Determination of fuzzy logic membership functions using genetic algorithms

Arslan, Ahmet; Kaya, Mehmet

doi:10.1016/s0165-0114(99)00065-2

Cited by 138 publications

(57 citation statements)

References 7 publications

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“…Without preknown model knowledge and complicated designing progress under mathematic theory, the simple model-free controller maybe gain more extensive use. Unfortunately, as other controllers with GA optimization [11][12][13][14][15][16], it lacks instrict mathematic theory support especially convergence stability proof.…”

Section: Experimental Testmentioning

confidence: 99%

“…A knowledge base was learned from interval and fuzzy data for regression problems by applying the GA [14]. In addition, GA could be used to determine the membership functions in fuzzy systems [15,16]. Scaling parameters, which describe input normalizations and output denormalization, play a role similar to that of gain coefficients in conventional PID controllers [17].…”

Section: Introductionmentioning

confidence: 99%

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Simple Model-Free Controller for the Stabilization of Planetary Inverted Pendulum

Mai

Huang

Liao

et al. 2012

Journal of Control Science and Engineering

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A simple model-free controller is presented for solving the nonlinear dynamic control problems. As an example of the problem, a planetary gear-type inverted pendulum (PIP) is discussed. To control the inherently unstable system which requires real-time control responses, the design of a smart and simple controller is made necessary. The model-free controller proposed includes a swing-up controller part and a stabilization controller part; neither controller has any information about the PIP. Since the input/output scaling parameters of the fuzzy controller are highly sensitive, we use genetic algorithm (GA) to obtain the optimal control parameters. The experimental results show the effectiveness and robustness of the present controller.

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Section: Experimental Testmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Simple Model-Free Controller for the Stabilization of Planetary Inverted Pendulum

Mai

Huang

Liao

et al. 2012

Journal of Control Science and Engineering

View full text Add to dashboard Cite

show abstract

“…Several methods have been developed for tuning fuzzy controllers. These involve adjustment of the MF [27] and scaling factors [28] and dynamically changing the defuzzification Procedure. Therefore, the approach needs as many variables as there are rules to get an optimal rule base.…”

Section: Encoding Fuzzy Logic Controlmentioning

confidence: 99%

Evolutionary Design of Intelligent Controller for a Cement Mill Process

Subbaraj¹,

Anand²

2010

IJCA

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The Knowledge Base of a Fuzzy Logic Controller (FLC) encapsulates expert knowledge and consists of the Data Base (membership functions) and Rule-Base of the controller. Optimization of these Knowledge Base components is critical to the performance of the controller and has traditionally been achieved through a process of trial and error. Such an approach is convenient for FLCs having low numbers of input variables however for greater number of inputs, more formal methods of Knowledge Base optimization are required. Genetic Algorithms (GAs) provide such a method to optimize the FLC parameters. An intelligent multi input multi output (MIMO) control for the cement milling circuit is presented. The FLC is optimized by GA for varying nonlinearity in the plant. The proposed control algorithm was tested on the cement mill simulation model within MATLABTM SimulinkTM environment. Parameters of the simulation model were set up based on the actual cement mill characteristics. The performances of the proposed control technique are compared with various control technique. The results of the control study indicate that the proposed algorithm can prevent the mill from plugging and control the cement mill circuit effectively compared to the other control technique.

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“…Genetic algorithms (GAs) are a part of evolutionary computing, which is a rapidly growing area of artificial intelligence. GAs can be used to tune the membership functions, rule bases and scaling factors of fuzzy controllers [1,2,9,11,14,16]. In this paper, GAs are used in the determination of membership functions and scaling factors for the reverse-motion fuzzy logic controller only (which is the most complex of the three fuzzy controllers that comprise the proposed automatic parking algorithm).…”

Section: Introductionmentioning

confidence: 99%

Robust automatic parallel parking in tight spaces via fuzzy logic

Zhao

Collins

2005

Robotics and Autonomous Systems

113

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This paper develops and experimentally demonstrates a robust automatic parallel parking algorithm for parking in tight spaces. Novel fuzzy logic controllers are designed for each step of the maneuvering process. The controllers are first demonstrated by simulation using the kinematic model of a skid steering autonomous ground vehicle (AGV). They are then demonstrated experimentally on a skid steering AGV and it is shown that the developed algorithm has the ability to parallel park AGVs in tight spaces under both vehicle localization errors and parking space detection errors. This paper also presents a genetic fuzzy system which uses a genetic algorithm's learning ability to determine effective parameters for the developed fuzzy logic controllers. The genetic fuzzy system is used to tune the fuzzy logic algorithm for both a skid steering AGV and a front-wheel steering AGV.

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Determination of fuzzy logic membership functions using genetic algorithms

Cited by 138 publications

References 7 publications

Simple Model-Free Controller for the Stabilization of Planetary Inverted Pendulum

Simple Model-Free Controller for the Stabilization of Planetary Inverted Pendulum

Evolutionary Design of Intelligent Controller for a Cement Mill Process

Robust automatic parallel parking in tight spaces via fuzzy logic

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