scholarly journals Programmable pattern formation in cellular systems with local signaling

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Tiago Ramalho ◽  
Stephan Kremser ◽  
Hao Wu ◽  
Ulrich Gerland
Keyword(s):  
Pattern Formation ◽  
Cellular Systems ◽  
Target Pattern ◽  
Discrete State ◽  
Alternative Scheme ◽  
Control Schemes ◽  
Local Signaling ◽  
Update Rules ◽  
And Control ◽  
Logical Rules

AbstractComplex systems, ranging from developing embryos to systems of locally communicating agents, display an apparent capability of “programmable” pattern formation: They reproducibly form target patterns, but those targets can be readily changed. A distinguishing feature of such systems is that their subunits are capable of information processing. Here, we explore schemes for programmable pattern formation within a theoretical framework, in which subunits process local signals to update their discrete state following logical rules. We study systems with different update rules, topologies, and control schemes, assessing their capability of programmable pattern formation and their susceptibility to errors. Only a fraction permits local organizers to dictate any target pattern, by transcribing temporal patterns into spatial patterns, reminiscent of the principle underlying vertebrate somitogenesis. An alternative scheme employing variable rules cannot reach all patterns but is insensitive to the timing of organizer inputs. Our results establish a basis for designing synthetic systems and models of programmable pattern formation closer to real systems.

scholarly journals Programmable pattern formation in cellular systems with local signaling

2021 ◽  
Author(s):  
Tiago Ramalho ◽  
Stephan Kremser ◽  
Hao Wu ◽  
Ulrich Gerland
Keyword(s):  
Pattern Formation ◽  
Internal State ◽  
Cellular Systems ◽  
Small Subset ◽  
Target Pattern ◽  
Alternative Scheme ◽  
Local Organizer ◽  
Common Principle ◽  
Pattern Forming ◽  
Update Rules

Diverse complex systems, ranging from developing embryos to systems of locally communicating agents, display an apparent capability of "programmable" pattern formation: They reproducibly form a target pattern, but this target can be readily changed. A distinguishing feature of such systems, as compared to simpler physical pattern forming systems, is that their subunits are capable of information processing. Here, we explore schemes for programmable pattern formation within a theoretical framework, in which subunits process discrete local signals to update their internal state according to logical rules. We study systems with different update rules, different topologies, and different control schemes, to assess their ability to perform programmable pattern formation and their susceptibility to errors. Only a small subset of systems permits local organizer cells to dictate any target pattern. These systems follow a common principle, whereby a temporal pattern is transcribed into a spatial pattern, reminiscent of the clock-and-wavefront mechanism underlying vertebrate somitogenesis. An alternative scheme employing several different rules can only form a fraction of patterns but is robust with respect to the timing of organizer cell inputs. Our results establish a basis for the design of synthetic systems, and for more detailed models of programmable pattern formation closer to real systems.

Biohybrid robots are synthetic biology systems

2018 ◽  
Author(s):  
Joseph Ayers
Keyword(s):  
Nervous System ◽  
Synthetic Biology ◽  
Behavioral Control ◽  
Eukaryotic Cells ◽  
Chemical Senses ◽  
Biomimetic Robots ◽  
Control Schemes ◽  
Induced Pluripotent ◽  
And Control ◽  
And Robotics

This chapter describes how synthetic biology and organic electronics can integrate neurobiology and robotics to form a basis for biohybrid robots and synthetic neuroethology. Biomimetic robots capture the performance advantages of animal models by mimicking the behavioral control schemes evolved in nature, based on modularized devices that capture the biomechanics and control principles of the nervous system. However, current robots are blind to chemical senses, difficult to miniaturize, and require chemical batteries. These obstacles can be overcome by integration of living engineered cells. Synthetic biology seeks to build devices and systems from fungible gene parts (gene systems coding different proteins) integrated into a chassis (induced pluripotent eukaryotic cells, yeast, or bacteria) to produce devices with properties not found in nature. Biohybrid robots are examples of such systems (interacting sets of devices). A nascent literature describes genes that can mediate organ levels of organization. Such capabilities, applied to biohybrid systems, portend truly biological robots guided, controlled, and actuated solely by life processes.

scholarly journals Vertical Jumping for Legged Robot Based on Quadratic Programming

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3679
Author(s):  
Dingkui Tian ◽  
Junyao Gao ◽  
Xuanyang Shi ◽  
Yizhou Lu ◽  
Chuzhao Liu
Keyword(s):  
Quadratic Programming ◽  
Stance Phase ◽  
Center Of Mass ◽  
Maximum Error ◽  
Legged Robot ◽  
Flight Phase ◽  
Programming Framework ◽  
Vertical Jumping ◽  
Control Schemes ◽  
And Control

The highly dynamic legged jumping motion is a challenging research topic because of the lack of established control schemes that handle over-constrained control objectives well in the stance phase, which are coupled and affect each other, and control robot’s posture in the flight phase, in which the robot is underactuated owing to the foot leaving the ground. This paper introduces an approach of realizing the cyclic vertical jumping motion of a planar simplified legged robot that formulates the jump problem within a quadratic-programming (QP)-based framework. Unlike prior works, which have added different weights in front of control tasks to express the relative hierarchy of tasks, in our framework, the hierarchical quadratic programming (HQP) control strategy is used to guarantee the strict prioritization of the center of mass (CoM) in the stance phase while split dynamic equations are incorporated into the unified quadratic-programming framework to restrict the robot’s posture to be near a desired constant value in the flight phase. The controller is tested in two simulation environments with and without the flight phase controller, the results validate the flight phase controller, with the HQP controller having a maximum error of the CoM in the x direction and y direction of 0.47 and 0.82 cm and thus enabling the strict prioritization of the CoM.

