Fuzzy Logic Switching Research Articles

Model predictive control with fuzzy logic switching for path tracking of autonomous vehicles

This paper introduces an integrated path tracking control strategy for autonomous vehicles. The proposed control strategy is based on a multi-input multi-output linear model predictive control (LMPC) with a fuzzy logic switching system. The designed MPC is based on Laguerre networks. The main target of the designed MPC is to produce the optimal control signals of the steering angle and the angular velocity while considering the physical constraints of the control signals and the measurements noise. Since the vehicle model is highly nonlinear and is operated over a wide range of operating points, different linearized models are obtained. The controller parameters for each linear model are designed and tuned. The gab metric analysis is used to select a number of these models to simplify the design of the proposed controller. Then, these models are combined using a fuzzy logic controller to switch between them. To test the proposed controller performance, different paths are generated using path planning algorithms. These paths simulate different vehicle maneuvers scenarios. The simulation results show that the designed tracking controller has a tracking performance on different designed paths better than that of a Linear quadratic gaussian (LQG) tracking controller, discussed in this paper.

Statistical data analysis for harmonic reduction in 3Ø -fragmented source using novel fuzzy digital logic switching technique

Purpose The purpose of this paper is to analyze the Luo super boost converter coupled fragmented source inversion system (LC-FSIS) and the progress of a controller structure for energy stored. The inversion system is characterized by a diode arm structure and can be easily amassed into a conversion system for high/medium- power conversion systems.Design/methodology/approach The investigation is based on the practice of a simplified circuit established as common anode/common cathode, where all the diodes in each arm are connected to a renewable DC voltage source. In this proposed work, a novel fuzzy digital logic switching technique (FDLST) for three-phase fragmented source inversion (FSI) for enhancement in power excellence is measured to enterprise the novel fuzzy digital logic switching technique to authorize operative voltage utilization and enhanced harmonic spectrum.Findings Sequential circuit design using flip-flops is used in the analysis of fuzzy digital logic switching technique.Originality/value The three-phase fragmented source configuration is designed using a split DC source which is obtained from the Opto-electric source and is implemented using MOSFET. The procedure of novel FDLST reduces the Statistical Harmonic Reduction (SHR). Simulation and results are carried out to prove the dominance of designed FDLST.

Fatigue load mitigation in wind turbine using a novel anticipatory predictive control strategy

Wind energy has become a recent evolving source of renewable energy which needs an extensive exploration. The increased configuration of wind turbine instantaneously increases the structural loading of wind turbine components. The prolonged loading on structural components imposes a great impact on the lifetime of the wind turbine and reduces the feasibility of thinner rotor blade. These economical detriments due to the structural damage of turbine are dealt effectively by proposing an anticipatory preview-based control strategy for individual pitching of blades. The proposed multiple point model predictive controller with fuzzy logic switching, exploits the preview wind measurement from the light detection and ranging system which predicts the wind speed in advance. The utmost objective of the proposed controller is its optimized individual blade pitching to reduce the fatigue loads on the wind turbine components using the preview wind data. The blade root bending moment of individual blades and the tower fore-aft deflection which are chosen as the parameters of fatigue stress to be analyzed and controlled. The National Renewable Energy Laboratory’s Fatigue, Aerodynamics, Structures and Turbulence 5 MW baseline onshore wind turbine is used for validating the proposed controller in MATLAB simulation. The non-linear wind field is simulated using the TurbSim software, and the short-term and lifetime damage equivalent loads of the wind turbine for the simulated wind field is analyzed using the MLife software of the National Renewable Energy Laboratory. The proposed controller is compared with the typical model predictive controller and linear quadratic-based controller.

Double‐Target Switching Control of Vehicle Longitudinal Low‐Frequency Vibration Based on Fuzzy Logic

Rapid increase of vehicle longitudinal acceleration is required in an engine torque increasing phase, whereas little overshoot and oscillating acceleration are required in a torque holding phase. These two features give satisfying results with respect to both drivability and comfortability. However, when subjected to a sudden torque change in the tip‐in condition, the driveline undergoes strong low‐frequency torsional vibration which has an adverse impact on vehicle comfortability. Normally, a linear quadratic (LQ) controller has a good comfort performance in reducing the vibration but with negative impact on the dynamic response of the vehicle which weakens the drivability. The two different performance demands in the two phases cannot be achieved simultaneously by only adjusting the weighting coefficients of the LQ controller. Therefore, a new control strategy decoupling the two phases is necessary and proposed in this paper. A linear quadratic regulator (LQR) is used in the torque increasing phase for dynamic response demand while a linear quadratic tracking (LQT) controller is applied in the torque holding phase for comfortability demand. The two controllers are switched smoothly via a fusion weighting factor based on the proposed fuzzy logic switching strategy. A quantitative evaluation method is used to evaluate the performances of the proposed control strategy. The results show that the double‐targets switching control keeps better performances in both drivability and comfortability. The comfortability index of the proposed strategy is improved by 79.74% compared with that of the LQT whereas the dynamic response index is improved by 21.88% compared with that of the LQR.

