Displaying similar documents to “Fuzzy and neural control of an induction motor”

Fuzzy feedback linearizing controller and its equivalence with the fuzzy nonlinear internal model control structure

Reda Boukezzoula, Sylvie Galichet, Laurent Foulloy (2007)

International Journal of Applied Mathematics and Computer Science

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This paper examines the inverse control problem of nonlinear systems with stable dynamics using a fuzzy modeling approach. Indeed, based on the ability of fuzzy systems to approximate any nonlinear mapping, the nonlinear system is represented by a Takagi-Sugeno (TS) fuzzy system, which is then inverted for designing a fuzzy controller. As an application of the proposed inverse control methodology, two popular control structures, namely, feedback linearization and Nonlinear Internal Model...

Cooperative-corrector multivariable fuzzy controller.

M.ª Cristina Rivero, P. Lasso, Félix Monasterio-Huelin (1998)

Mathware and Soft Computing

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This paper deals with the decomposition problem of a multivariable fuzzy controller. For this purpose, the use of notions taken from the framework of the Game Theory is proposed. Using the notion of couple between variables, a partition of the rule space in subsystems is obtained. The subsystems are considered players that correct the actions of the others. These ideas are applied to the control of a polymerization reactor (CSTR).

Soft computing in modelbased predictive control footnotemark

Piotr Tatjewski, Maciej Ławrynczuk (2006)

International Journal of Applied Mathematics and Computer Science

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The application of fuzzy reasoning techniques and neural network structures to model-based predictive control (MPC) is studied. First, basic structures of MPC algorithms are reviewed. Then, applications of fuzzy systems of the Takagi-Sugeno type in explicit and numerical nonlinear MPC algorithms are presented. Next, many techniques using neural network modeling to improve structural or computational properties of MPC algorithms are presented and discussed, from a neural network model...

Neural network based feedback linearization control of a servo-hydraulic vehicle suspension system

Jimoh Olarewaju Pedro, Olurotimi Akintunde Dahunsi (2011)

International Journal of Applied Mathematics and Computer Science

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This paper presents the design of a neural network based feedback linearization (NNFBL) controller for a two degree-offreedom (DOF), quarter-car, servo-hydraulic vehicle suspension system. The main objective of the direct adaptive NNFBL controller is to improve the system's ride comfort and handling quality. A feedforward, multi-layer perceptron (MLP) neural network (NN) model that is well suited for control by discrete input-output linearization (NNIOL) is developed using input-output...

Fuzzy logic gain scheduling for non-linear servo tracking

Mieczysław Brdyś, Jonathan Littler (2002)

International Journal of Applied Mathematics and Computer Science

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This paper proposes the use of gain scheduling as a method of controlling a servo system with hard non-linear elements. The servo controls two elements of a tracker mounted on a ship at sea. There is stiction at the zero velocity point and non-linear friction against the motion of each tracker axis. A dual feedback loop control structure is employed. Fuzzy logic is used to provide smoothly varying non-linear scheduling functions to map the velocity of the servo relevant to the deck of...