Displaying similar documents to “Improved fuzzy feedback linearization and Sinswat-transformation control of inverted pendulum”

Robust adaptive fuzzy filters output feedback control of strict-feedback nonlinear systems

Shaocheng Tong, Changliang Liu, Yongming Li (2010)

International Journal of Applied Mathematics and Computer Science

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In this paper, an adaptive fuzzy robust output feedback control approach is proposed for a class of single input single output (SISO) strict-feedback nonlinear systems without measurements of states. The nonlinear systems addressed in this paper are assumed to possess unstructured uncertainties, unmodeled dynamics and dynamic disturbances, where the unstructured uncertainties are not linearly parameterized, and no prior knowledge of their bounds is available. In recursive design, fuzzy...

Non-quadratic performance design for Takagi-Sugeno fuzzy systems

Miguel Bernal, Petr Hušek (2005)

International Journal of Applied Mathematics and Computer Science

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This paper improves controller synthesis of discrete Takagi-Sugeno fuzzy systems based on non-quadratic Lyapunov functions, making it possible to accomplish various kinds of control performance specifications such as decay rate conditions, requirements on control input and output and disturbance rejection. These extensions can be implemented via linear matrix inequalities, which are numerically solvable with commercially available software. The controller design is illustrated with an...

LMI-based adaptive fuzzy integral sliding mode control of mismatched uncertain systems

Chaouki Mnasri, Moncef Gasmi (2011)

International Journal of Applied Mathematics and Computer Science

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Integral sliding mode design is considered for a class of uncertain systems in the presence of mismatched uncertainties in both state and input matrices, as well as norm-bounded nonlinearities and external disturbances. A sufficient condition for the robust stability of the sliding manifold is derived by means of linear matrix inequalities. The initial existence of the sliding mode is guaranteed by the proposed control law. The improvement of the proposed control scheme performances,...

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).

Indirect adaptive controller based on a self-structuring fuzzy system for nonlinear modeling and control

Ruiyun Qi, Mietek A. Brdys (2009)

International Journal of Applied Mathematics and Computer Science

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In this paper, a unified nonlinear modeling and control scheme is presented. A self-structuring Takagi-Sugeno (T-S) fuzzy model is used to approximate the unknown nonlinear plant based on I/O data collected on-line. Both the structure and the parameters of the T-S fuzzy model are updated by an on-line clustering method and a recursive least squares estimation (RLSE) algorithm. The rules of the fuzzy model can be added, replaced or deleted on-line to allow a more flexible and compact...