scholarly journals On the generation of validated manufacturing process optimization and control schemes

Procedia CIRP ◽  
2021 ◽  
Vol 96 ◽  
pp. 57-62
Author(s):  
Alexios Papacharalampopoulos ◽  
Harry Bikas ◽  
Christos Michail ◽  
Panagiotis Stavropoulos
Keyword(s):  
Process Optimization ◽  
Manufacturing Process ◽  
Control Schemes ◽  
Optimization And Control ◽  
And Control

Design, Construction and Control of a Remotely Operated Vehicle (ROV)

2011 ◽  
Author(s):  
Alireza Marzbanrad ◽  
Jalil Sharafi ◽  
Mohammad Eghtesad ◽  
Reza Kamali
Keyword(s):  
High Speed ◽  
Degrees Of Freedom ◽  
Remotely Operated Vehicle ◽  
Six Degrees Of Freedom ◽  
Depth Sensors ◽  
Control Schemes ◽  
On Line ◽  
Operation Unit ◽  
And Control ◽  
Design Construction

This is report of design, construction and control of “Ariana-I”, an Underwater Remotely Operated Vehicle (ROV), built in Shiraz University Robotic Lab. This ROV is equipped with roll, pitch, heading, and depth sensors which provide sufficient feedback signals to give the system six degrees-of-freedom actuation. Although its center of gravity and center of buoyancy are positioned in such a way that Ariana-I ROV is self-stabilized, but the combinations of sensors and speed controlled drivers provide more stability of the system without the operator involvement. Video vision is provided for the system with Ethernet link to the operation unit. Control commands and sensor feedbacks are transferred on RS485 bus; video signal, water leakage alarm, and battery charging wires are provided on the same multi-core cable. While simple PI controllers would improve the pitch and roll stability of the system, various control schemes can be applied for heading to track different paths. The net weight of ROV out of water is about 130kg with frame dimensions of 130×100×65cm. Ariana-I ROV is designed such that it is possible to be equipped with different tools such as mechanical arms, thanks to microprocessor based control system provided with two directional high speed communication cables for on line vision and operation unit.

Target Pattern Formation in a Chaotic Medium

1991 ◽  
Vol 60 (8) ◽  
pp. 2485-2488 ◽  
Author(s):  
Hiroyuki Nagashima
Keyword(s):  
Pattern Formation ◽  
Target Pattern

scholarly journals Control-Influenced Layout Optimization of Arrays of Wave Energy Converters

2014 ◽  
Author(s):  
Philip Balitsky ◽  
Giorgio Bacelli ◽  
John V. Ringwood
Keyword(s):  
Wave Climate ◽  
Radiative Properties ◽  
Optimal Layout ◽  
Wave Direction ◽  
Sea State ◽  
Wave Energy Converters ◽  
Control Schemes ◽  
And Control ◽  
Tuning Scheme ◽  
Optimal Configurations

In this paper we compare the optimal configurations for an array of WECs given two control schemes, a real-time global control and a passive sea-state based tuning scheme. In a particular wave climate and array orientation with its axis normal to the prevailing wave direction, closely-spaced symmetrical arrays of 2, 3, 4, 5, and 6 cylinders of different radiative properties are simulated for varying inter-device separation distances. For each device and control type, we focus on the factors that influence the optimal layout, including number of devices, separating distance and angular spreading. The average annual power output is calculated for each optimal configuration.

Modelling and control of greenhouse system using neural networks

2016 ◽  
Vol 40 (3) ◽  
pp. 918-929 ◽  
Author(s):  
A Manonmani ◽  
T Thyagarajan ◽  
M Elango ◽  
S Sutha
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Moving Average ◽  
Growth Conditions ◽  
Operating Conditions ◽  
Adverse Weather ◽  
Control Schemes ◽  
Non Linear ◽  
Auto Regressive ◽  
And Control

A greenhouse system (GHS) is a closed structure that facilitates modified growth conditions to crops and provides protection from pests, diseases and adverse weather. However, a GHS exhibits non-linearity due to the interaction between the biological subsystem and the physical subsystem. Non-linear systems are difficult to control, particularly when their characteristics change with time. These systems are best handled with methods of computation intelligence, such as artificial neural networks (ANNs) and fuzzy systems. In the present work, the approximation capability of a neural network is used to model and control sufficient growth conditions of a GHS. An optimal neural network-based non-linear auto regressive with exogenous input (NARX) time series model is developed for a GHS. Based on the NARX model, two intelligent control schemes, namely a neural predictive controller (NPC) and non-linear auto regressive moving average (NARMA-L2) controller are proposed to achieve the desired growth conditions such as humidity and temperature for a better yield. Finally, closed-loop performances of the above two control schemes for servo and regulatory operations are analysed for various operating conditions using performance indices.

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