A New Adaptive RISE Feedforward Approach based on Associative Memory Neural Networks for the Control of PKMs

In this paper, a RISE (Robust Integral of the Sign Error) controller with adaptive feedforward compensation terms based on Associative Memory Neural Network (AMNN) type B-Spline is proposed to regulate the positioning of a Delta Parallel Robot (DPR) with three degrees of freedom. Parallel Kinematic...

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Bibliographic Details
Published in:Journal of intelligent & robotic systems 2020-12, Vol.100 (3-4), p.827-847
Main Authors: Escorcia-Hernández, Jonatan Martín, Aguilar-Sierra, Hipólito, Aguilar-Mejia, Omar, Chemori, Ahmed, Arroyo-Núñez, José Humberto
Format: Article
Language:English
Subjects:
Analysis
Artificial Intelligence
Associative memory
Automatic
Case studies
Compensation
Control
Controllers
Electrical Engineering
Engineering
Engineering Sciences
Extreme values
Feedforward control
Mathematical analysis
Mechanical Engineering
Mechatronics
Memory
Neural networks
Nonlinear systems
Numerical analysis
Parallel degrees of freedom
Robot arms
Robotics
Robustness (mathematics)
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Summary:In this paper, a RISE (Robust Integral of the Sign Error) controller with adaptive feedforward compensation terms based on Associative Memory Neural Network (AMNN) type B-Spline is proposed to regulate the positioning of a Delta Parallel Robot (DPR) with three degrees of freedom. Parallel Kinematic Manipulators (PKMs) are highly nonlinear systems, so the design of a suitable control scheme represents a significant challenge given that these kinds of systems are continually dealing with parametric and non-parametric uncertainties and external disturbances. The main contribution of this work is the design of an adaptive feedforward compensation term using B-Spline Neural Networks (BSNNs). They make an on-line approximation of the DPR dynamics and integrates it into the control loop. The BSNNs’ functions are bounded according to the extreme values of the desired joint space trajectories that are the BSNNs’ inputs, and their weights are on-line adjusted by gradient descend rules. In order to evaluate the effectiveness of the proposed control scheme with respect to the standard RISE controller, numerical simulations for different case studies under different scenarios were performed.
ISSN:0921-0296
1573-0409
DOI:10.1007/s10846-020-01242-9