scholarly journals Layered Feedback Control Overcomes Performance Trade-off in Synthetic Biomolecular Networks

2021 ◽  
Author(s):  
Chelsea Y. Hu ◽  
Richard M Murray
Keyword(s):  
Feedback Control ◽  
Control Theory ◽  
Biological Networks ◽  
Performance Optimization ◽  
System Analysis ◽  
Mechanistic Model ◽  
Optimization Strategy ◽  
Trade Off ◽  
Environmental Perturbations ◽  
Biomolecular Networks

Layered feedback is an optimization strategy in feedback control designs widely used in electrical and mechanical engineering. Layered control theory suggests that the performance of controllers is bound by the universal robustness-efficiency trade-off limit, which could be overcome by layering two or more feedbacks together. In natural biological networks, genes are often regulated with redundancy and layering to adapt to environmental perturbations. Control theory hypothesizes that this layering architecture is also adopted by nature to overcome this performance trade-off. In this work, we validated this property of layered control with a synthetic network in living E. coli cells. We performed system analysis on a node-based design to confirm the trade-off properties before proceeding to simulations with an effective mechanistic model, which guided us to the best performing design to engineer in cells. Finally, we interrogated its system dynamics experimentally with eight sets of perturbations on chemical signals, nutrient abundance, and growth temperature. For all cases, we consistently observed that the layered control overcomes the robustness-efficiency trade-off limit. This work experimentally confirmed that layered control could be adopted in synthetic biomolecular networks as a performance optimization strategy. It also provided insights in understanding genetic feedback control architectures in nature.

scholarly journals Layered Feedback Control Overcomes Performance Trade-off in Synthetic Biomolecular Networks

2021 ◽  
Author(s):  
Chelsea Hu ◽  
Richard Murray
Keyword(s):  
Feedback Control ◽  
Control Theory ◽  
Biological Networks ◽  
Performance Optimization ◽  
System Analysis ◽  
Mechanistic Model ◽  
Optimization Strategy ◽  
Trade Off ◽  
Environmental Perturbations ◽  
Biomolecular Networks

Abstract Layered feedback is an optimization strategy in feedback control designs widely used in electrical and mechanical engineering. Layered control theory suggests that the performance of controllers is bound by the universal robustness-efficiency trade-off limit, which could be overcome by layering two or more feedbacks together. In natural biological networks, genes are often regulated with redundancy and layering to adapt to environmental perturbations. Control theory hypothesizes that this layering architecture is also adopted by nature to overcome this performance trade-off. In this work, we validated this property of layered control with a synthetic network in living E. coli cells. We performed system analysis on a node-based design to confirm the trade-off properties before proceeding to simulations with an effective mechanistic model, which guided us to the best performing design to engineer in cells. Finally, we interrogated its system dynamics experimentally with eight sets of perturbations on chemical signals, nutrient abundance, and growth temperature. For all cases, we consistently observed that the layered control overcomes the robustness-efficiency trade-off limit. This work experimentally confirmed that layered control could be adopted in synthetic biomolecular networks as a performance optimization strategy. It also provided insights in understanding genetic feedback control architectures in nature.

Performance Optimization Strategy for Quantum Satellite Communication Based on Entanglement Feedback Control

2021 ◽  
Vol 58 (5) ◽  
pp. 0527002-527002271
Author(s):  
聂敏 Nie Min ◽  
张彦朋 Zhang Yanpeng ◽  
杨光 Yang Guang ◽  
张美玲 Zhang Meiling ◽  
裴昌幸 Pei Changxing
Keyword(s):  
Feedback Control ◽  
Performance Optimization ◽  
Satellite Communication ◽  
Optimization Strategy

scholarly journals Probability-based mechanisms in biological networks with parameter uncertainty

2021 ◽  
Author(s):  
Oscar O. Ortega ◽  
Blake A. Wilson ◽  
James C. Pino ◽  
Michael W. Irvin ◽  
Geena V. Ildefonso ◽  
...  
Keyword(s):  
Biological Networks ◽  
Parameter Uncertainty ◽  
Mechanistic Model ◽  
Dynamic Network ◽  
Parameter Inference ◽  
Signal Flow ◽  
Cellular Processes ◽  
Biomolecular Networks ◽  
Extrinsic Apoptosis ◽  
Modulate Signal

