Robust model reference tracking control for interval type‐2 fuzzy stochastic systems

Kavikumar, Ramasamy; Sakthivel, R.; Kwon, Oh‐Min; Kaviarasan, B.

doi:10.1049/iet-cta.2019.0781

Cited by 15 publications

(7 citation statements)

References 47 publications

(38 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Remark Compared with the results in some existing literature, 22,32,34,62 in this article, the fixed‐time stabilization and the finite‐time stabilization of quaternion‐valued neural networks are studied by adding an adaptive feedback controller which is different from robust control, in which it does not need a priori information about the bounds on these uncertain or time‐varying parameters; robust control guarantees that if the changes are within given bounds, the control law need not be changed, while adaptive control is concerned with control law changing itself.…”

Section: Resultsmentioning

confidence: 99%

“…▪ Remark 11. It may be noted that the settling timeT 1 proposed in (22), respectively, depends on the initial states V(0…”

Section: Theorem 2 Under Assumptions 1 and 2 System (3) Is Fixed-timentioning

confidence: 99%

“…So it is important to analyze his dynamics behaviors. 8,[17][18][19][20][21][22][23] Rather than Lyapunov's classic asymptotic stability 6,[24][25][26][27] and exponential stability, 28 finite-time stability means that the system's solution trajectories converge the equilibrium point after a finite-time, and the finite-time is called the settling time or time convergence. [29][30][31][32] The finite-time stability is involved in many control problems, such as secure communication, 33 finite-time output feedback stabilization of the double integrator, 34 and the finite-time attitude tracking problem for a single spacecraft and multiple spacecraft.…”

Section: Introductionmentioning

confidence: 99%

“…The applications heavily depend on the dynamical behaviors of FNNs. So it is important to analyze his dynamics behaviors 8,17‐23 …”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

New feedback control techniques of quaternion fuzzy neural networks with time‐varying delay

Aouiti

Jallouli

2021

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

This article addresses the problems of fixed-time stabilization for a class of quaternion fuzzy neural networks (QFNNs) with time-varying delay. The QFNNs are developed by dividing our system into four real-valued parts based on the Hamilton rule. Then, based on fixed-time stability theory, some inequality techniques, and selecting the appropriate controllers and Lyapunov function, a novel criterion guaranteeing the fixed-time stabilization and the finite-time stabilization of the addressed system is derived. Finally, three numerical examples are presented to show the effectiveness of our theoretical results. K E Y W O R D S finite-time stabilization, fixed-time stabilization, fuzzy neural networks, quaternion neural networks, time-varying delay

show abstract

Section: Resultsmentioning

confidence: 99%

“…▪ Remark 11. It may be noted that the settling timeT 1 proposed in (22), respectively, depends on the initial states V(0…”

Section: Theorem 2 Under Assumptions 1 and 2 System (3) Is Fixed-timentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…The applications heavily depend on the dynamical behaviors of FNNs. So it is important to analyze his dynamics behaviors 8,17‐23 …”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

New feedback control techniques of quaternion fuzzy neural networks with time‐varying delay

Aouiti

Jallouli

2021

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

show abstract

“…The high-complexity computation of T2FLSs can be reduced by the interval type-2 fuzzy-logic systems (IT2FLSs), proposed by Liang and Mendel in 2010 [14]. Since this first work, the development of IT2FLSs has been significantly extended and widely applied to various research fields [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Optimum Design of Function-Link Type-2 Fuzzy Asymmetric CMAC Based on Self-Organizing Algorithm and Modified Jaya Algorithm

Quynh

Long

et al. 2020

IEEE Access

View full text Add to dashboard Cite

In this work, a novel self-organizing interval type-2 fuzzy cerebellar model articulation controller design for chaotic systems synchronization is presented. A function-link network and an asymmetric-membership function are employed to improve the learning ability and flexibility of the proposed network. The gradient-descent method is used to derive the adaptive laws for online updating the network parameters. Suitable learning rates for adaptive laws can be achieved by employing the modified Jaya algorithm. Furthermore, the proposed self-organizing algorithm is used to generate new fuzzy rules or delete less-active fuzzy rules. The control system's exponential stability is analyzed by applying the Lyapunov theory. Finally, numerical simulations on the synchronization of four-dimensional chaotic systems (considering external disturbances and system uncertainties) are conducted to validate the efficiency and performance of the proposed method. INDEX TERMS self-organizing algorithm, type-2 fuzzy CMAC, Jaya algorithm, function-link network, asymmetric membership function.

show abstract

Mixed H∞ and passivity guaranteed cost proportional‐integral control for interval‐valued type‐2 fuzzy systems with input delay

Sakthivel

Sri

2022

Optim Control Appl Methods

View full text Add to dashboard Cite

The paper focuses on the problem of mixed H ∞ and passivity guaranteed cost control of an interval-valued type-2 (IT-2) fuzzy system with time-varying input delay and exogenous disturbance. For this, an IT-2 fuzzy-model-based system with a mismatched membership grade function is formulated. The main objective is to design a proportional-integral controller that stimulates our system to attain its equilibrium state even with the occupancy of exogenous disturbance and input delay in the system. In addition, a quadratic cost function is constructed such that the cost function of the closed-loop IT-2 fuzzy system is lower than the upper-bound cost index. Mixed H ∞ and passivity performance indices are then calculated to address the exogenous disturbance in the system. A set of new criteria ensuring the asymptotic stability of the closed-loop IT-2 fuzzy system with input delay are derived in terms of linear matrix inequalities with the aid of Lyapunov stability theory and the employment of a Wirtinger-based inequality. Finally, two example simulations are conducted to validate our theoretical results.

show abstract

Robust model reference tracking control for interval type‐2 fuzzy stochastic systems

Cited by 15 publications

References 47 publications

New feedback control techniques of quaternion fuzzy neural networks with time‐varying delay

New feedback control techniques of quaternion fuzzy neural networks with time‐varying delay

Optimum Design of Function-Link Type-2 Fuzzy Asymmetric CMAC Based on Self-Organizing Algorithm and Modified Jaya Algorithm

Mixed H∞ and passivity guaranteed cost proportional‐integral control for interval‐valued type‐2 fuzzy systems with input delay

Contact Info

Product

Resources

About