Radial Basis Function Controller of a Class of Nonlinear Systems Using Mamdani Type as a Fuzzy Estimator

Bahita, Mohamed; Belarbi, Khaled

doi:10.1016/j.proeng.2012.07.204

Cited by 8 publications

(9 citation statements)

References 16 publications

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“…The fact that u e is a bounded quantity is a key result in this work. In order to ensure boundedness of the weights, we replace the update law (14) by the so-called e-modification law [13]:…”

Section: The Adaptive Ts Fuzzy Controllermentioning

confidence: 99%

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An adaptive fuzzy control scheme with application in flexible spacecraft control

Bahita

Belarbi

2014

2014 Proceedings of the SICE Annual Conference (SICE)

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We introduce a direct fuzzy adaptive control for the large angle rotational movement and vibration suppression of a flexible spacecraft. A Takagi Sugeno inference system is used to approximate a feedback linearization control law. The adaptation mechanism is based on estimate of the error between the ideal unknown control signal and the actual control signal using a Mamdani fuzzy inference system. Simulation results show precise trajectory control and vibration suppression of the flexible spacecraft.

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Section: The Adaptive Ts Fuzzy Controllermentioning

confidence: 99%

“…For more detail, we can refer to our work [14]. We use triangular membership functions for the inputs and output as shown in figure …”

Section: Is Positive Then U ê Is Positivementioning

confidence: 99%

An adaptive fuzzy control scheme with application in flexible spacecraft control

Bahita

Belarbi

2014

2014 Proceedings of the SICE Annual Conference (SICE)

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“…17 In most control cases, the suggested adaptive fuzzy control techniques employ tracking error as the adaptation signal. A few authors [18][19][20] have proposed to employ the control error in the adaptation mechanism. As the control error is the difference between the known actual control signal (measured) and the ideal unknown control signal, our method will be based on some approximation reasoning.…”

Section: Introductionmentioning

confidence: 99%

Real-time application of a fuzzy adaptive control to one level in a three-tank system

Bahita

Belarbi

2018

Proceedings of the Institution of Mechanical Engineers, Part I:

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In this experimental work, a fuzzy combined control method has been described and applied to test the ability of the artificial intelligence technique in controlling nonlinear systems in real-time applications. A direct feedback linearization ideal control law is approximated with a Takagi-Sugeno fuzzy inference system. The adaptation law of the Takagi-Sugeno controller parameters is computed based on a fuzzy approximation term of the control error. This approximation is computed using a Mamdani fuzzy system. The experiment is applied on one level in a three-tank system and a test of the controller ability against perturbations is considered. The obtained results were compared to those leaded by a classical proportional-integral controller. The basic idea of this work is the use of the control error (between the actual control signal and the perfect control signal) instead of the tracking error (between the output of the one level in a three-tank system and the reference signal). As our approach is model free, that is, we do not use the mathematical model of the three-tank system, in the application part of this work, the output of the constructed Takagi-Sugeno fuzzy controller is injected to the real three-tank system.

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“…Conceptually, there are two distinct approaches to design the adaptive controllers: direct and indirect adaptive control methods. In the direct control method, a fuzzy logic system or neural networks are employed to simulate the action of the ideal controller and the parameters are directly adjusted to meet the control objective [15,19,[23][24][25][26][27][28][29][30][31]. In contrast, the indirect control method uses a fuzzy logic system or neural networks to approximate the unknown nonlinear terms of model dynamics and then synthesizes control laws based on these approximations [15,20,21,[32][33][34][35][36][37][38][39][40].…”

Section: Introductionmentioning

confidence: 99%

Improved Adaptive Sliding Mode Control for a Class of Uncertain Nonlinear Systems Subjected to Input Nonlinearity via Fuzzy Neural Networks

Nguyen

Liao

Yan

2015

Mathematical Problems in Engineering

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The paper presents an improved adaptive sliding mode control method based on fuzzy neural networks for a class of nonlinear systems subjected to input nonlinearity with unknown model dynamics. The control scheme consists of the modified adaptive and the compensation controllers. The modified adaptive controller online approximates the unknown model dynamics and input nonlinearity and then constructs the sliding mode control law, while the compensation controller takes into account the approximation errors and keeps the system robust. Based on Lyapunov stability theorem, the proposed method can guarantee the asymptotic convergence to zero of the tracking error and provide the robust stability for the closed-loop system. In addition, due to the modification in controller design, the singularity problem that usually appears in indirect adaptive control techniques based on fuzzy/neural approximations is completely eliminated. Finally, the simulation results performed on an inverted pendulum system demonstrate the advanced functions and feasibility of the proposed adaptive control approach.

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Radial Basis Function Controller of a Class of Nonlinear Systems Using Mamdani Type as a Fuzzy Estimator

Cited by 8 publications

References 16 publications

An adaptive fuzzy control scheme with application in flexible spacecraft control

An adaptive fuzzy control scheme with application in flexible spacecraft control

Real-time application of a fuzzy adaptive control to one level in a three-tank system

Improved Adaptive Sliding Mode Control for a Class of Uncertain Nonlinear Systems Subjected to Input Nonlinearity via Fuzzy Neural Networks

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