AbstractMathematical models of biomolecular networks are commonly used to study mechanisms of cellular processes, but their usefulness is often questioned due to parameter uncertainty. Here, we employ Bayesian parameter inference and dynamic network analysis to study dominant reaction fluxes in models of extrinsic apoptosis. Although a simplified model yields thousands of parameter vectors with equally good fits to data, execution modes based on reaction fluxes clusters to three dominant execution modes. A larger model with increased parameter uncertainty shows that signal flow is constrained to eleven execution modes that use 53 out of 2067 possible signal subnetworks. Each execution mode exhibits different behaviors to in silico perturbations, due to different signal execution mechanisms. Machine learning identifies informative parameters to guide experimental validation. Our work introduces a probability-based paradigm of signaling mechanisms, highlights systems-level interactions that modulate signal flow, and provides a methodology to understand mechanistic model predictions with uncertain parameters.

Robust Two-degree-of-freedom Control Based on H∞ Method for PMSM Drive System

2020 ◽  
Vol 13 (4) ◽  
pp. 496-506
Author(s):  
Qixin Zhu ◽  
Lei Xiong ◽  
Hongli Liu ◽  
Yonghong Zhu ◽  
Guoping Zhang
Keyword(s):  
Control System ◽  
Control Theory ◽  
Position Control ◽  
Dynamic Performance ◽  
Servo System ◽  
Degree Of Freedom ◽  
Trade Off ◽  
Standard Design ◽  
Position Controller ◽  
Two Degree Of Freedom

Background: The conventional method using one-degree-of-freedom (1DOF) controller for Permanent Magnet Synchronous Motor (PMSM) servo system has the trade-off problem between the dynamic performance and the robustness. Methods: In this paper, by using H∞ control theory, a novel robust two-degree-of-freedom (2DOF) controller has been proposed to improve the position control performance of PMSM servo system. Using robust control theory and 2DOF control theory, a H∞ robust position controller has been designed and discussed in detail. Results: The trade-off problem between the dynamic performance and robustness which exists in one-degree-of-freedom (1DOF) control can be dealt with by the application of 2DOF control theory. Then, through H∞ control theory, the design of robust position controller can be translated to H∞ robust standard design problem. Moreover, the control system with robust controller has been proved to be stable. Conclusion: Further simulation results demonstrate that compared with the conventional PID control, the designed control system has better robustness and attenuation to the disturbance of load impact.

Tips on Stochastic Optimal Feedback Control and Bayesian Spatiotemporal Models: Applications to Robotics

2014 ◽  
Vol 137 (3) ◽  
Author(s):  
Jongeun Choi ◽  
Dejan Milutinović
Keyword(s):  
Sensor Networks ◽  
Feedback Control ◽  
Control Theory ◽  
Mobile Sensor Networks ◽  
Optimal Feedback Control ◽  
Mobile Sensor ◽  
Optimal Feedback ◽  
Spatiotemporal Models ◽  
Feedback Control Theory

This tutorial paper presents the expositions of stochastic optimal feedback control theory and Bayesian spatiotemporal models in the context of robotics applications. The presented material is self-contained so that readers can grasp the most important concepts and acquire knowledge needed to jump-start their research. To facilitate this, we provide a series of educational examples from robotics and mobile sensor networks.

