Neural‐network‐based robust terminal sliding‐mode control of quadrotor

Lin, Xianqing; Wang, Yulu; Liu, Yunfei

doi:10.1002/asjc.2478

Cited by 39 publications

(29 citation statements)

References 32 publications

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“…The radial basis function network model is an artificial neural network model that uses the radial basis function as the activation function. The RBF network [42] is based on the function approximation theory and is also a forward network. However, compared with the BP network as a typical global approximation network, the RBF network is a local approximation network, which only exists in a certain local area of the input space.…”

Section: Rbf Neural Network Modelmentioning

confidence: 99%

Optimization of Extraction Process Based on Neural Network

Sun

Chen

2022

AJOCS

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Liquid-liquid extraction is a chemical unit operation that utilizes the difference in solubility or distribution ratio of target components in two immiscible solvents to achieve separation, extraction or purification. There are many factors that affect the extraction efficiency, and it is difficult to quickly optimize the process using traditional methods. Artificial neural network is a system structure composed of multiple artificial neuron models, with functions such as self-learning, associative storage and fault tolerance. It can be used for optimization or control of multi-variable complex systems, and has been successfully applied to the extraction process of various products. optimization. This paper discusses the basic situation of artificial neural network, and analyzes the research progress of extraction process optimization based on neural network.

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Section: Rbf Neural Network Modelmentioning

confidence: 99%

Optimization of Extraction Process Based on Neural Network

Sun

Chen

2022

AJOCS

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“…Several modeling and control techniques have been explored, including intelligent control based on fuzzy logic approaches and sliding mode 19 and/or both. 20 These above approaches suffer from a slow response time which makes their implementation in real time complicated and very hard.…”

Section: Introductionmentioning

confidence: 99%

Flatness tracking control scheme of rehabilitation exoskeleton robot with dynamic uncertainties

Bououden

Brahmi

Rahman

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part I:

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Artificial limbs are new robotic devices created to help stroke victims in the rehabilitation process. In this article, we focus our work on applying passive, active or assistive control strategies to provide a physical assistance and rehabilitation by a 7-degree-of-freedom exoskeleton robot with nonlinear uncertain dynamics and unknown bounded external disturbances due to the robot user’s physiological characteristics. The flatness controller combined with time-delay estimation is designed for the 7-degree-of-freedom exoskeleton robot called ETS-MARSE (Ecole de Technologie Supérieure—Motion Assistive Robotic-exoskeleton for Superior Extremity) in order to ensure a passive rehabilitation exercises with a high level of tracking accuracy and robustness against the uncertainty constraints. The stability analysis of such systems is proven using the Lyapunov–Krasovskii functional theory. This approach is illustrated by experimental results with healthy human to highlight the efficiency of the suggested controller scheme.

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“…This increases the difficulty of measurement. In order to deal with uncertain dynamics, neural network (NN) approaches have been considered due to their inherent advantages including excellent approximation and learning performance [1,7,[15][16][17][18][19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Swarm Control for Connectivity-Preserving and Collision-Avoiding Unmanned Surface Vehicles Subject to Multiple Constraints

Xia

Sun

Xia

2022

JMSE

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This paper investigates swarm control for unmanned surface vessels subject to multiple constraints. These constraints can be summarized as model parameter uncertainty, the unavailability of velocity measurements, time-varying environmental disturbances, input saturation and output constraints. Firstly, to recover unmeasured velocity information, to identify unknown vehicle dynamics and to estimate time-varying environmental disturbances, a neural adaptive state observer is designed for each vessel. Secondly, to avoid complex calculations, a second-order linear tracking differentiator is employed to generate a smooth reference signal and to extract the time derivative of the kinematic control law. Thirdly, to solve the input saturation, an auxiliary dynamic system is introduced. Fourthly, the barrier Lyapunov function is used to achieve connectivity preservation, collision avoidance and swarm control. Meanwhile, by using the estimated velocities of vessels, an output feedback controller is designed. The stability of the closed-loop system is proved. The simulation results show the effectiveness of the proposed swarm control strategy.

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Neural‐network‐based robust terminal sliding‐mode control of quadrotor

Cited by 39 publications

References 32 publications

Optimization of Extraction Process Based on Neural Network

Optimization of Extraction Process Based on Neural Network

Flatness tracking control scheme of rehabilitation exoskeleton robot with dynamic uncertainties

Swarm Control for Connectivity-Preserving and Collision-Avoiding Unmanned Surface Vehicles Subject to Multiple Constraints

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