A novel adaptive multi-critic based separated-states neuro-fuzzy controller: Architecture and application to chaos control

Eqra, Navid; Vatankhah, Ramin; Eghtesad, Mohammad

doi:10.1016/j.isatra.2020.11.020

Cited by 3 publications

(1 citation statement)

References 23 publications

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“…e backstepping SMC is suggested in [22] for stabilizing CSs in the presence of time delay, and FLSs are used to estimate some nonlinearities. In [23], based on reinforcement learning and FLSs, a synchronization scheme is developed. e performance of FLS-based synchronization methods is evaluated and analyzed in [24].…”

Section: Introductionmentioning

confidence: 99%

A Type‐3 Fuzzy Approach for Stabilization and Synchronization of Chaotic Systems: Applicable for Financial and Physical Chaotic Systems

et al. 2022

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In this paper, a new approach is presented for stabilizing and synchronizing financial chaotic systems. A new type-3 (T3) fuzzy-based system (FLS) with an online optimization scheme is designed to cope with chaotic behavior, high-level uncertainties, and unknown dynamics. An adaptive compensator also eliminates the effect of approximation errors (AEs) and perturbations. The stability of the dynamics of synchronization errors is guaranteed by the use of the Lyapunov method. Several simulations and comparisons demonstrate the superiority of the suggested control and synchronization scenarios.

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Section: Introductionmentioning

confidence: 99%

A Type‐3 Fuzzy Approach for Stabilization and Synchronization of Chaotic Systems: Applicable for Financial and Physical Chaotic Systems

et al. 2022

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Proposal of a Compact Neuro-Fuzzy Adaptive Controller for Filling Regulation of Two Coupled Spherical Tanks

Espitia,

Machón,

López

2024

Int. J. Fuzzy Syst.

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This paper displays the set up and simulation of a compact neuro-fuzzy adaptive scheme for the filling regulation of two coupled spherical tanks. The suggested scheme employs two compact neuro-fuzzy blocks: the first one to model the plant, and the second one for the controller implementation. In this scheme, the controller is trained employing the fuzzy model estimated with data of the system working in closed-loop. Thus, the controller optimization iteratively is performed when plant variations occur. The work also includes the deduction of the equations for training, showing the adaptive process employing neuro-fuzzy systems. Moreover, the training (optimization) process of the controller’s neuro-fuzzy system includes within the adjustment function the control action and the error signal. Various experimental cases are considered using statistical analysis to verify behaviors in the adaptive control system. In this order, the main contribution of this work consists of the adjustment (coupling) of two structures of compact neuro-fuzzy systems used for identification and control, as well as the deduction and adjustment of the training algorithms to implement the adaptive control system.

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BOBShield: An Open-Source Miniature “Ball and Beam” Device for Control Engineering Education

Takács

Mikuláš

Vargová

et al. 2021

2021 IEEE Global Engineering Education Conference (EDUCON)

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A novel adaptive multi-critic based separated-states neuro-fuzzy controller: Architecture and application to chaos control

Cited by 3 publications

References 23 publications

A Type‐3 Fuzzy Approach for Stabilization and Synchronization of Chaotic Systems: Applicable for Financial and Physical Chaotic Systems

A Type‐3 Fuzzy Approach for Stabilization and Synchronization of Chaotic Systems: Applicable for Financial and Physical Chaotic Systems

Proposal of a Compact Neuro-Fuzzy Adaptive Controller for Filling Regulation of Two Coupled Spherical Tanks

BOBShield: An Open-Source Miniature “Ball and Beam” Device for Control Engineering Education

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