Self-organizing Control Research Articles

Cancer Epithelial Cells Participate in the Self-Organization of Lung Tumor Spheroids: A Morphological Approach.

The tumor microenvironment is known to play an important role in tumor progression. However, the specific mechanisms underlying this process are still not known in detail and more research is needed on the elements that control tumor progression in lung cancer. In this work, we aimed to investigate the involvement of epithelial and stromal cancer cells in growth, cell migration, and epithelial-to-mesenchymal transition (EMT) in a 3D in vitro model consisting of cell spheroids cultured in a type I collagen scaffold. Spheroids were manufactured using different combinations of epithelial cells, particularly H460 and H1792 cell lines, with cancer-associated fibroblasts and normal fibroblasts, both isolated from adenocarcinoma patients. We evaluated the morphology of the spheroids by analysis of F-actin and pankeratin with confocal microscopy. We determined the ultrastructure of cells in the spheroids by transmission electron microscopy and the expression of CDH1, CDH2, and VIM by RT-PCR. We observed that, on the one hand, the type of epithelial cell influences the morphology of spheroids. Stromal cells stimulated spheroid growth and cell dissemination through the collagen matrix, either alone or organized in branches with a nucleus of epithelial cells preceded by fibroblast cells. They also induced the appearance of new cell groups in the scaffold and the presence of EMT markers. The results presented here indicate the participation of both epithelial and stromal cells in the control of spheroid self-organization. The experimental model proposed here, although preliminary, is useful for the study of some aspects related to tumor progression in lung cancer.

Predictor-Based Self-Organizing Control for Unknown Nonlinear Dynamical Systems

Predictor-Based Self-Organizing Control for Unknown Nonlinear Dynamical Systems

Intelligent Robust Control of Autonomous Robot: Quantum Self-Organization of Imperfect Knowledge Bases—Experiment

The article discusses the information technology of a robust intelligent control system design based on quantum fuzzy inference. The application of the developed design methodology is based on the quantum self-organization of fuzzy controller’s imperfect knowledge bases and leads to an increase in the robustness of intelligent control systems in unpredicted situations. The results of mathematical modeling and physical experiment are compared using the example of an autonomous robot in the form of a “cart – pole” system. Experimental confirmation of the synergetic effect existence in the robust self-organized fuzzy controller formation from a finite number of non-robust fuzzy controllers in on-line has been demonstrated. The resulting effect is based on the existence of hidden quantum information extracted from the classical states of the controller’s time-varying gain coefficients processes schedule. The derived law of quantum information thermodynamics establishes the possibility to forming a thermodynamic control force due to the extracted amount of hidden quantum information and performing additional useful work, that guarantees the achievement of the control goal based on increasing the robustness of a self-organized quantum controller. At the same time, the amount of useful work performed by the control object (at the macro level) exceeds the amount of work spent (at the micro level) by a quantum self-organized controller to extract the quantum information hidden in the responses of imperfect knowledge bases without violating the second thermodynamics information law for open quantum systems with information exchange of entangled super correlated states. A concrete example of an autonomous robot is given, demonstrating the existence of a quantum self-organization synergetic effect to imperfect knowledge bases.

Моделирование процессов социальной самоорганизации интеллектуальных агентов

The relevance of the task of modeling human behavior posed in the article is associated with the need for active development of the economy, ensuring the involvement of an increasing proportion of the young population with higher and secondary specialized education. As a result of state incentives for business, more and more people are ready to receive higher education or continue to study at the next levels. When studying the processes of managing the social behavior of subjects, the “system of systems” (SoS) approach was used, within which various methods of modeling, simulation and analysis are developed and studied. The simulation is based on the multi-agent principle, the mechanism of which allows analyzing the behavior of the self-organization of an intelligent agent (social subject) and, if necessary, changing its behavior. The ideal approach for implementing the modeling of a multi-agent system (MAS) of the behavior of an agent is the evolutionary simulation methodology (ESM), which allows developing a set of interconnected optimization simulation models that implements the property of self-organization of intelligent agents. The basic principles of self-organization control are formulated, which is based on the theory of equilibrium random processes (RSP), which provides the possibility of influencing any available parameters and in any combination of them using ESM. The ways of adapting ESM in relation to modeling the process of social self-organization of a young person are considered, and a graphical interpretation of social development is proposed, two parameters are used as phase coordinates: social status and income level. Models of indirect methods of management, choice of the direction of development of a person’s career growth in the production management system based on dynamic programming have been developed. Examples of ESM implementation are given.

