2,900 research outputs found
Adaptive Neural Network Feedforward Control for Dynamically Substructured Systems
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Stochastic Nonlinear Control via Finite-dimensional Spectral Dynamic Embedding
Optimal control is notoriously difficult for stochastic nonlinear systems.
Ren et al. introduced Spectral Dynamics Embedding for developing reinforcement
learning methods for controlling an unknown system. It uses an
infinite-dimensional feature to linearly represent the state-value function and
exploits finite-dimensional truncation approximation for practical
implementation. However, the finite-dimensional approximation properties in
control have not been investigated even when the model is known. In this paper,
we provide a tractable stochastic nonlinear control algorithm that exploits the
nonlinear dynamics upon the finite-dimensional feature approximation, Spectral
Dynamics Embedding Control (SDEC), with an in-depth theoretical analysis to
characterize the approximation error induced by the finite-dimension truncation
and statistical error induced by finite-sample approximation in both policy
evaluation and policy optimization. We also empirically test the algorithm and
compare the performance with Koopman-based methods and iLQR methods on the
pendulum swingup problem
Modeling plant diseases under climate change: evolutionary perspectives
Infectious plant diseases are a major threat to global agricultural productivity, economic development, and ecological integrity. There is widespread concern that these social and natural disasters caused by infectious plant diseases may escalate with climate change and computer modeling offers a unique opportu-nity to address this concern. Here, we analyze the intrinsic problems associated with current modeling strategies and highlight the need to integrate evolutionary principles into polytrophic, eco-evolutionary frameworks to improve predictions. We particularly discuss how evolutionary shifts in functional trade-offs, relative adaptability between plants and pathogens, ecosystems, and climate preferences induced by climate change may feedback to future plant disease epidemics and how technological advances can facilitate the generation and integration of this relevant knowledge for better modeling predictions
Robust Integral of Sign of Error and Neural Network Control for Servo System with Continuous Friction
Robust Integral of Sign of Error and Neural Network Control for Servo System with Continuous Friction
Antenna Array Design in MIMO Radar Using NSK Polynomial Factorization Algorithm
The work presented here is concerned with the antenna array design in collocated multiple-input multiple-output (MIMO) radars. After knowing the system requirements, the antenna array design problem is formulated as a standard polynomial factorization. In addition, an algorithm based on Newton-Schubert-Kronecker (NSK) polynomial factorization is proposed. The algorithm contains three steps. First, linear factors are extracted by extended Vieta theorem. Then, undermined high-order factors are confirmed with Newton interpolation and certain high-order factors should be searched for within the undermined ones. Finally, the antenna array configurations are determined according to the result of polynomial factorization. Simulations confirm the wide use of the proposed algorithm in MIMO radar antenna array design
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