Neural network sliding mode robot control

Robotica ◽  
1997 ◽  
Vol 15 (1) ◽  
pp. 23-30 ◽  
Author(s):  
Karel Jezernik ◽  
Miran Rodič ◽  
Riko šafarič ◽  
Boris Curk
Keyword(s):  
Neural Network ◽  
Robot Control ◽  
Sliding Mode ◽  
Variable Structure ◽  
Network Control ◽  
Neural Network Control ◽  
Sliding Modes ◽  
Neural Network Learning ◽  
Network Learning ◽  
Learning Rules

This paper develops a method for neural network control design with sliding modes in which robustness is inherent. Neural network control is formulated to become a class of variable structure (VSS) control. Sliding modes are used to determine best values for parameters in neural network learning rules, thereby robustness in learning control can be improved. A switching manifold is prescribed and the phase trajectory is demanded to satisfy both, the reaching condition and the sliding condition for sliding modes.

Neural network control of simple limb movements

1990 ◽  
Vol 68 (1) ◽  
pp. 126-130 ◽  
Author(s):  
Hon C. Kwan ◽  
Tet H. Yeap ◽  
Bai C. Jiang ◽  
Donald Borrett
Keyword(s):  
Neural Network ◽  
Network Control ◽  
Neural Network Control ◽  
Joint Movement ◽  
Initial State ◽  
Neural Network Learning ◽  
Network Learning ◽  
Nonlinear Network ◽  
Movement Trajectories ◽  
Final Equilibrium State

It is possible to embed the control and computation of a simple single-joint movement at different speeds by a small nonlinear network of neuron-like elements. The network "learns" by appropriate adjustment of the strengths of interconnection, or synaptic weights, between the neuron-like elements. The learning of a few movement trajectories is generalized to the learning of a family of unlearned trajectories. These observations are in support of our hypothesis that relaxation of a network from an initial state to a final equilibrium state is both causal and computational to movement generation and control.Key words: control of movement, neural network, learning, nonlinear dynamics.

Backstepping sliding mode neural network control system for hypersonic vehicle

2019 ◽  
Vol 27 (11) ◽  
pp. 2392-2401
Author(s):  
刘 蓉 LIU Rong ◽  
黄大庆 HUANG Da-qing ◽  
姜定国 JIANG Ding-guo
Keyword(s):  
Neural Network ◽  
Control System ◽  
Sliding Mode ◽  
Hypersonic Vehicle ◽  
Network Control ◽  
Neural Network Control ◽  
Network Control System

A Review on Pitch Angle Control Strategy of Variable Pitch Wind Turbines

2013 ◽  
Vol 772 ◽  
pp. 744-748 ◽  
Author(s):  
Chao Tan ◽  
Hong Hua Wang
Keyword(s):  
Wind Turbines ◽  
Pitch Angle ◽  
Sliding Mode ◽  
Integrated Control ◽  
Variable Structure ◽  
Network Control ◽  
Neural Network Control ◽  
Variable Pitch ◽  
Structure Control ◽  
Sliding Mode Variable Structure

This paper summarizes the development process of wind turbines control technology, reviews the application of traditional control, sliding mode variable structure control, H robust control, adaptive control, fuzzy control, artificial neural network control and integrated control in the pitch angle control system of variable pitch wind turbines, points out its present situation and development prospect.

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