Modeling of nonlinear nonstationary dynamic systems with a novel class of artificial neural networks

1999 ◽  
Vol 10 (2) ◽  
pp. 327-339 ◽  
Author(s):  
M. Iatrou ◽  
T.W. Berger ◽  
V.Z. Marmarelis
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Dynamic Systems ◽  
Artificial Neural

scholarly journals Adaptive Robust Motion Control of Quadrotor Systems Using Artificial Neural Networks and Particle Swarm Optimization

Mathematics ◽  
2021 ◽  
Vol 9 (19) ◽  
pp. 2367
Author(s):  
Hugo Yañez-Badillo ◽  
Francisco Beltran-Carbajal ◽  
Ruben Tapia-Olvera ◽  
Antonio Favela-Contreras ◽  
Carlos Sotelo ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Dynamic Systems ◽  
Tracking Control ◽  
Motion Tracking ◽  
Swarm Optimization ◽  
B Spline ◽  
Control Scheme ◽  
Taylor Series Expansions ◽  
Artificial Neural

Most of the mechanical dynamic systems are subjected to parametric uncertainty, unmodeled dynamics, and undesired external vibrating disturbances while are motion controlled. In this regard, new adaptive and robust, advanced control theories have been developed to efficiently regulate the motion trajectories of these dynamic systems while dealing with several kinds of variable disturbances. In this work, a novel adaptive robust neural control design approach for efficient motion trajectory tracking control tasks for a considerably disturbed non-linear under-actuated quadrotor system is introduced. Self-adaptive disturbance signal modeling based on Taylor-series expansions to handle dynamic uncertainty is adopted. Dynamic compensators of planned motion tracking errors are then used for designing a baseline controller with adaptive capabilities provided by three layers B-spline artificial neural networks (Bs-ANN). In the presented adaptive robust control scheme, measurements of position signals are only required. Moreover, real-time accurate estimation of time-varying disturbances and time derivatives of error signals are unnecessary. Integral reconstructors of velocity error signals are properly integrated in the output error signal feedback control scheme. In addition, the appropriate combination of several mathematical tools, such as particle swarm optimization (PSO), Bézier polynomials, artificial neural networks, and Taylor-series expansions, are advantageously exploited in the proposed control design perspective. In this fashion, the present contribution introduces a new adaptive desired motion tracking control solution based on B-spline neural networks, along with dynamic tracking error compensators for quadrotor non-linear systems. Several numeric experiments were performed to assess and highlight the effectiveness of the adaptive robust motion tracking control for a quadrotor unmanned aerial vehicle while subjected to undesired vibrating disturbances. Experiments include important scenarios that commonly face the quadrotors as path and trajectory tracking, take-off and landing, variations of the quadrotor nominal mass and basic navigation. Obtained results evidence a satisfactory quadrotor motion control while acceptable attenuation levels of vibrating disturbances are exhibited.

AN APPROACH TO EXPLAIN NEURAL NETWORKS USING SYMBOLIC ALGORITHMS

2002 ◽  
Vol 02 (04) ◽  
pp. 365-376 ◽  
Author(s):  
CLAUDIA R. MILARÉ ◽  
ANDRÉ C. P. DE L. F. DE CARVALHO ◽  
MARIA C. MONARD
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Learning Community ◽  
Dynamic Systems ◽  
Concept Representation ◽  
Symbolic Representations ◽  
Symbolic Learning ◽  
Artificial Neural ◽  
Clustering Pattern ◽  
Symbolic Algorithms

Although Artificial Neural Networks have been satisfactorily employed in several problems, such as clustering, pattern recognition, dynamic systems control and prediction, they still suffer from significant limitations. One of them is that the induced concept representation is not usually comprehensible to humans. Several techniques have been suggested to extract meaningful knowledge from trained networks. This paper proposes the use of symbolic learning algorithms, commonly used by the Machine Learning community, such as C4.5, C4.5rules and CN2, to extract symbolic representations from trained networks. The approach proposed is similar to that used by the Trepan algorithm, which extracts symbolic representations, expressed as decision trees, from trained networks. Experimental results are presented and discussed in order to compare the knowledge extracted from Artificial Neural Networks using the proposed approach and the Trepan approach. Results are compared regarding two aspects: fidelity and comprehensibility.

FUTURE OF ARTIFICIAL NEURAL NETWORKS IN SPACE EXPLORATION AND SPACE ENGINEERING

2018 ◽  
pp. 6-11
Author(s):  
Kobiljon Kh. Zoidov ◽  
◽  
Svetlana V. Ponomareva ◽  
Daniel I. Serebryansky ◽  
◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Space Exploration ◽  
Artificial Neural
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