high accuracy and medium speed, which is composed of several improved functional blocks. Z-shaped, Gaussian and S-shaped membership function circuits with compact structures are designed, performing well with low power, high speed and small areas. A current minimization circuit is provided with high accuracy and high speed. A follower-aggregation defuzzification block composed of several multipliers for center of gravity (COG) defuzzification is presented without using a division circuit. Based on these blocks, a two-input one-output singleton fuzzy controller with nine rules is designed under a CMOS 0.6 μm standard technology provided by CSMC. HSPICE simulation results show that this controller reaches an accuracy of ±3% with power consumption of only 3.5 mW (at ±2.5 V). The speed of this controller goes up to 0.625M Fuzzy Logic Inference per Second (FLIPS), which is fast enough for real-time control.

Lorenz System Stabilization Using Fuzzy Controllers

The paper suggests a Takagi Sugeno (TS) fuzzy logic controller (FLC) designed to stabilize the Lorentz chaotic systems. The stability analysis of the fuzzy control system is performed using Barbashin-Krasovskii theorem. This paper proves that if the derivative of Lyapunov function is negative semi-definite for each fuzzy rule then the controlled Lorentz system is asymptotically stable in the sense of Lyapunov. The stability theorem suggested here offers sufficient conditions for the stability of the Lorenz system controlled by TS FLCs. An illustrative example describes the application of the new stability analysis method.

Design of robust fuzzy neural network controller with reduced rule base

Designing an effective learning algorithm to find the best rule and optimal structure is a major problem in building fuzzy controllers. This paper contributes a new alternative for the synthesis of Takagi-Sugeno fuzzy logic controller with reduced rule base. A hybrid learning algorithm called Hybrid Approach to Fuzzy Supervised Learning (HAFSL) which combines the Multiobjective Genetic Algorithms (MGA) and gradient descent technique is proposed for constructing a Robust Fuzzy Neural Network Controllers (RFNNC). This RFNNC is similar to nonlinear PI/PD controllers. Two phases of design and learning process are presented in this work. An MGA is used for finding near optimal structure/parameters of the fuzzy neural controller that minimises the number of rules (initialization procedure). The second stage of learning algorithm uses the backpropagation algorithm based on gradient descent method to fine tune some scaling factors, membership functions and the consequent parameters of the proposed controller. The genes of chromosome are arranged into two parts, the first part contains the control genes and the second part contains the genes parameters representing the fuzzy knowledge base. The effectiveness of this chromosome formulation enables the fuzzy sets and rules to be optimally reduced. Simulations demonstrate that the proposed robust control has successfully met the design specifications for efficiency and robustness, respectively.

How to optimize the TS‐fuzzy knowledge base to achieve desired performances: Accuracy and robustness

AbstractDesigning an effective criterion/learning to find the best rule and optimal structure is a major problem in the design process of fuzzy neural controller. In this paper, we introduce a new robust model of Takagi Sugeno fuzzy logic controller. A hybrid learning algorithm, called hybrid approach to fuzzy supervised learning (HAFSL), which combines the genetic algorithm (GA) and gradient descent technique (GD) is proposed for constructing an efficient and robust fuzzy neural network controller (FNNC). Two phases of design and learning process are presented in this work. A GA is used for finding near optimal structure/parameters of the FNNC that minimizes the number of rules (initialization procedure). The second stage of learning algorithm uses the backpropagation algorithm based on GD method to fine tune the consequent parameters of the controller. The genes of chromosome are arranged into two parts, the first part contains the control genes (the certainty factors) and the second part contains the parameters genes that representing the fuzzy knowledge base. The effectiveness of this chromosome formulation enables the fuzzy sets and rules to be optimally reduced. The performances of the HAFSL are compared to these found by the traditional PI with genetic optimization (GA‐PI). Simulations demonstrate that the proposed HAFSL and GA‐PI algorithms have good generalization capabilities and robustness on the water bath temperature control system. Copyright © 2007 John Wiley & Sons, Ltd.

SYSTEMATIC DESIGN PROCEDURE OF TS-TYPE FUZZY CONTROLLERS

This paper contributes a new alternative for the synthesis of Takagi–Sugeno fuzzy logic controller with reduced rule base. A Genetic Approach to Fuzzy Supervised Learning algorithm called GAFSL based on the Multiobjective Genetic Algorithms (MGAs) is used to construct the proposed robust fuzzy controller. The result controller is similar to nonlinear PI/PD controllers. The tuning algorithm cannot only tune the scaling factors, the shapes of membership functions, and the consequent values, but also optimize the number of rules as possible with guaranteed desired performances: accuracy and robustness. The construction of the chromosomes is based on the mixed binary–real coding system. The genes of chromosome are arranged into two parts, the first part contains the control genes (the certainty factors) and the second part contains the parameters genes that represent the fuzzy knowledge base. The concept of elite strategy is adopted, where the best individuals in a population are regarded as elites. Computer simulation results on two nonlinear problems that are derived to demonstrate the powerful GAFSL algorithm.