Distributed Self-Organizing Control of CAVs Between Multiple Adjacent-Ramps

Traffic self-organizing is controlled autonomously by rules that rely on adaptation to local variations in traffic state and enable effective coordination of the vehicular traffic at a network level. Combined self-organizing network with intelligent transportation, we proposed a distributed self-organizing method for Connected and Autonomous Vehicles (CAVs), which aimed to improve the efficiency and safety between Multiple Adjacent-Ramps (multi-ARs). To make the mainline formation more stable, the speed of ramp vehicles was adjusted to ensure suitable speed and headway of the mainline formation. In the test, the multi-ARs in the East Ring Interchange on the Inner Ring Express in Chongqing was selected to collect the initial data sample by the drones and fixed-point cameras. Under the respective scenarios of conventional driving and intelligent networks, the Python, SUMO, and TraCI were adopted to run simulations and validate the proposed model. Results showed that our model could keep Time to Conflict (TTC) above 1.4s, reduce the average delay by 34.22%, reduce the lane-changing times by 28.07%, reduce single lane occupancy to 8% and improve average speed by 3.68% of multi-ARs. To verify the applicability of the proposed model, experiments were carried out under different traffic volumes, demonstrating the relevance of the proposed method for medium-to-high-density traffic flows. It can provide a basis for traffic engineers and policymakers to maintain the stable development of the urban expressways and ensure the overall operation quality of the multi-ARs.

A rationally designed optochemogenetic switch for activating canonical Wnt signaling

Accurate spatiotemporal control of multicellular self-organization by various signaling pathways is essential for developmental stages. In particular, evolutionarily conserved Wnt signaling serves as a major morphogenetic switch to determine the anteroposterior axis of the embryo. Here, we developed a genetically encoded optochemogenetic Wnt switch, named optochemoWnt, by coupling a blue light-inducible CRY2olig and rapamycin-inducible LRP6c clustering. The rationally designed optochemoWnt successfully modulated Wnt signaling with AND-gated patterns and demonstrated an improved signal-to-noise ratio (SNR). The dual-triggered switch provides a safeguard to prevent signal leakage resulting from ambient light sources under general laboratory conditions. OptochemoWnt expands the molecular toolbox available for the fields of developmental biology and tissue engineering. In addition, the AND-gated strategy of optochemoWnt may be used for other biomedical applications that integrate user defined switch elements with Boolean logic gates.

Self-Organizing Control Systems for Nonlinear Spacecraft in the Class of Structurally Stable Mappings

Self-Organizing Control Systems for Nonlinear Spacecraft in the Class of Structurally Stable Mappings

Autonomous Tracking of ShenZhou Reentry Capsules Based on Heterogeneous UAV Swarms

The safe landing and rapid recovery of the reentry capsules are very important to manned spacecraft missions. A variety of uncertain factors, such as flight control accuracy and wind speed, lead to a low orbit prediction accuracy and a large landing range of reentry capsules. It is necessary to realize the autonomous tracking and continuous video observation of the reentry capsule during the low-altitude phase. Aiming at the Shenzhou return capsule landing mission, the paper proposes a new approach for the autonomous tracking of Shenzhou reentry capsules based on video detection and heterogeneous UAV swarms. A multi-scale video target detection algorithm based on deep learning is developed to recognize the reentry capsules and obtain positioning data. A self-organizing control method based on virtual potential field is proposed to realize the cooperative flight of UAV swarms. A hardware-in-the-loop simulation system is established to verify the method. The results show that the reentry capsule can be detected in four different states, and the detection accuracy rate of the capsule with parachute is 99.5%. The UAV swarm effectively achieved autonomous tracking for the Shenzhou reentry capsule based on the position obtained by video detection. This is of great significance in the real-time searching of reentry capsules and the guaranteeing of astronauts’ safety.

