A PI-type fuzzy controller with self-tuning scaling factors

Chung, Habong; Chen, Bor-Chin; Lin, J.

doi:10.1016/s0165-0114(96)00215-1

Cited by 66 publications

(37 citation statements)

References 4 publications

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“…We can find that any change in the SFs results in adjustment of the poles and zeros of the overall transfer function. This is the reason that changes in the SFs have a dramatic effect on the overall dynamics of the closed loop system [7]. We use the PD-like FLC; because it makes quick response with less oscillation than the PI-like FLC [1].…”

Section: Fuzzy Controller With Self Tuning Scaling Factorsmentioning

confidence: 99%

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An Auto-Tuning Method for the Scaling Factors of Fuzzy Logic Controllers with Application to SISO Mechanical System

Nasser¹,

Sharkawy²,

Soliman³

2015

IJMMM

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I. INTRODUCTIONFor enhancing control systems, there are two important information sources: sensors, which provide measurements of variables, and human experts who give linguistic instructions and descriptions about the system. Fuzzy logic controller (FLC) was created to combine these two different types of information by handling information coming from human operators. The main advantage of the FLC is that it can be applied to plants that are difficult to model mathematically [1]. Self-tuning of a FLC aims to adapt the controller to different operating conditions [2]. For successful design of a FLC, proper selection of input and output scaling factors and/or the tuning of other controller parameters, such as the representation and construction of the rule base or the determination of the position and shape of the membership functions, are conclusive jobs [2]. Basically, there are two different tuning approaches to achieve optimal parameters for a FLC: on-line and off-line tuning [2]. Off-line tuning scaling factor using genetic algorithm optimization method can be found in [3]. On-line tuning membership function using gradient descent optimization method is discussed in In this work, we introduce an auto-tuning mechanism for the scaling factors of a PD-type FLC. The gradient descent method is employed to optimally determine them on-line. This algorithm along with other classical controllers is tested experimentally using an inverted pendulum mounted on a cart. The inverted pendulum is a highly nonlinear and open-loop unstable system [13], [14]. Inverted pendulum system is often used as a benchmark for verifying the performance and effectiveness of control algorithms because of the simplicity of its structure [15]. Results show the effectiveness of the proposed control system. The paper is organized as follows. Section II introduces the mathematical model of the inverted pendulum. Section III gives a brief description about classical controllers which are experimentally tested in this work. In Section IV, we derive the auto-tuning algorithm for the scaling factors of the FLC. Section V describes the experimental setup. Section VI discusses the experimental results and Section VII offers our concluding remarks. II. INVERTED PENDULUM MODELThis Section provides description of the inverted pendulum used in this study. Fig. 1 shows the free body diagram. Using Newton's second law, it can be easily shown that the dynamic equations of motion are as follow: The symbols used in (1) and (2) and their numerical values are defined (see Table I).An Auto-Tuning Method for the Scaling Factors of Fuzzy Logic Controllers with Application to SISO Mechanical System Gamal Abdel Nasser, Abdel Badie Sharkawy, and M-Emad S. SolimanInternational Journal of Materials, Mechanics and Manufacturing, Vol. 3, No. 1, February 2015 The linearized model equations areWe used equations (3) and (4) for solving the Riccati equation in LQR controller to get the optimal state feedback gains. III. CLASSICAL CONTROL ALGORITHMS A. LQR (Liner Quadra...

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Section: Fuzzy Controller With Self Tuning Scaling Factorsmentioning

confidence: 99%

“…On-line tuning membership function using gradient descent optimization method is discussed in [4]- [6]. On-line tuning scaling factor using fuzzy tuner is demonstrated in [7]- [9]. The authors of [10] have used Neuro-Fuzzy tuner and in [1], [11], [12] used the gradient descent optimization method.…”

Section: Introductionmentioning

confidence: 99%

An Auto-Tuning Method for the Scaling Factors of Fuzzy Logic Controllers with Application to SISO Mechanical System

Nasser¹,

Sharkawy²,

Soliman³

2015

IJMMM

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“…Additionally, modification of many parameters at a time easily results in big computation cost. So, the definition of membership functions and the establishment of control rules base are usually designed subjective [29]. Different experts maybe obtain different experiences.…”

Section: Design Of Input/output Scaling Factorsmentioning

confidence: 99%

“…For a successful design of FLC, proper selection of input and output scaling factors is critical tasks, which in many cases, is performed through trial and error or based on some training data [18]. An interesting fuzzy controller which include seven rules was proposed to for self-tuning its scaling factors [19]. To tune the optimal parameters of the fuzzy controller at some operating points, Serra and Bottura used GA off-line to get the optimal scaling parameters [20].…”

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

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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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“…Then several adaptive structures associated with fuzzy controllers were disclosed. The most popular one among them is the system involving a self-tuning method [23,24,25,26,27,28], which adjusts the input-output scaling factors or membership functions during the operation.…”

Section: Historical Backgroundmentioning

confidence: 99%

Fuzzy control of an ANFIS model representing a nonlinear liquid-level system

Engin

Kuvulmaz

Ömurlü

2004

Neural Comput & Applic

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This paper aims to serve two main objectives; one is to demonstrate the modelling capabilities of a neuro-fuzzy approach, namely ANFIS (adaptive-network based fuzzy inference system) to a nonlinear system; and the other is to design a fuzzy controller to control such a system. The nonlinear system, which is a liquid-level system, is represented first by its mathematical model and then by ANFIS architecture. The ANFIS model is formed by means of input-output data set taken from the mathematical model. Then a PIDtype fuzzy controller, which linguistically approximates the classical three-term compensation, was designed to control the system represented by both its mathematical and ANFIS models in order to perform an agreement comparison between them. It is shown that the ANFIS architecture can model a nonlinear system very accurately by means of input-output pairs obtained either from the actual system or its mathematical model. It is also shown that such a system can be controlled effectively by a fuzzy controller.

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A PI-type fuzzy controller with self-tuning scaling factors

Cited by 66 publications

References 4 publications

An Auto-Tuning Method for the Scaling Factors of Fuzzy Logic Controllers with Application to SISO Mechanical System

An Auto-Tuning Method for the Scaling Factors of Fuzzy Logic Controllers with Application to SISO Mechanical System

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

Fuzzy control of an ANFIS model representing a nonlinear liquid-level system

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