Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Multi-input and Multi-variable systems

Multi-input and Multi-variable systems

Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...

Multicompartment Models: Overview

Multicompartment Models: Overview

Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...

Normal and Tangetial Components: Problem Solving

Normal and Tangetial Components: Problem Solving

Consider a man with a mass of 70 kg seated in a chair connected to a pin support through a member BC. If the man maintains an upright position, the task is to determine the horizontal and vertical reactions of the chair on the man when the member makes a 45° angle with the horizontal. At this moment, the man has a speed of 5 m/s, increasing at a rate of 1 m/s².

Modeling with Differential Equations

Modeling with Differential Equations

Population dynamics can be described mathematically by considering the population size P(t) as a function of time. The rate of change of the population is then represented by the derivative of P(t). A simple assumption is that the rate of growth is proportional to the size of the population itself. This leads to an exponential growth model, where the population increases rapidly without bound. While this is a useful first approximation, it does not reflect realistic long-term...

Functional Classification of Joints

Functional Classification of Joints

Functional Classification of Joints
The functional classification of joints is determined by the amount of mobility between the adjacent bones. Joints are functionally classified as a synarthrosis or immobile joint, an amphiarthrosis or slightly moveable joint, or as a diarthrosis, a freely moveable joint. Fibrous and cartilaginous joints can be functionally classified as either synarthroses or amphiarthroses, whereas all synovial joints are classified as diarthroses.
Synarthrosis
An immobile...

Classification of Systems-II

Classification of Systems-II

Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Similarity measures in fuzzy rule base simplification.

IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society·2008

Same author

Fuzzy relational classifier trained by fuzzy clustering.

IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society·2008

Same author

Modified Gath-Geva fuzzy clustering for identification of Takagi-Sugeno fuzzy models.

IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society·2008

Same author

Model-based operational constraints for fluidised bed crystallisation.

Water research·2007

Same author

Optimal flow distribution over multiple parallel pellet reactors: a model-based approach.

Water science and technology : a journal of the International Association on Water Pollution Research·2006

Same author

Estimation of respiratory parameters via fuzzy clustering.

Artificial intelligence in medicine·2001

Search research articles

Related Experiment Videos

Fuzzy modeling with multivariate membership functions: gray-box identification and control design.

J Abonyi¹, R Babuska, F Szeifert

¹Dept. of Process Eng., Univ. of Veszprem.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society

|February 5, 2008

Summary

This study presents a new fuzzy modeling framework using Takagi-Sugeno models and Delaunay triangulation for enhanced control system design. The approach optimizes parameters with prior knowledge, improving process modeling and control strategies.

Related Experiment Videos

Area of Science:

Engineering
Control Systems
Fuzzy Logic

Background:

Fuzzy modeling and model-based control are crucial for complex systems.
Takagi-Sugeno (TS) models offer a structured approach to fuzzy modeling.
Efficient parameter estimation and control design are key challenges.

Purpose of the Study:

To introduce a novel framework for fuzzy modeling and model-based control design.
To develop methods for estimating TS fuzzy model parameters using constrained optimization.
To create techniques for control design via model linearization and inversion.

Main Methods:

Utilized Takagi-Sugeno (TS) type fuzzy models with constant consequents.
Employed multivariate antecedent membership functions derived from Delaunay triangulation.
Implemented an iterative insertion algorithm for characteristic point determination.
Applied constrained optimization for parameter estimation, incorporating a priori process knowledge.
Developed control design strategies through model linearization and inversion.

Main Results:

Successfully identified the Box-Jenkins gas furnace model using the proposed framework.
Demonstrated inverse model-based control for a pH process.
Achieved comparable or improved results against existing literature benchmarks.
Validated the effectiveness of the novel fuzzy modeling and control design techniques.

Conclusions:

The proposed framework provides an effective approach for fuzzy modeling and model-based control design.
The integration of Delaunay triangulation and constrained optimization enhances model accuracy and control performance.
The demonstrated applications highlight the versatility and efficacy of the developed methods.