Implications of Optimal Feedback Control Theory for Sport Coaching and Motor Learning: A Systematic Review

Motor Control ◽  
2021 ◽  
pp. 1-24
Author(s):  
Steven van Andel ◽  
Robin Pieper ◽  
Inge Werner ◽  
Felix Wachholz ◽  
Maurice Mohr ◽  
...  
Keyword(s):  
Feedback Control ◽  
Motor Learning ◽  
Control Theory ◽  
Skill Acquisition ◽  
Best Practice ◽  
Optimal Feedback Control ◽  
Optimal Feedback ◽  
Different Types ◽  
Novel Theory ◽  
Feedback Control Theory

Best practice in skill acquisition has been informed by motor control theories. The main aim of this study is to screen existing literature on a relatively novel theory, Optimal Feedback Control Theory (OFCT), and to assess how OFCT concepts can be applied in sports and motor learning research. Based on 51 included studies with on average a high methodological quality, we found that different types of training seem to appeal to different control processes within OFCT. The minimum intervention principle (founded in OFCT) was used in many of the reviewed studies, and further investigation might lead to further improvements in sport skill acquisition. However, considering the homogenous nature of the tasks included in the reviewed studies, these ideas and their generalizability should be tested in future studies.

scholarly journals Smes’ Performance Evaluation and Optimization Based on DEA and CFI

2013 ◽  
Vol 4 (1) ◽  
pp. 57-64 ◽  
Author(s):  
Zhao Shi ◽  
Josu Takala ◽  
Matti Muhos ◽  
Jyrki Poikkimaki ◽  
Yang Chen
Keyword(s):  
Performance Assessment ◽  
Performance Optimization ◽  
Dynamic Environment ◽  
Critical Factor ◽  
Assessment Method ◽  
Enterprise Performance ◽  
Optimization Strategy ◽  
Real World Data ◽  
Core Content ◽  
Technological Effectiveness

Abstract It is a core content of enterprise performance research evaluating and comparing enterprise performance in dynamic environment. In allusion to this problem, a variety of enterprise performance assessment methods and indexes systems are proposed. Data envelopment analysis (DEA) is a kind of effective mathematical model which is used for comparing the performance among enterprises or different units inside an enterprise, based on the real-world data. Through comparing the performance, DEA can evaluate the enterprise performance from scale effectiveness and technological effectiveness, and then get the performance optimization goals. Critical Factor Index (CFI) is a new enterprise performance assessment method proposed in recent years. This method, based on the performance perception of business leaders or staffs, evaluates the enterprise performance in different dimensions, and then gets the optimization strategy of enterprise resource allocation to improve integrated enterprise performance. This paper has structured a new evaluation and optimization system for performance of small and medium-sized enterprises (SMEs), which combine properly the DEA and CFI method to evaluate and optimize the SMEs’ performance comprehensively, and has confirm this system with data of 5 Finnish SMEs.

Wastewater modification processes assessment in a stabilization reservoir

2008 ◽  
Vol 57 (7) ◽  
pp. 1037-1045 ◽  
Author(s):  
G. Mannina ◽  
G. Mancini ◽  
M. Torregrossa ◽  
G. Viviani
Keyword(s):  
Organic Matter ◽  
Performance Optimization ◽  
Flow Reactor ◽  
Batch Reactor ◽  
Mechanistic Model ◽  
Resource Planning ◽  
Treated Wastewater ◽  
State Variables ◽  
Factors Affecting ◽  
Effluent Quality

A semi-empirical mechanistic model able to simulate the dynamics of a stabilization reservoir was developed incorporating both settling of particulate components and chemical/biological processes. Several factors affecting the reservoir effluent quality were taken into account: hydraulics and hydrology, solar radiation, atmospheric reaeration, algae, zooplankton, organic matter, pathogen bacteria, and sediment-water interaction. The model quantifies the specific influence of each factor on effluent quality, evaluating the correlation between the different considered factors. State variables included in the model were: algae, dissolved oxygen, organic matter, zooplankton and indicator bacteria. The model was transferred into a computational code in order to provide a useful and versatile tool for water resource planning management issues. The model was verified by comparing simulated results with full-scale data collected from a small reservoir (Sicily, IT) filled with partially treated wastewater. The reservoir has a volume of 11,000 m3, a maximum depth of 6.3 m and a mean depth of about 5 m. The monitoring period lasted four months during which the reservoir operated in different hydraulics conditions: as a standard batch reactor and as a continuous flow reactor. The model was able to reproduce the behaviour of the principal simulated parameters thus representing a potential tool for the management and performance optimization of these peculiar storage/treatment systems.

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