Coscheduling Spatial Self-organization and Distributed Data Collection in Multi-agent System

The problem of coscheduling spatial self-organization control processes and distributed data collection processes in a multi-agent system has been considered. The goal of coscheduling is to find and use the possibilities of functional coordination of these processes and increase the efficiency of the multi-agent system due to their parallel execution. An analysis of the main features of spatial self-organization tasks that affect the solution of the problem of coscheduling has been carried out. Variants of the mobile agent robotic platform configuration and the problem of the dependence of spatial self-organization algorithms on the type of robotic platform have been considered. A method of coscheduling of spatial self-organization and distributed data collection by coordinated parallel execution of the corresponding data collection process and the process of controlling mobile agent motion has been proposed. The method of coscheduling is implemented using the interaction protocol of these processes and the algorithm for planning their parallel execution using functional decomposition. The simulation results of the proposed method of coscheduling are given. It is proved that the proposed method of coscheduling provides acceleration of computations in the decision-making module of the mobile agent due to more efficient parallelization. On average, for typical values of parameters of control processes, the proposed method of coscheduling provides acceleration of computations in the decision-making module of the mobile agent by 40.6 %.

Control of self-organization of drop-casted Nafion film for improving proton conduction in a polymer-electrolyte-membrane fuel cell to raise its output power density

A simple drop-cast method to directly deposit Nafion polymer electrolyte membrane (PEM) on nanostructured thin-film catalyst layer composed of stacked Pt nanoparticles prepared by pulsed laser deposition (PLD) was demonstrated. Through optimization of solvent composition and drying temperature of Nafion solution to control self-organization of Nafion, a uniform PEM with better bulk and interface microstructures could be produced, leading to a significant improvement in the output current density of a PEM fuel cell over that using reference commercial PEMs. The formation of facile proton conduction pathways in the bulk Nafion membrane resulted in a 35% reduction in ohmic resistance compared to that with the commercial membrane. Moreover, the infiltration of Nafion in the catalyst layer formed suitable proton transport network to render more catalyst nanoparticles effective and thus lower charge-transfer resistance. With the optimized PLD, drop-cast, and hot-pressing conditions, the current density of PEMFCs using drop-casted PEM reached 1902 mA cm−2 at 0.6 V at 2 atm H2 and O2 pressures with a cathode Pt loading of 100 μg cm−2, corresponding to a power density of 1.14 W cm−2 and a cathode mass-specific power density of 11.4 kW g−1.

Self-Organizing Control of Mega Constellations for Continuous Earth Observation

This work presents a novel self-organizing control method for mega constellations to meet the continuous Earth observation requirements. In order to decrease the TT&C pressure caused by numerous satellites, constellation satellites are not controlled according to the designed configurations but are controlled with respect to intersatellite constraints. By analyzing the street-of-coverage (SOC) of coplanar constellation satellites, the continuous coverage constraint of the mega constellation is transformed into constraints of the right ascension of ascending node (RAAN) and relative motion bound between every two adjacent coplanar satellites. The proposed continuous coverage constraint can be satisfied by most ongoing or planned mega constellations. Artificial potential functions (APFs) are used to realize self-organizing control. The scale-independent relative orbital elements (SIROEs) are innovatively presented as the self-organizing control variables. Using the Gaussian equations and Lyapunov’s theory, the stability of the APF control in quadratic form is proven, from which it can be concluded that the APF control variables of the controlled satellite should have the same time derivative as the target satellite states under two-body Keplerian motion condition, and SIROEs are ideal choices. The proposed controllers and self-organizing rules are verified in the sub-constellation of the GW-2 mega constellation by simulation. The results demonstrate the goodness in control effect and ground coverage performance.

