A novel adaptive interval type-3 neuro-fuzzy robust controller for nonlinear complex dynamical systems with inherent uncertainties

Taghieh, Amin; Mohammadzadeh, Ardashir; Zhang, Chunwei; Sakthivel, R.; Bekiros, Stelios

doi:10.1007/s11071-022-07867-9

Cited by 18 publications

(5 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where η is the training rate and g : R n → R is a cost function, g ∈ C 2 . Assume that all f and f ∈ D ⊂ R n for some compact D. Then, to train the parameters M i q ∈ M i q 1 , M i q 2 and σ i q , to minimize the squared errors given by (42), and to test for convergence, it is necessary to apply Lemma 1 [69] to each of the lower and upper firing interval functions (23). Both are piecewise differentiable, i.e., each branch is differentiable [60] over its segment domain, and using the Taylor series expansion, it is possible to prove the next points:…”

Section: Convergence Analisysmentioning

confidence: 99%

“…In recent years, 2019-2022, important and novel papers were developed [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28], [29], [30], [31], [32], [33], [34], [35],…”

Section: Introductionmentioning

confidence: 99%

“…The analysis of the state-of-the-art literature shows that IT3 models use singleton inputs [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28], [29], [30], [31], [33], [34], [35], [36], [37], [38], [39], [40], [41], [42] and type-1, non-singleton inputs [32]. The applications of the IT3 FLS systems mainly covers the areas of: Mathematical theory [1], [2], [3], [4], [6], [11], [19], [24], [56], [57],…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A New Method for the Design of Interval Type-3 Fuzzy Logic Systems With Uncertain Type-2 Non-Singleton Inputs (IT3 NSFLS-2): A Case Study in a Hot Strip Mill

Mendez¹,

López-Juárez

Dorantes³

et al. 2023

IEEE Access

View full text Add to dashboard Cite

This paper presents a new method for the construction and training of interval type-3 fuzzy logic systems whose inputs are uncertain type-2 non-singleton numbers (IT3 NSFLS-2). The proposed methodology is divided in two processes: 1) The novel construction of the structure of the IT3 NSFLS-2 systems based on: a) The level-alpha-0 of the interval type-2 fuzzy logic system (IT2-alpha-0 FLS), and on b) The secondary membership function using Gaussian modeling to construct each rule of the alpha-k fuzzy rule base (FRB), the firing intervals of the antecedent and the centroids of the consequent, and 2) The training methodology based on gradient descent algorithm to train the antecedent and consequent parameters of the alpha-0 FRB. The primary membership functions (MF) of the antecedents of the IT3 NSFLS-2 system are modeled as Gaussians with uncertain means and with common standard deviation. The proposal was applied and tested with the prediction of a transfer bar's surface temperature in an industrial hot strip mill facility located in Monterrey México. The modeling results show that the proposal supports the stability required by this critical process and shows the best performance when compared with similar methods.INDEX TERMS General type-2 fuzzy systems, gradient descent algorithm, hot strip mill, interval type-2 fuzzy systems, interval type-3 fuzzy systems, temperature prediction, type-2 non-singleton inputs.

show abstract

Section: Convergence Analisysmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A New Method for the Design of Interval Type-3 Fuzzy Logic Systems With Uncertain Type-2 Non-Singleton Inputs (IT3 NSFLS-2): A Case Study in a Hot Strip Mill

Mendez¹,

López-Juárez

Dorantes³

et al. 2023

IEEE Access

View full text Add to dashboard Cite

show abstract

“…In recent decades, there has been significant attention given to the research on control methods for memristor chaotic systems. Notable control methods for memristor chaotic systems encompass traditional PID control [15,16], adaptive control [17,18], sliding mode control [19,20], impulse control [21,22], as well as emerging techniques like event-triggered control [23,24] and fuzzy control [25,26].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Sliding Mode Filtering Control of Memristor-Based Bao Hyper-chaotic System

Zhu,

Feng,

et al. 2023

Preprint

View full text Add to dashboard Cite

Utilizing the three-dimensional Bao system as a basis, we propose a hyper-chaotic circuit system and develop a dimensionless mathematical model by incorporating the smooth cubic nonlinear flux control arrester model as the feedback component. The dynamic characteristics, equilibrium set, and stability of the system are investigated through the utilization of Lyapunov exponential spectrum and bifurcation diagrams. Subsequently, we design the hardware circuit and simulate its analog equivalent circuit, demonstrating consistent results with the numerical simulation. Additionally, we construct an adaptive sliding mode filter controller for ensuring asymptotic stability of the system under external interference and unknown parameters conditions. By integrating the Lyapunov stability principle, we effectively control the Bao hyper-chaotic system. In the final section of this paper, the results of numerical simulation and analysis demonstrate the effectiveness of the designed controller, thus establishing a solid foundation for the practical application of memristor chaotic circuits.

show abstract

“…The SMC would be inherently robust with regard to the uncertain and unknown terms. The SMC technique may be combined with fuzzy neural network in some control applications (Taghieh et al, 2022). So the self-constructing fuzzy neural fractional-order SMC (Fei et al, 2022c) and self-evolving Chebyshev fuzzy neural SMC (Fei et al, 2022a) are studied in an active power filter.…”

Section: Introductionmentioning

confidence: 99%

Robust composite nonlinear feedback control based on integral sliding-mode control of uncertain time-delay systems under input saturation

Hu¹,

Ghaffari

Mobayen

et al. 2022

Journal of Vibration and Control

View full text Add to dashboard Cite

In this research, based on the integral sliding-mode scheme, a robust composite nonlinear feedback (CNF) control is addressed for time-delayed uncertain systems. Such an uncertain system may contain some complexities like time-varying delays, actuators saturation, and exterior disturbances. Hence, the tracking problem would be solved under the mentioned constraints. Thus, for achieving these goals, the tracking problem is firstly formulated by considering system’s limitations. The controller parameters may be determined via a solution for linear matrix inequality. As a result, the CNF tracker law is numerically simulated in some control cases. The simulation outcomes are compared with a similar technique in terms of the transient response, boundedness of the error, and the disturbance rejections. Thus, the findings illustrate the efficiency of the advised CNF control method comparing to the recent technique.

show abstract

A novel adaptive interval type-3 neuro-fuzzy robust controller for nonlinear complex dynamical systems with inherent uncertainties

Cited by 18 publications

References 43 publications

A New Method for the Design of Interval Type-3 Fuzzy Logic Systems With Uncertain Type-2 Non-Singleton Inputs (IT3 NSFLS-2): A Case Study in a Hot Strip Mill

A New Method for the Design of Interval Type-3 Fuzzy Logic Systems With Uncertain Type-2 Non-Singleton Inputs (IT3 NSFLS-2): A Case Study in a Hot Strip Mill

Adaptive Sliding Mode Filtering Control of Memristor-Based Bao Hyper-chaotic System

Robust composite nonlinear feedback control based on integral sliding-mode control of uncertain time-delay systems under input saturation

Contact Info

Product

Resources

About