Robust pole placement for power systems using two‐dimensional membership fuzzy constrained controllers

This study presents a design method of power system stabilisers (PSSs) to damp system oscillations. The design is based on fuzzy logic and linear matrix inequality (LMI) techniques. The design guarantees a robust pole placement in a desired region in the complex plane for different loading conditions. Due to system non-linearity and load dependency, the whole wide range of operating condition is divided into three subregions. For each subregion, a norm-bounded uncertainty linear model is developed and a robust LMI-based pole placer is designed considering controller constraint. Takagi–Sugeno fuzzy model is used to guarantee smoothly switching between the LMI-based controllers of the three subregions. The proposed technique avoids solving many LMIs as in the polytypic approach and avoids the infeasibility problem that might arise from solving the LMIs for the complete region of operation. In addition, the proposed fuzzy logic switching controller has reduced drastically the fuzzy rules due to the use of two-dimensional (2D) membership functions. Particle swarm optimisation is used to tune the proposed 2D membership functions to guarantee smooth switching of the controllers while maintaining the desired constraints. The simulation results of both single-machine and multi-machine power systems confirm the effectiveness of the proposed PSS design.

Fuzzy Logic Application for Intelligent Control of An Asynchronous Machine

<p>The aim of this article is propose a method to improve the direct torque control and design a Fuzzy Logic based Controller which can take necessary control action to provide the desired torque and flux of an asynchronous machine. It’s widely used in the industrial application areas due to several features such as fast torque response and less dependence on the rotor parameters. The major problem that is usually associated with DTC control is the high torque ripple as it is not directly controlled. The high torque ripple causes vibrations to the motor which may lead to component lose, bearing failure or resonance. The fuzzy logic controller is applied to reduce electromagnetic torque ripple. In this proposed technique, the two hysteresis controllers are replaced by fuzzy logic controllers and a methodology for implementation of a rule based fuzzy logic controller are presented. The simulation by Matlab/Simulink was built which includes induction motor d-q model, inverter model, fuzzy logic switching table and the stator flux and torque estimator. The validity of the proposed method is confirmed by the simulative results of the whole drive system and results are compared with conventional DTC method. </p>

Improved Sensorless Direct Torque Control of Induction Motor Using Fuzzy Logic and Neural Network Based Duty Ratio Controller

This paper presents improvements in Direct Torque control of an induction motor using Fuzzy logic with Fuzzy logic and neural network based duty ratio controller. The conventional DTC (CDTC) of induction motor suffers from major drawbacks like high torque and flux ripples and poor transient response. Torque and flux ripples are reduced by replacing hysteresis controller and switching table with Fuzzy logic switching controller (FDTC). In FDTC the selected switching vector is applied for the complete switching time period. The FDTC steady state performance can be improved by using duty ratio controller, the selected switching vector is applied only for the time determined by the duty ratio (δ) and for the remaining time period zero switching vector is applied. The selection of duty ratio using Fuzzy logic and neural networks is projected in this paper. The effectiveness proposed methods are evaluated using simulation by Matlab/Simulink.

Improvements in direct torque control of induction motor for wide range of speed operation using fuzzy logic

The main objective of this paper is to ameliorate the performance of direct torque control of induction motor for a wide range of speed control using fuzzy logic. The major drawbacks of conventional direct torque control are high torque, flux ripples and poor dynamic response at low speed. In this paper the CDTC is improved by fuzzy logic switching controller and fuzzy logic duty cycle controller. In FDTC with FDCC the selected voltage vector is applied only for the duty cycle time period (δ) determined. Simulation results validate that the proposed scheme shows improved dynamic performance of the drive for a wide range of speed (0–100rad/s) and the torque and flux ripples are reduced by 90% compared to CDTC.

