Sliding Mode Control for Flexible-link Manipulators Based on Adaptive Neural Networks

Yang, Hongjun; Tan, Min

doi:10.1007/s11633-018-1122-2

Cited by 51 publications

(36 citation statements)

References 24 publications

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“…Kane and Levinson (1980) presented the comparison of different methods for deriving the equations of motion. In addition to these commonly used methods, other techniques to obtain equations of motion of FLMs includes, but are not limited to, Hamilton's principle (Ding et al, 1989;Dogan and Morgül, 2010;Yang et al, 2018b;Yang and Tan, 2018;Yang et al, 2019;Cao and Liu, 2020;Meng and He, 2020) and Gibbs-Appell formulation (Korayem and Shafei, 2007).…”

Section: Methods Of Deriving Equations Of Motionmentioning

confidence: 99%

“…Lochan et al (2016b) and Lochan and Roy (2018) used the sliding mode control technique with the PID sliding surface and the second-order sliding surface respectively, to control a two-link flexible manipulator. Yang and Tan (2018) designed a sliding mode control for joint position control and vibration suppression of a single-link flexible manipulator by using an adaptive neural approximator to compensate for the modeling uncertainties and external disturbances. Si et al (2017) proposed a fast non-singular terminal sliding mode control for trajectory tracking of the two-link flexible manipulators with payload and external disturbances.…”

Section: Model-free Control Techniquesmentioning

confidence: 99%

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Review on Modeling and Control of Flexible Link Manipulators

Subedi¹,

Tyapin²,

Hovland³

2020

MIC

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This paper presents a review of dynamic modeling techniques and various control schemes to control flexible link manipulators (FLMs) that were studied in recent literature. The advantages and complexities associated with the FLMs are discussed briefly. A survey of the reported studies is carried out based on the method used for modeling link flexibility and obtaining equations of motion of the FLMs. The control techniques are reviewed by classifying them into two main categories: model-based and model-free control schemes. The merits and limitations of different modeling and control methods are highlighted.

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Section: Methods Of Deriving Equations Of Motionmentioning

confidence: 99%

Section: Model-free Control Techniquesmentioning

confidence: 99%

Review on Modeling and Control of Flexible Link Manipulators

Subedi¹,

Tyapin²,

Hovland³

2020

MIC

View full text Add to dashboard Cite

show abstract

“…That can influence the system performance and stability. Yang and Tan in [26] designed a sliding mode boundary controller for a single flexible-link manipulator based on adaptive radial basis function (RBF) neural network in presence of the uncertainties and external disturbances. In [27] Xu developed the adaptive sliding mode control based on the neural networks for flexible-joint robot with compound uncertainty.…”

Section: Introductionmentioning

confidence: 99%

Reconfigurable control of flexible joint robot with actuator fault and uncertainty

Elghoul

Tellili

Abdelkrim

2019

Journal of Electrical Engineering

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This paper presents the fault tolerant control (FTC) of a flexible joint robot using singular perturbation method in order to compensate for the lost performance due to the occurrence of actuator fault and the uncertainty. This FTC is based on Lyapunov redesign principle. The singular perturbation method is used to reduce the dynamic model of the flexible joint robot in a fast and slow subsystem. The time scale reduction of the flexible joint model is carried out when their joint stiffness is large enough and the singular perturbation parameter is set to zero. The fault-tolerant control structure in this paper is based on two parts. The first term described the composite control for the system without defect and without uncertainty which represents the sum between slow and fast controllers. While the second term of the fault tolerant command describes additive control designed to compensate for the fault effect of the actuator on the uncertain system. The additive approach is based on the Lyapunov theorem, which guarantees asymptotic stability despite the presence of actuator defects and the parametric uncertainty. The theoretical results are applied on a robot manipulator with a single flexible joint.

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“…Therefore, HBFNNs have become one of the hotspots of feedforward neural networks research. Nowadays, HBFNNs have been widely used in pattern recognition (Lu, 2008), intelligent robot (Rao, Ramji, Rao, Vasu, & Puneeth, 2017;Yang & Tan, 2018), image processing (Ha et al, 2018), biology and medicine (Nieminen, Hakama, Viikki, Tarkkanen, & Anttila, 2003;Vicente et al, 2012), economy (Kimoto, Asakawa, Yoda, & Takeoka, 1990) and other fields.…”

Section: Introductionmentioning

confidence: 99%

A survey on hyper basis function neural networks

Zhou

Pang

et al. 2019

Systems Science & Control Engineering

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Hyper basis function neural networks (HBFNNs) have gained considerable attention in recent years, which have shown good performance in a variety of application domains. In this paper, we first briefly introduce the development of neural networks. Then the structure of HBFNNs is presented in detail. HBFNNs are an extension of radial basis function neural networks (RBFNNs), which use the weighted norm instead of the Euclidean norm to represent the distance from input data to hidden layer neuron centres. With this change, the generalization ability of neural networks becomes stronger than that of RBFNNs. Subsequently, we summarize several commonly used training methods for HBFNNs, including static training methods and dynamic training methods. Finally, we give several typical application fields of HBFNNs.

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Sliding Mode Control for Flexible-link Manipulators Based on Adaptive Neural Networks

Cited by 51 publications

References 24 publications

Review on Modeling and Control of Flexible Link Manipulators

Review on Modeling and Control of Flexible Link Manipulators

Reconfigurable control of flexible joint robot with actuator fault and uncertainty

A survey on hyper basis function neural networks

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