Hybrid Fuzzy Control of Robotics Systems

This paper presents a new approach towards optimal design of a hybrid fuzzy controller for robotics systems. The salient feature of the proposed approach is that it combines the fuzzy gain scheduling method and a fuzzy proportional-integral-derivative (PID) controller to solve the nonlinear control problem. The resultant fuzzy rule base of the proposed controller can be decomposed into two layers. In the upper layer, the gain scheduling method is incorporated with a Takagi-Sugeno (TS) fuzzy logic controller to linearize the robotics system for a given reference trajectory. In the lower layer, a fuzzy PID controller is derived for all the locally linearized systems by replacing the conventional PI controller by a linear fuzzy logic controller, which has different gains for different linearization conditions. Within the guaranteed stability region, the controller gains can be optimally tuned by genetic algorithms. Simulation studies on a pole balancing robot and a multilink robot manipulator demonstrate the effectiveness and robustness of the proposed approach.

터보제트엔진의 퍼지제어기 설계 및 다목적함수 만족기법을 통한 제어성능 향상에 관한 연구

터보제트엔진 모델에 대한 제어에 있어서, 비교적 잘 설계된 PI 제어기 성능결과를 바탕으로 Takagi-Sugeno형 뉴로-퍼지 추론계를 통한 플랜트 모델의 제어 시스템을 규명함으로서, PI형 T-S 퍼지규칙들을 퍼지제어기를 설계하였다. 이렇게 설계된 제어기의 성능을 향상시키기 위하여, 각 퍼지규칙들을 퍼지 C-Means Algorithm으로부터 각각의 목적 함수군으로 분류한 후, 각 분류군에 대해 규칙간의 가중치가 각 목적함수의 만족도에 부합되도록 하는 기법을 제시하였고, 이를 잘 설계된 T-S형 퍼지제어기에 적용하여 성능을 향상시킴으로써 그 유용성을 보였다. In the study of control technique for a turbojet engine model, the Takagi-Sugeno fuzzy logic controller has been designed based on the model identification by the well designed PI controlled system through T-S neuro-fuzzy inference system. To enhance this designed controller, those procedures are proposed that certainty factors are adopted to each rule of objective groups which are classified by the fuzzy C-Means algorithm and the satisfaction degrees are matched to meet the objectives. This proposed technique shows its feasibility by upgrading performances of the previously well-designed T-S fuzzy controller.

Adaptive fuzzy control of electrically stimulated muscles for arm movements.

A modified adaptive Takagi-Sugeno (TS) fuzzy logic controller (FLC) is proposed that allows a simulated elbow-like biomechanical system to accurately track sigmoidal and sinusoidal trajectories in the sagittal plane. The work is a first effort towards the implementation of a system to restore elbow movements in quadriplegics using functional neuromuscular stimulation. The single-joint musculo-skeletal system is composed of a co-contractable pair of electrically stimulated muscles; the muscle model accounts for the increase in fatigue during the tracking exercise. In the proposed controller structure, a reinforcement learning scheme is used to accomplish the parameter tuning, and the parameter projection algorithm guarantees the system stability during the adaptation process. The controller performance is evaluated using computer simulation experiments and compared with the performance achievable when a standard proportional-integrative-derivative (PID) controller is employed for the same application. The modified adaptive TSFLC outperforms the PID controller in all tested situations, with a clear-cut advantage in the case of high-frequency sinusoidal trajectories (angular frequencies spanning the interval 8-12 rad s-1). The standard controller suffers from a dramatic increase in root mean square (RMS) tracking error above the value at 8 rad s-1, e.g. ERMS > or = 0.013, whereas the correlation coefficient between the actual and desired trajectory falls almost to zero, starting from the value rho approximately equal to 0.97 at 8 rad s-1. On the other hand, the adaptive TSFLC yields ERMS < or = 0.015, with rho > or = 0.78, over the whole range of tested angular frequencies.

Depression with and without concurrent panic attacks: Differences in thyroid economy

This paper proposes a hybrid controller that consists of a second order data–driven Model–Free Control (MFC), also known as intelligent proportional–integral (iPI), and a Takagi-Sugeno Fuzzy (TSF) logic controller for nonlinear Multi Input–Multi Output (MIMO) twin rotor aerodynamic systems (TRASs). The pitch position control is carried out using a single input–single output control system structure. The hybrid controller (the second order MFC algorithm and the TSF logic controller) is referred to as second order MFC–TSF controller. The performance of the control system structure (CSS) with the new second order MFC–TSF controller is compared with the performance of a CSS with the second order MFC controller, which is optimally tuned in a model-based manner by a Grey Wolf Optimizer algorithm. Experimental validation results on a nonlinear TRAS laboratory equipment are included.