Neurodynamics-based Model Predictive Control of Continuous-time Under-actuated Mechatronic Systems

Wang, Jiasen; Wang, Jun; Han, Qing‐Long

doi:10.1109/tmech.2020.3016757

Cited by 27 publications

(4 citation statements)

References 57 publications

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“…Neural network is a subset of machine learning with numerous applications such as compressed image reconstruction [ 10 ], asset allocation [ 11 , 12 ], non-negative matrix factorization [ 13 , 14 ], and model predictive control [ 15 ]. With the development of transfer learning, G.E.…”

Section: Literature Reviewmentioning

confidence: 99%

A BERT Framework to Sentiment Analysis of Tweets

Bello

Leung

2023

Sensors

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Sentiment analysis has been widely used in microblogging sites such as Twitter in recent decades, where millions of users express their opinions and thoughts because of its short and simple manner of expression. Several studies reveal the state of sentiment which does not express sentiment based on the user context because of different lengths and ambiguous emotional information. Hence, this study proposes text classification with the use of bidirectional encoder representations from transformers (BERT) for natural language processing with other variants. The experimental findings demonstrate that the combination of BERT with CNN, BERT with RNN, and BERT with BiLSTM performs well in terms of accuracy rate, precision rate, recall rate, and F1-score compared to when it was used with Word2vec and when it was used with no variant.

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Section: Literature Reviewmentioning

confidence: 99%

A BERT Framework to Sentiment Analysis of Tweets

Bello

Leung

2023

Sensors

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“…7. For comparison purposes, the traditional gradient descent-based recurrent neural network is denoted as [28],ḋ…”

Section: Varying-parameter One-layer Projection Neural Network (Vp-onelpnn)mentioning

confidence: 99%

Nonlinear Model Predictive Control for Mobile Medical Robot Using Neural Optimization

et al. 2021

IEEE Trans. Ind. Electron.

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Mobile medical robots have been widely used in various structured scenarios, such as hospital drug delivery, public area disinfection, and medical examinations. Considering the challenge of environment modeling and controller design, how to achieve the information from the human demonstration in a structured environment directly arouse our interests. Learning skills is a powerful way that can reduce the complexity of algorithm in searching space. This is especially true when naturally acquiring new skills, as mobile medical robot must learn from the interaction with a human being or the environment with limited programming effort. In this paper, a learning scheme with nonlinear model predictive control (NMPC) is proposed for mobile robot path tracking. The learning-by-imitation system consists of two levels of hierarchy: in the first level, a multi-virtual spring-dampers system is presented for imitation of the mobile robot's trajectories; and in the second level, the NMPC method is used in the motion control system. The NMPC strategy utilizes a varying-parameter one-layer projection neural network to solve an online quadratic programming optimization via iteration over a limited receding horizon. The proposed algorithm is evaluated on a mobile medical robot with an emulated trajectory in simulation and three scenarios used in the experiment.

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“…For this reason, the IP system was extended by introducing the Rotary-IP (RIP) system which has two main components including the arm which can rotate in the horizontal plane and the pendulum connected to the arm which can rotate in the vertical plane (Dwivedi et al, 2017; Du et al, 2019; Fukushima et al, 2014; Nath and Dewan, 2017; Watson et al, 2019), whereas the RIP system is an underactuated system with interesting features including nonlinear characteristics and availability, so it is considered as an experimental case study (Kim et al, 2019; Mehedi et al, 2020; Park and Chwa, 2009; Pujol-Vazquez et al, 2018; Roy et al, 2021; Wang et al, 2014). All in all, stability of the position and angular velocity of the arm and up-right balancing of the pendulum are the fundamental control objectives concerned to the RIP system (Hamza et al, 2019; Huang et al, 2012; Li et al, 2017; Wang et al, 2020b; Yiğit, 2017). Due to this reason, some control methods have been applied in the target of stability and swing-up control of the RIP system (Hazem et al, 2020; Patil et al, 2018; Sun et al, 2018; Wasiwitono et al, 2021; Rahmani et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Adaptive finite-time command-filtered backstepping sliding mode control for stabilization of a disturbed rotary-inverted-pendulum with experimental validation

Mofid

Alattas

Mobayen

et al. 2022

Journal of Vibration and Control

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In this paper, the finite-time stabilization of the disturbed and uncertain rotary-inverted-pendulum system is studied based on the adaptive backstepping sliding mode control procedure. For this purpose, first of all, the dynamical equation of the rotary-inverted-pendulum system is obtained in the state-space form in the existence of external disturbances and model uncertainties with unknown bound. Afterward, a novel command filter is defined to enhance the control strategy by consideration of a virtual control input. Therefore, the differential signal is replaced by the output of the command filter to reduce the complicated computing in the control process. Hence, the finite-time convergence of the sliding surface to the origin is attested by using the backstepping sliding mode control scheme according to the Lyapunov theory. Besides, the unknown upper bound of the exterior perturbation and uncertainty is approximated providing the adaptive control technique. Finally, simulations and experimental results are done to demonstrate the impression and proficiency of the suggested method.

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Neurodynamics-based Model Predictive Control of Continuous-time Under-actuated Mechatronic Systems

Cited by 27 publications

References 57 publications

A BERT Framework to Sentiment Analysis of Tweets

A BERT Framework to Sentiment Analysis of Tweets

Nonlinear Model Predictive Control for Mobile Medical Robot Using Neural Optimization

Adaptive finite-time command-filtered backstepping sliding mode control for stabilization of a disturbed rotary-inverted-pendulum with experimental validation

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