Adaptive control based on neural network for uncertain space robot

2011 ◽  
Author(s):  
Zhiyong Tang ◽  
Shao He ◽  
Mingyi Yang ◽  
Zhongcai Pei
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Space Robot

Partitioned Adaptive Control Based on Neural Network of a Flexible Space Robot After Capture Operation

2018 ◽  
Author(s):  
Jing Cheng ◽  
Li Chen ◽  
Jianxun Liang ◽  
Wei Ma
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Space Robot ◽  
Target System ◽  
Fast System ◽  
Control Scheme ◽  
Adaptive Neural Network Control ◽  
Combination System ◽  
The Impact ◽  
Slow System

The partitioned adaptive control and vibration suppression of free-floating space robot with flexible arms in post-impact process are studied. At first, the dynamic model of combination system after flexible space robot system capturing a target system is established based on the collision theory; the impact effect of space robot combination system after capture operation is analyzed at the same time. Secondly, based on the double time scale decomposition theory, the unstable combination system is decomposed into fast system and slow system, representing the rigid motion of the system and the flexible vibration respectively. To satisfy the compute capacity of space-borne computer and modular design concept, the slow system is considered as a set of interconnected subsystems and a decentralized adaptive neural network control scheme is designed. Neural network is applied to approximating the unknown dynamic of the subsystems; an adaptive sliding mode controller is designed to eliminate both interconnection term and approximation error. The control algorithm has a cutting edge in independent control signal and reduced calculation amount. The Linear Quadratic Optimal control scheme is designed for fast system to suppress the elastic vibration of the flexible manipulators. At last, numerical example demonstrates the validity of the proposed composite control scheme.

scholarly journals Adaptive Control Structure with Neural Data Processing Applied for Electrical Drive with Elastic Shaft

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3389
Author(s):  
Marcin Kamiński ◽  
Krzysztof Szabat
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Dynamic Properties ◽  
Experimental Studies ◽  
Drive System ◽  
Particle Swarm Optimizer ◽  
The Neural Network ◽  
Control Concept ◽  
Constant Coefficients ◽  
Selection Of

This paper presents issues related to the adaptive control of the drive system with an elastic clutch connecting the main motor and the load machine. Firstly, the problems and the main algorithms often implemented for the mentioned object are analyzed. Then, the control concept based on the RNN (recurrent neural network) for the drive system with the flexible coupling is thoroughly described. For this purpose, an adaptive model inspired by the Elman model is selected, which is related to internal feedback in the neural network. The indicated feature improves the processing of dynamic signals. During the design process, for the selection of constant coefficients of the controller, the PSO (particle swarm optimizer) is applied. Moreover, in order to obtain better dynamic properties and improve work in real conditions, one model based on the ADALINE (adaptive linear neuron) is introduced into the structure. Details of the algorithm used for the weights’ adaptation are presented (including stability analysis) to perform the shaft torque signal filtering. The effectiveness of the proposed approach is examined through simulation and experimental studies.

Real-Time Modular Deep Neural Network-Based Adaptive Control of Nonlinear Systems

2021 ◽  
pp. 1-1
Author(s):  
Duc M. Le ◽  
Max L. Greene ◽  
Wanjiku A. Makumi ◽  
Warren E. Dixon
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Nonlinear Systems ◽  
Real Time ◽  
Deep Neural Network
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