Quasi-static Optimal Control Strategy of Lattice Boom Crane Based on Large-Scale Flexible Non-linear Dynamics

Gao, Lingchong; Dai, Xiaobing; Kleeberger, Michael; Fottner, Johannes

doi:10.1007/978-3-031-23149-0_9

Cited by 5 publications

(3 citation statements)

References 4 publications

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“…In addition, numerical approaches that are commonly used for solving optimal control problems in systems biology [36,39] were not adequate in our study, as we sought optimal solutions defined over a range of unknown parameters. By taking advantage of the relative simplicity of our model and using a steady-state approximation similar to other studies in mechanics [27,40], we could explicitly solve the problem analytically and find optimal solutions that depend on the control and input parameters. To our knowledge, our study is hence the first to investigate the dynamics of ribosomes as solutions of an optimal control problem that includes ribophagy.…”

Section: Discussionmentioning

confidence: 99%

Optimal control of ribosome population for gene expression under periodic nutrient intake

Soubrier,

Foxall,

Ciandrini

et al. 2024

J. R. Soc. Interface.

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Translation of proteins is a fundamental part of gene expression that is mediated by ribosomes. As ribosomes significantly contribute to both cellular mass and energy consumption, achieving efficient management of the ribosome population is also crucial to metabolism and growth. Inspired by biological evidence for nutrient-dependent mechanisms that control both ribosome-active degradation and genesis, we introduce a dynamical model of protein production, that includes the dynamics of resources and control over the ribosome population. Under the hypothesis that active degradation and biogenesis are optimal for maximizing and maintaining protein production, we aim to qualitatively reproduce empirical observations of the ribosome population dynamics. Upon formulating the associated optimization problem, we first analytically study the stability and global behaviour of solutions under constant resource input, and characterize the extent of oscillations and convergence rate to a global equilibrium. We further use these results to simplify and solve the problem under a quasi-static approximation. Using biophysical parameter values, we find that optimal control solutions lead to both control mechanisms and the ribosome population switching between periods of feeding and fasting, suggesting that the intense regulation of ribosome population observed in experiments allows to maximize and maintain protein production. Finally, we find some range for the control values over which such a regime can be observed, depending on the intensity of fasting.

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Section: Discussionmentioning

confidence: 99%

Optimal control of ribosome population for gene expression under periodic nutrient intake

Soubrier,

Foxall,

Ciandrini

et al. 2024

J. R. Soc. Interface.

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show abstract

“…Although many studies concentrate on linear agent dynamics, the application of control methods developed for linear systems is inadequate for intricate physical systems. In this paper, we delve into Euler-Lagrange MASs, which serve as a modeling framework for diverse physical systems like robotic manipulators [2] and underwater vehicles [3].…”

Section: Introductionmentioning

confidence: 99%

“…1 Robert Koch Institute, Berlin, Germany. 2 Technical University of Munich, Munich, Germany. 3 The University of Texas at Austin, Austin, TX, USA.…”

Section: Introductionmentioning

confidence: 99%

Distributed Learning Consensus Control for Unknown Nonlinear Multi-Agent Systems based on Gaussian Processes

Yang

Sosnowski

Liu

et al. 2021

2021 60th IEEE Conference on Decision and Control (CDC)

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This work presents an innovative learning-based approach to tackle the tracking control problem of Euler-Lagrange multi-agent systems with partially unknown dynamics operating under switching communication topologies. The approach leverages a correlation-aware cooperative algorithm framework built upon Gaussian process regression, which adeptly captures inter-agent correlations for uncertainty predictions. A standout feature is its exceptional efficiency in deriving the aggregation weights achieved by circumventing the computationally intensive posterior variance calculations. Through Lyapunov stability analysis, the distributed control law ensures bounded tracking errors with high probability. Simulation experiments validate the protocol's efficacy in effectively managing complex scenarios, establishing it as a promising solution for robust tracking control in multi-agent systems characterized by uncertain dynamics and dynamic communication structures.

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Learning-based Control for PMSM Using Distributed Gaussian Processes with Optimal Aggregation Strategy

Yin,

Dai,

Yang

et al. 2023

IECON 2023- 49th Annual Conference of the IEEE Industrial Electronics Society

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Quasi-static Optimal Control Strategy of Lattice Boom Crane Based on Large-Scale Flexible Non-linear Dynamics

Cited by 5 publications

References 4 publications

Optimal control of ribosome population for gene expression under periodic nutrient intake

Optimal control of ribosome population for gene expression under periodic nutrient intake

Distributed Learning Consensus Control for Unknown Nonlinear Multi-Agent Systems based on Gaussian Processes

Learning-based Control for PMSM Using Distributed Gaussian Processes with Optimal Aggregation Strategy

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