Robust Reference Signal Self-Organizing Control based on Deep Reinforcement Learning

Robust Reference Signal Self-Organizing Control based on Deep Reinforcement Learning

Traffic Signal Self-Organizing Control With Road Capacity Constraints

This paper aims at the problem of intersection traffic oversaturation and the spillover of queuing vehicles caused by random fluctuations of traffic flow. A self-organizing control model with road capacity constraints and taking minimum expected traffic flow delay as the goal is established to realize real-time adaptive control of traffic signal phase duration. Taking the local intersection and its adjacent intersections as the basic unit of traffic signal control, defining the constraints of road space in queuing state and the equations of traffic signal control basic unit are established. According to the equations, an online method for solving the optimal phase duration of traffic signal is established to efficiently allocate intersection space resources, by which a rule-based real-time traffic signal control model is established. Through the interaction between adjacent intersections, the traffic inflow rate of oversaturated phase is automatically restricted to alleviate the queuing spillover problem. Computer simulation shows that the control effect is significantly better than that of SCATS under the same boundary inflow conditions. Compared with the traditional centralized coordinated control, the rule-based real-time traffic signal control model can better adapt to the fluctuation of traffic flow and effectively relief the problem of vehicle queuing spillover caused by fluctuations of traffic flow.

Self-Organizing Control-Loop Recovery for Predictive Networked Formation Control of Fractionated Spacecraft

Going beyond the current trend of cooperating multiple small satellites we arrive at fractionated satellite architectures. Here the subsystems of all satellites directly self-organize and cooperate among themselves to achieve a common mission goal. Although this leads to a further increase of the advantages of the initial trend it also introduces new challenges, one of which is how to perform closed-loop control of a satellite over a network of subsystems. We present a two-fold approach to deal with the two main disturbances, data losses in the network and failure of the controller, in a networked predictive formation control scenario. To deal with data loss an event based networked model predictive control approach is extended to enable it to adapt to changing network conditions. The controller failure detection and compensation approach is tailored for a possibly large network of heterogeneous cooperating actuator- and controller nodes. The self-organized control task redistribution uses an auction-based methodology. It scales well with the number of nodes and allows to optimize for continuing good control performance despite the controller switch. The stability and smooth control behavior of our approach during a self-organized controller failure compensation while also being subject to data losses was demonstrated on a hardware testbed using as mission a formation control scenario.

An innovative distributed self-organizing control of unmanned surface vehicle swarm with collision avoidance

In this paper, a novel distributed self-organizing control of swarm is proposed for multiple underactuated surface vehicles (USVs) under the environment of irregular obstacles and complex unknown disturbances. An innovative method to deal with irregular obstacles by discretizing the boundary of obstacles is introduced, combining with artificial potential field (APF) method, it can achieve more reasonable collision avoidance of irregular obstacles. A virtual center point of swarm (CPS) is designed to make USVs follow the desired path, while the self-organization swarm strategy has more flexible response when encountering obstacles. Meanwhile, in order to compensate the uncertainty dynamics, the neural network (NN) approximation is used to estimate the ocean disturbance and model uncertainty. Simulation results demonstrate the effectiveness of the proposed strategy.

Observer-based self-organizing adaptive fuzzy neural network control for non-linear, non-affine systems with unknown sign of control gain and dead zone: A case study of pneumatic actuators

In this paper, an observer-based self-organizing adaptive fuzzy neural network (OSOAFNN) control for non-linear, non-affine systems with the unknown sign of control gain and dead zone is presented. First, a reverse dead-zone compensator scheme to cope with the impact of dead-zone phenomenon existing in control input is investigated. Then, an observed-based control approach to address immeasurable states of the system is proposed, utilizing this approach all states of the system are not needed to be available. A self-organizing fuzzy neural network (SOFNN) technique is presented to approximate the non-linear and unknown function of the observer error dynamics. The proposed fuzzy neural network (FNN) model benefits from two main advantages: (1) the number of rules is automatically generated or pruned and (2) the parameters of antecedent and consequent part of SOFNN are updated through the hybrid tuning, simultaneously. Furthermore, the control law contains a Nussbaum function which deals with the unknown sign of control gain. As the system states are immeasurable, the strictly positive real (SPR) Lyapunov function to guarantee the closed-loop system stability, and tracking error convergence to zero is employed, as well as the boundedness of control parameters are assured through a projection law merged with adaptive law. Finally, the controller is practically implemented on a non-linear, non-affine pneumatic system with unknown dead zone which exploits just a sensor for output measuring. Experimental results show that the proposed controller has satisfactory performance in tracking different trajectories, and tracking error for desired signal case I and case II is limited to [−1.5, 1.5] and [−1,1]mm, respectively.

