A Method for Embedding Human Expert Knowledge Into a Fuzzy Logic Controller

1995 ◽  
Author(s):  
John R. Canning ◽  
Dean B. Edwards
Keyword(s):  
Fuzzy Logic ◽  
Classification System ◽  
Fuzzy Logic Controller ◽  
Expert Knowledge ◽  
Autonomous Vehicle ◽  
Sensor Data ◽  
Rule Base ◽  
Terrain Classification ◽  
Human Expert ◽  
Terrain Features

Abstract This paper presents a method for embedding human expert knowledge into a fuzzy logic controller. The method was developed while designing a fuzzy logic control system for an autonomous vehicle that utilized sparse sensor data for terrain classification. We will discuss the design for the fuzzy logic terrain classification system and use it as an example for explaining the embedding method. In the example, a human expert classified terrain features from sensor data provided by a two-dimensional computer simulation. From the information derived from the expert, we developed both a classification system for the terrain features and a fuzzy logic rule base for the controller. The simulation, along with an optimization algorithm, was then used to train the fuzzy logic controller to match the human responses.

Implementation of Fuzzy Logic Control on Battery Charging System

2019 ◽  
Vol 3 (1) ◽  
pp. 118-126 ◽  
Author(s):  
Prihangkasa Yudhiyantoro
Keyword(s):  
Complex System ◽  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Expert Knowledge ◽  
Experimental Result ◽  
Rule Base ◽  
Battery Charging ◽  
Charging System ◽  
Logic Control ◽  
Charging Control

This paper presents the implementation fuzzy logic control on the battery charging system. To control the charging process is a complex system due to the exponential relationship between the charging voltage, charging current and the charging time. The effective of charging process controller is needed to maintain the charging process. Because if the charging process cannot under control, it can reduce the cycle life of the battery and it can damage the battery as well. In order to get charging control effectively, the Fuzzy Logic Control (FLC) for a Valve Regulated Lead-Acid Battery (VRLA) Charger is being embedded in the charging system unit. One of the advantages of using FLC beside the PID controller is the fact that, we don’t need a mathematical model and several parameters of coefficient charge and discharge to software implementation in this complex system. The research is started by the hardware development where the charging method and the combination of the battery charging system itself to prepare, then the study of the fuzzy logic controller in the relation of the charging control, and the determination of the parameter for the charging unit will be carefully investigated. Through the experimental result and from the expert knowledge, that is very helpful for tuning of the  embership function and the rule base of the fuzzy controller.

scholarly journals Experts versus Algorithms? Optimized Fuzzy Logic Energy Management of Autonomous PV Hybrid Systems with Battery and H2 Storage

Energies ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1777
Author(s):  
Lisa Gerlach ◽  
Thilo Bocklisch
Keyword(s):  
Fuzzy Logic ◽  
Energy Management ◽  
Fuzzy Logic Controller ◽  
Expert Knowledge ◽  
Large Power ◽  
Hybrid Energy ◽  
Knowledge Based ◽  
Long Term Storage ◽  
Fuzzy Logic Approach

Off-grid applications based on intermittent solar power benefit greatly from hybrid energy storage systems consisting of a battery short-term and a hydrogen long-term storage path. An intelligent energy management is required to balance short-, intermediate- and long-term fluctuations in electricity demand and supply, while maximizing system efficiency and minimizing component stress. An energy management was developed that combines the benefits of an expert-knowledge based fuzzy logic approach with a metaheuristic particle swarm optimization. Unlike in most existing work, interpretability of the optimized fuzzy logic controller is maintained, allowing the expert to evaluate and adjust it if deemed necessary. The energy management was tested with 65 1-year household load datasets. It was shown that the expert tuned controller is more robust to changes in load pattern then the optimized controller. However, simple readjustments restore robustness, while largely retaining the benefits achieved through optimization. Nevertheless, it was demonstrated that there is no one-size-fits-all tuning. Especially, large power peaks on the demand-side require overly conservative tunings. This is not desirable in situations where such peaks can be avoided through other means.

scholarly journals Design and Lyapunov Stability Analysis of a Fuzzy Logic Controller for Autonomous Road Following

2010 ◽  
Vol 2010 ◽  
pp. 1-20 ◽  
Author(s):  
Yi Fu ◽  
Howard Li ◽  
Mary Kaye
Keyword(s):  
Fuzzy Logic ◽  
Stability Analysis ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Intelligent Vehicles ◽  
Intelligent Vehicle ◽  
Rule Base ◽  
Control Laws ◽  
Robotic Vehicle ◽  
Road Following

Autonomous road following is one of the major goals in intelligent vehicle applications. The development of an autonomous road following embedded system for intelligent vehicles is the focus of this paper. A fuzzy logic controller (FLC) is designed for vision-based autonomous road following. The stability analysis of this control system is addressed. Lyapunov's direct method is utilized to formulate a class of control laws that guarantee the convergence of the steering error. Certain requirements for the control laws are presented for designers to choose a suitable rule base for the fuzzy controller in order to make the system stable. Stability of the proposed fuzzy controller is guaranteed theoretically and also demonstrated by simulation studies and experiments. Simulations using the model of the four degree of freedom nonholonomic robotic vehicle are conducted to investigate the performance of the fuzzy controller. The proposed fuzzy controller can achieve the desired steering angle and make the robotic vehicle follow the road successfully. Experiments show that the developed intelligent vehicle is able to follow a mocked road autonomously.

