Controller Design for Continuous-Time Takagi-Sugeno Fuzzy Systems with Fuzzy Lyapunov Functions : LMI Approach

Kim, Ho Jun; Joo, Young Hoon; Park, Jin Bae

doi:10.5391/ijfis.2012.12.3.187

Cited by 4 publications

(3 citation statements)

References 11 publications

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“…The BS is a robust control technique that is highly nonlinear and based on the Lyapunov stability theorem. Stability is achieved through the recursive process, as Lyapunov is a scalar function that ensures the stability of the system [ 28 ]. This technique can only be implemented by using strict feedback systems.…”

Section: Control Techniquesmentioning

confidence: 99%

Control strategies for inverted pendulum: A comparative analysis of linear, nonlinear, and artificial intelligence approaches

Irfan,

Zhao,

Ullah

et al. 2024

PLoS ONE

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An inverted pendulum is a challenging underactuated system characterized by nonlinear behavior. Defining an effective control strategy for such a system is challenging. This paper presents an overview of the IP control system augmented by a comparative analysis of multiple control strategies. Linear techniques such as linear quadratic regulators (LQR) and progressing to nonlinear methods such as Sliding Mode Control (SMC) and back-stepping (BS), as well as artificial intelligence (AI) methods such as Fuzzy Logic Controllers (FLC) and SMC based Neural Networks (SMCNN). These strategies are studied and analyzed based on multiple parameters. Nonlinear techniques and AI-based approaches play key roles in mitigating IP nonlinearity and stabilizing its unbalanced form. The aforementioned algorithms are simulated and compared by conducting a comprehensive literature study. The results demonstrate that the SMCNN controller outperforms the LQR, SMC, FLC, and BS in terms of settling time, overshoot, and steady-state error. Furthermore, SMCNN exhibit superior performance for IP systems, albeit with a complexity trade-off compared to other techniques. This comparative analysis sheds light on the complexity involved in controlling the IP while also providing insights into the optimal performance achieved by the SMCNN controller and the potential of neural network for inverted pendulum stabilization.

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Section: Control Techniquesmentioning

confidence: 99%

Control strategies for inverted pendulum: A comparative analysis of linear, nonlinear, and artificial intelligence approaches

Irfan,

Zhao,

Ullah

et al. 2024

PLoS ONE

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“…In fact, it is proven that T-S fuzzy models are universal approximators and used for various application in [11][12][13][14][15].…”

Section: T-s Fuzzy Duhem Modelmentioning

confidence: 99%

Modeling of Dynamic Hysteresis Based on Takagi-Sugeno Fuzzy Duhem Model

Lee¹,

Park²,

Baek³

2013

International Journal of Fuzzy Logic and Intelligent Systems

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In this study, we propose a novel method for modeling dynamic hysteresis. Hysteresis is a widespread phenomenon that is observed in many physical systems. Many different models have been developed for representing a hysteretic system. Among them, the Duhem model is a classical nonlinear dynamic hysteresis model satisfying the properties of hysteresis. The purpose of this work is to develop a novel method that expresses the local dynamics of the Duhem model by a linear system model. Our approach utilizes a certain type of fuzzy system that is based on Takagi-Sugeno (T-S) fuzzy models. The proposed T-S fuzzy Duhem model is achieved by fuzzy blending of the linear system model. A simulated example applied to shape memory alloy actuators, which have typical hysteretic properties, illustrates the applicability of our proposed scheme.

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“…Nonlinear systems can be represented as the average weighted sum of linear systems using TS fuzzy systems [21]. TS models are widely used for the analysis and controller synthesis of nonlinear systems through the direct Lyapunov method [14].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Observer Design for State and Fault Estimation for Takagi-Sugeno Implicit Models

Ouzaz

Assoudi

Yaagoubi

2023

IJFIS

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In this study, we develop a fuzzy observer for a class of discrete-time nonlinear implicit models that are described by the Takagi-Sugeno structure and affected by actuator and sensor faults with unmeasurable premise variables satisfying the Lipschitz constraints. This study is based on separating dynamic and static equations in discrete-time Takagi-Sugeno implicit models. The design of a fuzzy observer is proposed to estimate unknown states, actuators, and sensor faults simultaneously. It is designed by considering the fault variables constituted by the actuator and sensor faults as auxiliary state variables. The observer gain is calculated by studying the exponential convergence of the state estimation error using the Lyapunov theory and the stability condition given as a linear matrix inequality. Simulation results demonstrated the effectiveness and validity of the proposed method.

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Controller Design for Continuous-Time Takagi-Sugeno Fuzzy Systems with Fuzzy Lyapunov Functions : LMI Approach

Cited by 4 publications

References 11 publications

Control strategies for inverted pendulum: A comparative analysis of linear, nonlinear, and artificial intelligence approaches

Control strategies for inverted pendulum: A comparative analysis of linear, nonlinear, and artificial intelligence approaches

Modeling of Dynamic Hysteresis Based on Takagi-Sugeno Fuzzy Duhem Model

Fuzzy Observer Design for State and Fault Estimation for Takagi-Sugeno Implicit Models

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