Hybrid Force/Position Control of a Collaborative Parallel Robot Using Adaptive Neural Network

2018 ◽  
pp. 280-290
Author(s):  
Seyedhassan Zabihifar ◽  
Arkadi Yuschenko
Keyword(s):  
Neural Network ◽  
Position Control ◽  
Parallel Robot ◽  
Adaptive Neural Network

scholarly journals A Novel Adaptive Neural Network Compensator as Applied to Position Control of a Pneumatic System

2011 ◽  
Vol 02 (04) ◽  
pp. 388-395 ◽  
Author(s):  
Behrad Dehghan ◽  
Sasan Taghizadeh ◽  
Brian Surgenor ◽  
Mohammed Abu-Mallouh
Keyword(s):  
Neural Network ◽  
Position Control ◽  
Pneumatic System ◽  
Adaptive Neural Network ◽  
Neural Network Compensator

Tuning of an Adaptive Neural Network Compensator for Position Control of a Pneumatic System

2011 ◽  
Author(s):  
Behrad Dehghan ◽  
Sasan Taghizadeh ◽  
Brian Surgenor
Keyword(s):  
Neural Network ◽  
Pid Controller ◽  
Position Control ◽  
Experimental Results ◽  
Tracking Performance ◽  
Adaptive Neural Network ◽  
Extra Effort ◽  
Adaptive Nature ◽  
Gantry Robot ◽  
Neural Network Compensator

The paper examines the potential of a novel adaptive neural network compensator (ANNC) for the position control of a pneumatic gantry robot. Previousl experimental results were disappointing, with only a 20% improvement in performance when ANNC was employed with a PID controller. The conclusion was that the level of improvement with ANNC did not warrant the extra effort required for implementation. However, when the tests were repeated after the system had been reconfigured, improvements on the order of 45% to 70% were achieved. This paper presents a tuning procedure for ANNC, confirms the adaptive nature and provides results that support the conclusion that ANNC can indeed provide a significant improvement in tracking performance.

The Use of Adaptive Neural Network in a Voice Identification System

2004 ◽  
Vol 62 (11) ◽  
pp. 1005-1010
Author(s):  
O. N. Katkov ◽  
V. A. Pimenov ◽  
A. P. Ryzhkov
Keyword(s):  
Neural Network ◽  
Identification System ◽  
Adaptive Neural Network ◽  
Voice Identification

scholarly journals Observer-based adaptive neural network backstepping sliding mode control for switched fractional order uncertain nonlinear systems with unmeasured states

2021 ◽  
pp. 002029402110211
Author(s):  
Tao Chen ◽  
Damin Cao ◽  
Jiaxin Yuan ◽  
Hui Yang
Keyword(s):  
Neural Network ◽  
Nonlinear Systems ◽  
Sliding Mode Control ◽  
Fractional Order ◽  
Sliding Mode ◽  
Tracking Error ◽  
Adaptive Neural Network ◽  
Dynamic Surface ◽  
Mode Control ◽  
The Stability

This paper proposes an observer-based adaptive neural network backstepping sliding mode controller to ensure the stability of switched fractional order strict-feedback nonlinear systems in the presence of arbitrary switchings and unmeasured states. To avoid “explosion of complexity” and obtain fractional derivatives for virtual control functions continuously, the fractional order dynamic surface control (DSC) technology is introduced into the controller. An observer is used for states estimation of the fractional order systems. The sliding mode control technology is introduced to enhance robustness. The unknown nonlinear functions and uncertain disturbances are approximated by the radial basis function neural networks (RBFNNs). The stability of system is ensured by the constructed Lyapunov functions. The fractional adaptive laws are proposed to update uncertain parameters. The proposed controller can ensure convergence of the tracking error and all the states remain bounded in the closed-loop systems. Lastly, the feasibility of the proposed control method is proved by giving two examples.

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