2,492 research outputs found
A novel double-convection chaotic attractor, its adaptive control and circuit simulation
A 3-D novel double-convection chaotic system with three nonlinearities is proposed in this research work. The dynamical properties of the new chaotic system are described in terms of phase portraits, Lyapunov exponents, Kaplan-Yorke dimension, dissipativity, stability analysis of equilibria, etc. Adaptive control and synchronization of the new chaotic system with unknown parameters are achieved via nonlinear controllers and the results are established using Lyapunov stability theory. Furthermore, an electronic circuit realization of the new 3-D novel chaotic system is presented in detail. Finally, the circuit experimental results of the 3-D novel chaotic attractor show agreement with the numerical simulations
Interpretable PID Parameter Tuning for Control Engineering using General Dynamic Neural Networks: An Extensive Comparison
Modern automation systems rely on closed loop control, wherein a controller
interacts with a controlled process, based on observations. These systems are
increasingly complex, yet most controllers are linear
Proportional-Integral-Derivative (PID) controllers. PID controllers perform
well on linear and near-linear systems but their simplicity is at odds with the
robustness required to reliably control complex processes. Modern machine
learning offers a way to extend PID controllers beyond their linear
capabilities by using neural networks. However, such an extension comes at the
cost of losing stability guarantees and controller interpretability. In this
paper, we examine the utility of extending PID controllers with recurrent
neural networks-namely, General Dynamic Neural Networks (GDNN); we show that
GDNN (neural) PID controllers perform well on a range of control systems and
highlight how they can be a scalable and interpretable option for control
systems. To do so, we provide an extensive study using four benchmark systems
that represent the most common control engineering benchmarks. All control
benchmarks are evaluated with and without noise as well as with and without
disturbances. The neural PID controller performs better than standard PID
control in 15 of 16 tasks and better than model-based control in 13 of 16
tasks. As a second contribution, we address the lack of interpretability that
prevents neural networks from being used in real-world control processes. We
use bounded-input bounded-output stability analysis to evaluate the parameters
suggested by the neural network, thus making them understandable. This
combination of rigorous evaluation paired with better interpretability is an
important step towards the acceptance of neural-network-based control
approaches. It is furthermore an important step towards interpretable and
safely applied artificial intelligence
A New Chaotic System with Line of Equilibria: Dynamics, Passive Control and Circuit Design
A new chaotic system with line equilibrium is introduced in this paper. This system consists of five terms with two transcendental nonlinearities and two quadratic nonlinearities. Various tools of dynamical system such as phase portraits, Lyapunov exponents, Kaplan-Yorke dimension, bifurcation diagram and Poincarè map are used. It is interesting that this system has a line of fixed points and can display chaotic attractors. Next, this paper discusses control using passive control method. One example is given to insure the theoretical analysis. Finally, for the new chaotic system, An electronic circuit for realizing the chaotic system has been implemented. The numerical simulation by using MATLAB 2010 and implementation of circuit simulations by using MultiSIM 10.0 have been performed in this study
Earth System Modeling 2.0: A Blueprint for Models That Learn From Observations and Targeted High-Resolution Simulations
Climate projections continue to be marred by large uncertainties, which
originate in processes that need to be parameterized, such as clouds,
convection, and ecosystems. But rapid progress is now within reach. New
computational tools and methods from data assimilation and machine learning
make it possible to integrate global observations and local high-resolution
simulations in an Earth system model (ESM) that systematically learns from
both. Here we propose a blueprint for such an ESM. We outline how
parameterization schemes can learn from global observations and targeted
high-resolution simulations, for example, of clouds and convection, through
matching low-order statistics between ESMs, observations, and high-resolution
simulations. We illustrate learning algorithms for ESMs with a simple dynamical
system that shares characteristics of the climate system; and we discuss the
opportunities the proposed framework presents and the challenges that remain to
realize it.Comment: 32 pages, 3 figure
Hybrid Chaos Synchronization of 3-Cells Cellular Neural Network Attractors via Adaptive Control Method
Abstract: In this research work, we first discuss the properties of the 3-cells cellular neural network (CNN) attractor discovered b
Hybrid Synchronization of the Generalized Lotka-Volterra Three-Species Biological Systems via Adaptive Control
Abstract: Since the recent research has shown the importance of biological control in many biological systems appearing in nature, this research paper investigates research in the dynamic and chaotic analysis of the generalized Lotka-Volterra three-species biological system, which was studied b
Memory embedded non-intrusive reduced order modeling of non-ergodic flows
Generating a digital twin of any complex system requires modeling and
computational approaches that are efficient, accurate, and modular. Traditional
reduced order modeling techniques are targeted at only the first two but the
novel non-intrusive approach presented in this study is an attempt at taking
all three into account effectively compared to their traditional counterparts.
Based on dimensionality reduction using proper orthogonal decomposition (POD),
we introduce a long short-term memory (LSTM) neural network architecture
together with a principal interval decomposition (PID) framework as an enabler
to account for localized modal deformation, which is a key element in accurate
reduced order modeling of convective flows. Our applications for convection
dominated systems governed by Burgers, Navier-Stokes, and Boussinesq equations
demonstrate that the proposed approach yields significantly more accurate
predictions than the POD-Galerkin method, and could be a key enabler towards
near real-time predictions of unsteady flows
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