Development of the system of coordinated control of traction power supply equipment and electric rolling stock

The aim of the study is to develop a system of coordinated control of traction power supply equipment and electric rolling stock based on self-organization principles. The research methods of the study are based on multi-agent approach and include coordination of settings of static equipment of reactive power compensation, on-load tap changing of traction transformers, other voltage regulators in the contact network, microprocessor protections, modes of operation of electric locomotives in the interstation area. The obtained results made it possible to substantiate the model of self-organizing control system of traction power supply equipment and electric rolling stock, including the description of interaction and coordination principles, attributes, ontology, algorithms of the efficiency of local regulators of traction network and electric locomotives. The study presents the description of the information model of the traction power supply system and electric rolling stock to control modes and increase the throughput capacity of the power supply system by means of optimum allocation of traction resources. The results of the simulation modeling have confirmed the effectiveness of the proposed solutions.

Towards Self-organized Control: Using Neural Cellular Automata to Robustly Control a Cart-pole Agent

Neural cellular automata (Neural CA) are a recent framework used to model biological phenomena emerging from multicellular organisms. In these systems, artificial neural networks are used as update rules for cellular automata. Neural CA are end-to-end differentiable systems where the parameters of the neural network can be learned to achieve a particular task. In this work, we used neural CA to control a cart-pole agent. The observations of the environment are transmitted in input cells while the values of output cells are used as a readout of the system. We trained the model using deep-Q learning where the states of the output cells were used as the Q-value estimates to be optimized. We found that the computing abilities of the cellular automata were maintained over several hundreds of thousands of iterations, producing an emergent stable behavior in the environment it controls for thousands of steps. Moreover, the system demonstrated life-like phenomena such as a developmental phase, regeneration after damage, stability despite a noisy environment, and robustness to unseen disruption such as input deletion.

Reversible and spatiotemporal control of colloidal structure formation

Tuning colloidal structure formation is a powerful approach to building functional materials, as a wide range of optical and viscoelastic properties can be accessed by the choice of individual building blocks and their interactions. Precise control is achieved by DNA specificity, depletion forces, or geometric constraints and results in a variety of complex structures. Due to the lack of control and reversibility of the interactions, an autonomous oscillating system on a mesoscale without external driving was not feasible until now. Here, we show that tunable DNA reaction circuits controlling linker strand concentrations can drive the dynamic and fully reversible assembly of DNA-functionalized micron-sized particles. The versatility of this approach is demonstrated by programming colloidal interactions in sequential and spatial order to obtain an oscillatory structure formation process on a mesoscopic scale. The experimental results represent an approach for the development of active materials by using DNA reaction networks to scale up the dynamic control of colloidal self-organization.

Crystal Growth Simulation of BMO Nanorods in BMO-Doped REBCO Films With Seed layers

The self-organization of BaMO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> (BMO) nanorods in REBa <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> Cu <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> O <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">y</sub> films is important for enhancing the superconducting properties in magnetic fields. We previously reported the control of BMO self-organization using the seed layer technique, which included the low-temperature growth (LTG) technique. The LTG technique can achieve a high number density of BMO nanorods and excellent critical current density in magnetic fields at 4.2 K. To clarify the self-organization mechanism of BMO nanorods, we develop a three-dimensional Monte Carlo simulation and investigate the effect of the seed layer technique on the growth of BMO nanorods. Results show that the number density of BMO nanorods is affected by the seed layer deposited at a low deposition rate (v <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DR</sub> ) and/or high substrate temperature (T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</sub> ). Moreover, we propose a growth kinetic factor (R) that includes v <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DR</sub> , T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</sub> , and BMO volume fraction. Our simulation results of the number density are affected by R; therefore, Ris useful for predicting the number density of BMO nanorods.