Approximated Simplest Fuzzy Logic Controlled Shunt Active Power Filter for Current Harmonic Mitigation

2013 ◽  
pp. 155-171
Author(s):  
Rambir Singh ◽  
Asheesh K. Singh ◽  
Rakesh K. Arya
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Active Power Filter ◽  
Pi Controller ◽  
Active Power ◽  
Rule Base ◽  
Shunt Active Power Filter ◽  
Harmonic Compensation ◽  
Power Filter ◽  
Harmonic Mitigation

This paper examines the size reduction of the fuzzy rule base without compromising the control characteristics of a fuzzy logic controller (FLC). A 49-rule FLC is approximated by a 4-rule simplest FLC using compensating factors. This approximated 4-rule FLC is implemented to control the shunt active power filter (APF), which is used for harmonic mitigation in source current. The proposed control methodology is less complex and computationally efficient due to significant reduction in the size of rule base. As a result, computational time and memory requirement are also reduced significantly. The control performance and harmonic compensation capability of proposed approximated 4-rule FLC based shunt APF is compared with the conventional PI controller and 49-rule FLC under randomly varying nonlinear loads. The simulation results presented under transient and steady state conditions show that dynamic performance of approximated simplest FLC is better than conventional PI controller and comparable with 49-rule FLC, while maintaining harmonic compensation within limits. Due to its effectiveness and reduced complexity, the proposed approximation methodology emerges out to be a suitable alternative for large rule FLC.

INTERVAL-VALUED FUZZY LOGIC CONTROL FOR A CLASS OF DISTRIBUTED PARAMETER SYSTEMS

2007 ◽  
Vol 15 (04) ◽  
pp. 457-481 ◽  
Author(s):  
XIAN-XIA ZHANG ◽  
SHAO-YUAN LI ◽  
HAN-XIONG LI
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Spatial Information ◽  
Packed Bed ◽  
Distributed Parameter Systems ◽  
Packed Bed Reactor ◽  
Distributed Parameter ◽  
Rule Base ◽  
Space Domain ◽  
Interval Valued

An interval-valued fuzzy logic controller (I-V FLC) is presented to control a class of nonlinear distributed parameter systems. The proposed FLC is inspired by human operators' knowledge or expert experience to control a distributed parameter process from the point of view of overall space domain. Based on spatial fuzzy set, the I-V FLC employs a centralized rule base over the space domain. Using spatial membership degree fusion operation, the I-V FLC can compress spatial input information into interval-valued fuzzy sets and then execute an interval-valued rule inference mechanism; thereby the I-V FLC has the capability to process spatial information over the space domain. Compared with traditional FLCs, the I-V FLC can improve its control performance due to its increased ability to express and process spatial information. The I-V FLC is successfully applied to a catalytic packed-bed reactor and compared with the traditional FLCs. The results demonstrate its effectiveness to control the unknown nonlinear distributed parameter process.

Fuzzy Logic Control of the End-Point Vibration in an Experimental Flexible Beam

2004 ◽  
Vol 10 (4) ◽  
pp. 493-506 ◽  
Author(s):  
A. Jnifene ◽  
W Andrews
Keyword(s):  
Fuzzy Logic ◽  
Strain Gage ◽  
Degrees Of Freedom ◽  
Fuzzy Logic Controller ◽  
Angular Displacement ◽  
Angular Position ◽  
Rule Base ◽  
Flexible Beam ◽  
End Point ◽  
Logic Control

This paper is concerned with the design and implementation of a fuzzy logic controller (FLC) to control the end-point vibration in a single flexible beam mounted on a two-degrees-of-freedom platform. The angular position of the hub and the signal from a strain gage mounted on the beam are used as the two inputs to the FLC. In order to add more damping, the strain gage signal is combined with the hub angular velocity represented by the output of a tachometer attached to the motor shaft. We discuss how to build the rule base for the flexible beam based on the relation between the angular displacement of the hub and the end-point deflection, as well as the effect of different scaling gains on the performance of the FLC. We present several experimental results showing the effectiveness of the FLC in reducing the end-point vibration of the flexible beam.

Neural Network Based Intelligent Learning of Fuzzy Logic Controller Parameters

2004 ◽  
Author(s):  
Manish Kumar ◽  
Devendra P. Garg
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Network Architecture ◽  
Fuzzy Logic Controller ◽  
Rule Base ◽  
Neural Network Architecture ◽  
The Neural Network ◽  
Neuro Fuzzy ◽  
Learning Capabilities ◽  
Optimal Signal

Design of an efficient fuzzy logic controller involves the optimization of parameters of fuzzy sets and proper choice of rule base. There are several techniques reported in recent literature that use neural network architecture and genetic algorithms to learn and optimize a fuzzy logic controller. This paper presents methodologies to learn and optimize fuzzy logic controller parameters that use learning capabilities of neural network. Concepts of model predictive control (MPC) have been used to obtain optimal signal to train the neural network via backpropagation. The strategies developed have been applied to control an inverted pendulum and results have been compared for two different fuzzy logic controllers developed with the help of neural networks. The first neural network emulates a PD controller, while the second controller is developed based on MPC. The proposed approach can be applied to learn fuzzy logic controller parameter online via the use of dynamic backpropagation. The results show that the Neuro-Fuzzy approaches were able to learn rule base and identify membership function parameters accurately.

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