Sensorless Direct Torque Control of Induction Motor Using AI Based Duty Ratio Controllers

This paper presents some improvements in direct torque control of an induction motor using fuzzy logic switching controller along with fuzzy logic and neural network based duty ratio controller. The conventional direct torque control of induction motor suffers from major drawbacks like high torque and flux ripples, current and torque distortion when sector changes and poor transient response. High torque and flux ripples are reduced to some extent by replacing hysteresis controller and switching table with fuzzy logic switching controller (FDTC). However, in FDTC the selected switching vector is applied for the complete switching time period which results in ripples under steady state. The FDTC steady state performance is improved by using duty ratio controller. Using duty ratio controller, the selected switching vector is applied to voltage source inverter only for the time determined by the duty ratio (δ) and for the remaining time, a zero switching vector is applied. As the switching vector is applied for only the time period till torque reaches its reference value, it results in the reduction of torque and flux ripples of induction motor. The selection of duty ratio is a nonlinear function of flux error, flux error and stator flux angle which is effectively implemented in this paper using the following artificial intelligence techniques: fuzzy logic and neural networks. The effectiveness of the proposed methods are evaluated by the simulation with Matlab/Simulink.

A novel multiple reference model adaptive control approach for multimodal and dynamic systems

This paper presents a fuzzy multiple-reference-model generator-based Model Reference Adaptive Control (MRAC) framework for controlling systems that perform a wide range of operating conditions. Following a rule base, the Fuzzy Logic Switching Scheme (FLSS) effectively monitors changes in operating conditions or such drastic changes in plant parameters, and generates a fuzzified reference model output. Then, a single adaptive controller forces the plant output to track the reference, even when plant mode changes. The proposed fuzzy switching Multiple Reference Model Adaptive Controller (MRMAC) is effective as well as feasible for online application, monitoring the plant output at selected control intervals. Unlike static multiple-model algorithms for switching (individual model-based filters do not interact) or switching dynamic algorithms (which are susceptible to numerical overflow), this scheme provides an interactive multiple model generator with soft switching. The strength of the scheme is demonstrated by an application to a theoretical system with disturbed model parameters and for the position tracking of a single-link manipulator. Investigation results show that the proposed scheme performs very positively at different operating modes.

SMES有効無効電力のファジィ論理型制御による電力系統安定化制御実験

A Superconducting Magnetic Energy Storage (SMES) is proposed to be used the stabilizing control of power systems. By simulations, we have been studying its control effect, the required energy for power system stabilization, and so on. We have also proposed the power system stabilizing control by SMES incorporated with the fuzzy logic switching control. Recently, a systematically designed 50kJ SMES system is installed at Kumamoto University. We have investigated the stabilizing effect of the proposed fuzzy logic switching control by means of experiments with the SMES system and a one-machine infinite-bus system. In the pervious paper, the experimental results about the SMES active power control is mentioned. In this paper, the experimental results of a simultaneous control of the SMES active and reactive power, which is incorporated with the fuzzy logic switching strategy, are shown and discussed. We make clear the effect of power system stabilizing control incorporated with the fuzzy logic switching control by SMES.

Coordinated fuzzy logic control between SVC and PSS to enhance stability of power systems

AbstractThis paper presents a new switching control scheme for static var compensator (SVC) using fuzzy logic control rules to enhance the overall stability of electric power systems. In addition, the coordination with power system stabilizers (PSS) is also considered to achieve a wider stable region. An SVC is set on one of the busbars in the transmission system, where the real power flow signal is utilized at the location of the SVC to determine the firing angle of the thyristor switch. The switching control scheme is simple so as not to require heavy computation on the microcomputer based switching controller. The PSSs are also set on the generators in the study system. Simulation results show the effectiveness of the proposed fuzzy logic switching control scheme for the SVC. The coordination between SVC and PSS is also effective to enlarge the stable region.

Fuzzy logic switching of thyristor controlled braking resistor considering coordination with SVC

This paper presents a new switching control scheme for braking resistors (BR) using fuzzy logic to enhance overall stability of electric power systems. In addition, the coordination with an SVC is also considered to achieve a wider stable region. The braking resistor is set on one of the generator busbars, where the real power output from the generator is measured to determine the firing-angle of the thyristor switch. The switching control scheme is simple so as not to require heavy computation on the microcomputer based switching controller. An SVC is set on one of the busbars in the system. The switching of the SVC uses a similar fuzzy logic control scheme to the one for the BR. Simulation results show the effectiveness of the proposed fuzzy logic switching control scheme. >

ファジー制御を適用した SVC と PSS との協調による電力系統安定化制御

This paper presents a new switching control scheme for static var compensator (SVC) using fuzzy logic control rules to enhance the overall stability of electric power systems. In addition, the coordination with power system stabilizers (PSS) is also considered to achieve a wider stable region. An SVC is set on one of the busbars in the transmission system, where the real power flow signal is utilized at the location of the SVC to determine the firing-angle of the thyristor switch. The switching control scheme is simple so as not to require heavy computation on the micro-computer based switching controller. PSS's are also set on the generators in the study system. Simulation results show the effectiveness of the proposed fuzzy logic switching control scheme for the SVC. The coordination between SVC and PSS is also effective to enlarge the stable region.