Adaptive Fuzzy Predictive Controller for a Class of Networked Nonlinear Systems With Time-Varying Delay

Hamdy, Mohamed; Abd‐Elhaleem, Sameh; Fkirin, M. A.

doi:10.1109/tfuzz.2017.2764851

Cited by 83 publications

(25 citation statements)

References 35 publications

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“…The dynamics of the state estimation error (11) for the identifier (14) are defined as (22). Equation 22is rewritten as (23) using (14) and (19), and adding and substracting * ⊤…”

Section: Rhonn Identifiermentioning

confidence: 99%

Real‐time neural observer‐based controller for unknown nonlinear discrete delayed systems

Rios

Alanis

Arana‐Daniel

et al. 2020

Intl J Robust & Nonlinear

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This work presents a neural observer-based controller for uncertain nonlinear discrete-time systems with unknown time-delays. The proposed neural observer does not need previous knowledge of the model about the system under consideration, neither the value of its parameters, delays, nor their explicit estimations. The proposed neural observer is based on a neural network composed of two recurrent high order neural networks (RHONNs) for nonmeasurable state variables, one in a parallel configuration, and for measurable state variables one in a series-parallel configuration. The neural network is trained on-line with an extended Kalman filter algorithm. The proposed RHONN observer provides a mathematical model for the system. Based on such a resulting mathematical model, a control law is designed using discrete-time sliding mode block control. Applicability is presented using real-time results that show the performance of the proposal using a linear induction motor prototype as the selected system; this prototype is under the presence of varying time-delays. A Lyapunov analysis is included to prove the semi-globally uniformly ultimately boundedness (SGUUB) of the proposed RHONN observer-controller scheme for uncertain nonlinear discrete-time systems with unknown delays. K E Y W O R D S discrete-time sliding modes, Lyapunov stability, neural block control, neural state estimation, real-time, time-delay systems 8402

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“…The dynamics of the state estimation error (11) for the identifier (14) are defined as (22). Equation 22is rewritten as (23) using (14) and (19), and adding and substracting * ⊤…”

Section: Rhonn Identifiermentioning

confidence: 99%

Real‐time neural observer‐based controller for unknown nonlinear discrete delayed systems

Rios

Alanis

Arana‐Daniel

et al. 2020

Intl J Robust & Nonlinear

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show abstract

“…As we know, when partial faults happen to the actuator but not a complete failure in practical engineering, the directions and amplitudes of control input signals are both unknown. In order to simulate the above conditions, uncertain constants θ 1 and θ 2 are introduced to the system in (5) and (6), and the signs of θ 1 and θ 2 , so-called control directions, are uncertain.…”

Section: Problem Formulationmentioning

confidence: 99%

“…Vibration is a major and widespread problem which exists extensively in many systems. Motivated by prolonging the life of the system, problems of vibration and stabilisation have been investigated by many researchers in recent decades and lots of control methods have been developed [1][2][3][4][5][6]. Since infinitedimensional distributed-parameter flexible systems, such as flexible manipulators, flexible cables, and flexible marine risers, have weak stiffness and are easily deformable, past decades have seen an enormous amount of research efforts going into partial differential equation (PDE) modelling and corresponding vibration control methods [7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

PDE modelling and vibration control of overhead crane bridge with unknown control directions and parametric uncertainties

Liu

2020

IET Control Theory & Applications

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“…16 Recently, some effective robust adaptive control methods have been proposed for uncertain nonlinear systems. [17][18][19][20][21] Control design of nonlinear systems with unknown backlash-like hysteresis (such as the one present in PEAs) has attracted a lot of interest [22][23][24][25][26][27] among researchers. Thanks to the universal approximation properties of fuzzy logic, fuzzy logic control techniques have been widely used for controlling unknown nonlinear systems with backlash-like hysteresis.…”

Section: Introductionmentioning

confidence: 99%

Enhanced adaptive control for a benchmark piezoelectric‐actuated system via fuzzy approximation

Yousef

Hamdy

Saleem

et al. 2019

Adaptive Control & Signal

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This paper investigates the problem of the high precision tracking control of piezoelectric actuators (PEAs) without using the inverse of the uncertain hysteresis. Based on fuzzy system approximator and particle swarm optimization (PSO) algorithm, a proposed enhanced  adaptive controller is developed. The proposed controller provides fast and robust adaptation simultaneously with guaranteed desired transient performance. Moreover, it has a simple form and requires fewer adaptation parameters. The adaptation gain is determined via PSO algorithm. The proposed controller is tested on a lab-scale PEA system.Experimental results with comparative studies with different techniques have been developed. The simulation results reveal that the proposed controller outperforms the other controllers in terms of normalized root-mean-square and maximum tracking errors for different frequencies.

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Adaptive Fuzzy Predictive Controller for a Class of Networked Nonlinear Systems With Time-Varying Delay

Cited by 83 publications

References 35 publications

Real‐time neural observer‐based controller for unknown nonlinear discrete delayed systems

Real‐time neural observer‐based controller for unknown nonlinear discrete delayed systems

PDE modelling and vibration control of overhead crane bridge with unknown control directions and parametric uncertainties

Enhanced adaptive control for a benchmark piezoelectric‐actuated system via fuzzy approximation

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