Adaptive Neural Network Finite-Time Control of Uncertain Fractional-Order Systems with Unknown Dead-Zone Fault via Command Filter

Deng, Xiongfeng; Wei, Lisheng

doi:10.3390/fractalfract6090494

Cited by 4 publications

(5 citation statements)

References 48 publications

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“…The presented design in ( 5)-( 13) depends on neither the prior knowledge of the powers and the system nonlinearities nor their specific bounding functions or bounds. Even so, no attempt is made for parameter identification [4][5][6][7][8][9][10][11][12][13][14], function approximation [15][16][17][18][19][20]44,45], gain adaptation [46,47], or adding a power integrator technique [11,13,[22][23][24][25][26][27][28]. Furthermore, the reference derivative and the intermediate control signal derivatives are not involved in the control law.…”

Section: Control Designmentioning

confidence: 99%

“…Furthermore, the reference derivative and the intermediate control signal derivatives are not involved in the control law. Nevertheless, this is accomplished without dynamic surface control [44] or auxiliary filters [45]. Thus, the controller exhibits fewer demands and simplicity.…”

Section: Control Designmentioning

confidence: 99%

“…It exhibits simplicity, without function approximation [15][16][17][18][19][20]44,45], parameter identification [4][5][6][7][8][9][10][11][12][13][14], command filtering [44,45], or adding a power integrator technique [11,13,[22][23][24][25][26][27][28].…”

mentioning

confidence: 99%

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Prescribed Performance Tracking Control of Lower-Triangular Systems with Unknown Fractional Powers

Xu,

Zhang

2023

Fractal Fract

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This paper is concerned with the tracking control problem for the lower-triangular systems with unknown fractional powers and nonparametric uncertainties. A prescribed performance control approach is put forward as a means of resolving this problem. The proposed control law incorporates a set of barrier functions to guarantee error constraints. Unlike the previous works, our approach works for the cases where the fractional powers, the nonlinearities, and their bounding functions or bounds are totally unknown; no restrictive conditions on the powers, such as power order restriction, specific size limitation or homogeneous condition, are made. Moreover, neither the powers and system nonlinearities nor their bounding functions or bounds are needed. It achieves reference tracking with the preassigned tracking accuracy and convergence speed. In addition, our controller is simple, as it does not necessitate parameter identification, function approximation, derivative calculation, or adding a power integrator technique. At the end, a comparative simulation demonstrates the effectiveness and advantage of the proposed approach.

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Section: Control Designmentioning

confidence: 99%

Section: Control Designmentioning

confidence: 99%

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Prescribed Performance Tracking Control of Lower-Triangular Systems with Unknown Fractional Powers

Xu,

Zhang

2023

Fractal Fract

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“…Lemma 5 [26] If for a fractional order chaotic system D w t X (t) = F, there exist positive definite and continuous functions V (X ), and positive constants p 1 ,p 2 , and gain functions o 1 ,o 2 ,then the following relation is satisfied:…”

Section: Introduction Conceptmentioning

confidence: 99%

Finite-time Neural Network Adaptive Sliding Mode Control of Fractional-Order Chaotic Systems With Disturbance Observer And Fractional Robust Differentiator

Cui,

Zhao

2024

Preprint

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This paper proposes a neural network adaptive sliding mode control scheme with a fractional-order disturbance observer and a robust differentiator for fractional-order chaotic systems with uncertain functions and random noise. The solution combines fractional-order differential controllers, gradient descent neural networks, and fractional-order disturbance observer techniques. A gradient descent neural network handles uncertain nonlinear functions, while fractional-order disturbance observers are presented to solve unexpected noise problems. On the premise that the above problems are solved, the state vector is used as the original signal, the ideal function as the tracking signal, and the design error function as the sliding mode surface. Meanwhile, in order to eliminate the vibration caused by the sliding mode functions, a fractional order robust differential observer is introduced in the estimation error. Adaptive rates are introduced in the design of the controller so that the control converges to a small interval in a finite time.

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“…Finite-time controllers have been extensively designed considering other systems [ 22 , 23 , 24 , 25 , 26 ] rather than UUVs. However, a few works presenting an SMC with some form of finite-time convergence for UUVs can be found in the literature.…”

Section: Introductionmentioning

confidence: 99%

Finite-Time Controller for Coordinated Navigation of Unmanned Underwater Vehicles in a Collaborative Manipulation Task

González-García

Narcizo-Nuci

Gómez-Espinosa

et al. 2022

Sensors

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Unmanned underwater vehicles perform inspection and maintenance tasks in complex and changing environments. Some of these tasks require synchronous navigation of multiple vehicles, which is challenging. This paper proposes a synchronous navigation scheme for two BlueROV2 underwater vehicles for a coordinated multi-vehicle task. In the proposed scheme, the vehicles perform the collaborative task of grasping, transporting, and releasing an object. In this scheme, no vehicle-to-vehicle communication is required. A model-free second-order sliding mode controller with finite-time convergence is used to accomplish this task. The controller’s convergence time is user-defined and does not depend on the physical or hydrodynamic parameters of the vehicle, unlike the other finite-time controllers found in the literature. Simulation experiments were conducted to verify the controller’s performance, including high ocean currents as external disturbances. Comparisons were made with two state-of-the-art controllers with finite-time convergence. The results showed that the proposed controller achieved the best results, as the user-defined convergence time was achieved for both vehicles and the collaborative task was completed, no ripples, deviations, or oscillations were observed, and no chattering occurred. The results proved the robustness of the controller in the presence of high ocean currents without the need to readjust the parameters.

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Adaptive Neural Network Finite-Time Control of Uncertain Fractional-Order Systems with Unknown Dead-Zone Fault via Command Filter

Cited by 4 publications

References 48 publications

Prescribed Performance Tracking Control of Lower-Triangular Systems with Unknown Fractional Powers

Prescribed Performance Tracking Control of Lower-Triangular Systems with Unknown Fractional Powers

Finite-time Neural Network Adaptive Sliding Mode Control of Fractional-Order Chaotic Systems With Disturbance Observer And Fractional Robust Differentiator

Finite-Time Controller for Coordinated Navigation of Unmanned Underwater Vehicles in a Collaborative Manipulation Task

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