Type-2 T–S fuzzy impulsive control of nonlinear systems

Li, Yimin; Sun, Yuanyuan

doi:10.1016/j.apm.2011.09.054

Cited by 17 publications

(3 citation statements)

References 15 publications

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“…At the same time experimental validation of the proposed controller is the future scope of this current work. The major challenges to implementing IT2FLS is computational time for converting type 2 to type 1 fuzzy sets in the processing unit [41]. But the same advanced algorithm is implemented for fast computation of type 2 to type 1 fuzzy set conversion presented in [42,43].…”

Section: Discussionmentioning

confidence: 99%

Stable Fault Tolerant Controller Design for Takagi–Sugeno Fuzzy Model-Based Control Systems via Linear Matrix Inequalities: Three Conical Tank Case Study

Patel

Shah

2019

Energies

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This paper deals with a methodical design approach of fault-tolerant controller that gives assurance for the the stabilization and acceptable control performance of the nonlinear systems which can be described by Takagi–Sugeno (T–S) fuzzy models. Takagi–Sugeno fuzzy model gives a unique edge that allows us to apply the traditional linear system theory for the investigation and blend of nonlinear systems by linear models in a different state space region. The overall fuzzy model of the nonlinear system is obtained by fuzzy combination of the all linear models. After that, based on this linear model, we employ parallel distributed compensation for designing linear controllers for each linear model. Also this paper reports of the T–S fuzzy system with less conservative stabilization condition which gives decent performance. However, the controller synthesis for nonlinear systems described by the T–S fuzzy model is a complicated task, which can be reduced to convex problems linking with linear matrix inequalities (LMIs). Further sufficient conservative stabilization conditions are represented by a set of LMIs for the Takagi–Sugeno fuzzy control systems, which can be solved by using MATLAB software. Two-rule T–S fuzzy model is used to describe the nonlinear system and this system demonstrated with proposed fault-tolerant control scheme. The proposed fault-tolerant controller implemented and validated on three interconnected conical tank system with two constraints in terms of faults, one issed to build the actuator and sond is system component (leak) respectively. The MATLAB Simulink platform with linear fuzzy models and an LMI Toolbox was used to solve the LMIs and determine the controller gains subject to the proposed design approach.

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Section: Discussionmentioning

confidence: 99%

Stable Fault Tolerant Controller Design for Takagi–Sugeno Fuzzy Model-Based Control Systems via Linear Matrix Inequalities: Three Conical Tank Case Study

Patel

Shah

2019

Energies

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“…As we know, the states of the dynamical systems are always bounded when they are periodic, chaotic or equilibrium. So, for the state vector of the systems (5), (7), and (8), they satisfy the following inequalities:…”

Section: Proof Consider the Following Lyapunov Function Candidate Asmentioning

confidence: 99%

“…Up to now, people pay more attention to such synchronization scheme that all states of the system are often assumed to be totally known. Common methods are active-passive control [2], adaptive control [3][4][5], impulsive control [6,7], sliding mode control [8][9][10], and so forth. In fact, only partial state information is available in many practical systems.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Integral Observer-Based Synchronization for Chaotic Systems with Unknown Parameters and Disturbances

2013

Journal of Applied Mathematics

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Considering the effects of external perturbations on the state vector and the output of the original system, this paper proposes a new adaptive integral observer method to deal with chaos synchronization between the drive and response systems with unknown parameters. The analysis and proof are given by means of the Lyapunov stability theorem and Barbalat lemma. This approach has fewer constraints because many parameters related to chaotic system can be unknown, as shown in the paper. Numerical simulations are performed in the end and the results show that the proposed method is not only suitable to the representative chaotic systems but also applied to some neural network chaotic systems.

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Practical implementation for stable adaptive interval A2-C0 type-2 TSK fuzzy controller

El-Nagar

2018

Soft Comput

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Type-2 T–S fuzzy impulsive control of nonlinear systems

Cited by 17 publications

References 15 publications

Stable Fault Tolerant Controller Design for Takagi–Sugeno Fuzzy Model-Based Control Systems via Linear Matrix Inequalities: Three Conical Tank Case Study

Stable Fault Tolerant Controller Design for Takagi–Sugeno Fuzzy Model-Based Control Systems via Linear Matrix Inequalities: Three Conical Tank Case Study

Adaptive Integral Observer-Based Synchronization for Chaotic Systems with Unknown Parameters and Disturbances

Practical implementation for stable adaptive interval A2-C0 type-2 TSK fuzzy controller

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