Neural network based adaptive tracking control for a class of pure feedback nonlinear systems with input saturation

Zerari, Nassira; Chemachema, Mohamed; Essounbouli, Najib

doi:10.1109/jas.2018.7511255

Cited by 53 publications

(44 citation statements)

References 52 publications

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“…Moreover, the scale factor λ is 20 and the proportion coefficient is 0.001. Simulations consist of two parts: the first part is the simulation of SA scheme (2) solved by RNN model (19); the second part includes the simulations of the MSA scheme (10) solved by RNN model (18) under different parameter settings.…”

Section: Simulationmentioning

confidence: 99%

“…For the problem of redundancy resolution which needs to be solved dynamically, these methods bring about the timedelay problem due to the absence of leveraging the time derivatives of dynamic parameters. Neural networks, with universal approximation property [16], fault-tolerance [17], parallelism [18], and excellent learning capabilities [19], are widely applied to address all kinds of complicated problems [20]. Recurrent neural networks (RNNs) have natural advantages in solving real-time problems because they contain hidden layers that can store data from the past, which is conducive to subsequent computations [21].…”

Section: Introductionmentioning

confidence: 99%

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Recurrent Neural Network for State Adjustment of Redundant Manipulators

Jin

Huang

et al. 2020

IEEE Access

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To make joints of a redundant manipulator moving automatically to a target state, a stateadjustment (SA) scheme is studied and modified in this paper. Specifically, owing to the problem of non-zero initial joint velocity in the SA scheme leading to a potential hazard to redundant manipulators, a modified state-adjustment (MSA) scheme is obtained on the basis of the SA scheme. The MSA scheme achieves the state adjustment by minimizing the differences between the joint angles and the target values. For solving the MSA scheme, a recurrent neural network (RNN) model is derived, of which the critical component is to iterate over the joint angles and joint velocities. The MSA scheme solved by the RNN model enables the redundant manipulator to adjust to the target state automatically while ensuring that the initial joint velocity is zero. Beyond that, several comparative simulations demonstrate the availability and accuracy of the MSA scheme solved by the RNN model in controlling the state adjustment of redundant manipulators. INDEX TERMS State-adjustment scheme, recurrent neural network, redundant manipulators.

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Section: Simulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Recurrent Neural Network for State Adjustment of Redundant Manipulators

Jin

Huang

et al. 2020

IEEE Access

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“…The network informal language (NIL) has become the mainstream. Network new words are emerging one after another, and the subject is generalized [12] [13]. Online social texts exist in rich social contexts, with short text lengths and incomplete information.…”

Section: Introductionmentioning

confidence: 99%

Improved Danmaku Emotion Analysis and Its Application Based on Bi-LSTM Model

et al. 2020

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With the rapid development of social media, danmaku video provides a platform for users to communicate online. To some extent, danmaku video provides emotional timing information and an innovative method to analyze video data. In the age of big data, studying the characteristics of danmaku and its emotional tendencies can not only help us understand the psychological characteristics of users but also feedback the effective information of users to video platforms, which can help the platforms optimize related short video recommendations so that it can provide a more accurate solution for the selection of audiences during video production. However, danmaku is different from traditional comments. Current emotion classification methods are only suitable for two-dimensional classification which are not suitable for danmaku emotion analysis. Aiming at the problems such as the colloquialism, diversity, spelling errors, structural non-linearity informal language on the Internet, diversity of social topics, and context dependency of emotion analysis of the danmaku data, this paper proposes an improved emotion analysis model based on Bi-LSTM model to classify the further four-dimensional emotions of Pleasure, Anger, Sorrow and Joy. Furthermore, we add tags such as comment time and user name to the danmaku information. Experimental results show that the improved model has higher Accuracy, Recall, Precision, and F1-Score under the same conditions compared with the CNN and SVM. The classification effect of improved model is close to the SOTA. Experimental results also show that the improved model can be effectively applied to the analysis of irregular danmaku emotion.

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“…If the gain is unknown, hyperbolic tangent curves [21,22], exponential function [23,24], Gaussian function [25,26] and explicit reference governor [27] are used to analyze the saturation phenomenon. And then, the method of fuzzy [19,28] or neural network controller [29] is designed to avoid the saturation. Among all the method, designing an auxiliary system is a common method to solve input saturation.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Nonlinear Control for the Stabilized Platform With Disturbance and Input Saturation

Zhang

Guan

et al. 2020

IEEE Access

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This paper proposes a control strategy including super-twisting extended state observer (STESO) and sliding mode controller based on backstepping technique for the stabilized platform with disturbance and input saturation. Firstly, the system is modelled to obtain a non-matched nonlinear model. Secondly, two STESOs are designed to estimate the disturbances in the matched channel and the unmatched channel. And then, multi-sliding mode controller based on backstepping technique is designed for non-matched nonlinear system; and the first-order auxiliary system is introduced to alleviate the effect of the input saturation. Furthermore, Lyapunov function is used to prove the stability of the closed system with the proposed controller and finite-time convergence proof is given in this paper. Finally, comparative simulations are carried out and the results show the effectiveness of the proposed controller. It is proved that the control accuracy is improved effectively and the proposed controller can guarantee the consistent tracking error of the system.

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Neural network based adaptive tracking control for a class of pure feedback nonlinear systems with input saturation

Cited by 53 publications

References 52 publications

Recurrent Neural Network for State Adjustment of Redundant Manipulators

Recurrent Neural Network for State Adjustment of Redundant Manipulators

Improved Danmaku Emotion Analysis and Its Application Based on Bi-LSTM Model

Adaptive Nonlinear Control for the Stabilized Platform With Disturbance and Input Saturation

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