Adaptive neural network based dynamic surface control for uncertain dual arm robots

Pham, Dung Tien; Nguyen, Thai Van; Le, Hai Xuan; Nguyen, Linh; Thai, Nguyen Huu; Phan, Tuan Anh; Pham, Hai Tuan; Duong, Anh Hoai

doi:10.1007/s40435-019-00600-2

Cited by 21 publications

(11 citation statements)

References 40 publications

(37 reference statements)

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“…Due to its fundamentality, researchers, engineers and practitioners who design a control law for a underactuated crane system always concern about robustness in the system response due to its parameter uncertainties and actuator nonlinearities. To address the concern, sliding mode control (SMC) method has then been favoured for those systems [8][9][10][11][12][13][14][15][16]. For instance, the authors in [17][18][19] proposed the robust SMC controllers for a gantry crane, which allow the system with uncertain parameters and nonlinear actuators to robustly work under external disturbances in a working environment.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Adaptive Fuzzy Hierarchical Sliding Mode Control Strategy for 6 Degrees of Freedom Overhead Crane

Nguyen¹

2022

Preprint

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<pre>The paper proposes a new approach to efficiently control a three-dimensional overhead crane with six degrees of freedom (DoF). In addition to five usual output variables including three positions of the trolley, bridge and pulley and two swing angles of the hoisting cable, it is proposed to consider elasticity of the hoisting cable, which causes oscillation in the cable direction. That is, there exists $6^{th}$ under-actuated output in the crane system. To design an efficient controller for the six-DoF crane, it first employs the hierarchical sliding mode control approach, which not only guarantees stability but also minimizes sway and oscillation of the overhead crane when it transports a payload to desired location. Moreover, the unknown and uncertain parameters of the system caused by its actuator nonlinearity and external disturbances are adaptively estimated and inferred by utilizing the fuzzy inference rule mechanism, which results in efficient operations of the crane in real time. More importantly, stabilization of the crane controlled by the proposed algorithm is theoretically proved by the use of the Lyapunov function. The proposed control approach was implemented in the synthetic environment for the extensive evaluation, where the obtained results demonstrate its effectiveness.</pre>

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Section: Introductionmentioning

confidence: 99%

An Efficient Adaptive Fuzzy Hierarchical Sliding Mode Control Strategy for 6 Degrees of Freedom Overhead Crane

Nguyen¹

2022

Preprint

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“…In other words, the parameters of the AUV and control systems are highly uncertain and impractical to be exactly determined. Therefore, it is practically expected that those parameters are adaptively estimated online [26,27]. Theoretically, the unknown, uncertain and nonlinear parameters can be learned through some adaptive strategies such as neural networks and fuzzy logic systems [25].…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Hierarchical Sliding Mode Controller for Autonomous Underwater Vehicles

Dinh

Nguyen

et al. 2021

Electronics

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The paper addresses a problem of efficiently controlling an autonomous underwater vehicle (AUV), where its typical underactuated model is considered. Due to critical uncertainties and nonlinearities in the system caused by unavoidable external disturbances such as ocean currents when it operates, it is paramount to robustly maintain motions of the vehicle over time as expected. Therefore, it is proposed to employ the hierarchical sliding mode control technique to design the closed-loop control scheme for the device. However, exactly determining parameters of the AUV control system is impractical since its nonlinearities and external disturbances can vary those parameters over time. Thus, it is proposed to exploit neural networks to develop an adaptive learning mechanism that allows the system to learn its parameters adaptively. More importantly, stability of the AUV system controlled by the proposed approach is theoretically proved to be guaranteed by the use of the Lyapunov theory. Effectiveness of the proposed control scheme was verified by the experiments implemented in a synthetic environment, where the obtained results are highly promising.

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“…Due to its fundamentality, researchers, engineers and practitioners who design a control law for a SIMO under-actuated robotic system always concern about robustness in the system response due to its parameter uncertainties and actuator nonlinearities. To address the concern, sliding mode control (SMC) method has been then favoured for those systems [3]- [6]. For instance, the authors in [7]- [9] proposed the robust SMC controllers for a gantry crane, which allow the system with uncertain parameters and nonlinear actuators to robustly work under external disturbances in a working environment.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Approach for SIMO Systems using Adaptive Fuzzy Hierarchical Sliding Mode Control

Nguyen¹

2021

Preprint

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<div>The paper addresses the problem of efficiently controlling a class of single input multiple output (SIMO) underactuated robotic systems such as a two dimensional inverted pendulum cart or a two dimensional overhead crane. It is first proposed to employ the hierarchical sliding mode control approach to design a control law, which guarantees stability and anti-swing of the vehicle when it is driven on a predefined trajectory. More importantly, the unknown and uncertain parameters of the system caused by its actuator nonlinearity and external disturbances are adaptively estimated and inferred by the proposed fuzzy logic mechanism, which results in the efficient operation of the SIMO under-actuated system in real time. The proposed algorithm was then implemented in the synthetic environment, where the obtained results demonstrate its effectiveness.</div>

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Adaptive neural network based dynamic surface control for uncertain dual arm robots

Cited by 21 publications

References 40 publications

An Efficient Adaptive Fuzzy Hierarchical Sliding Mode Control Strategy for 6 Degrees of Freedom Overhead Crane

An Efficient Adaptive Fuzzy Hierarchical Sliding Mode Control Strategy for 6 Degrees of Freedom Overhead Crane

An Adaptive Hierarchical Sliding Mode Controller for Autonomous Underwater Vehicles

An Efficient Approach for SIMO Systems using Adaptive Fuzzy Hierarchical Sliding Mode Control

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