Dynamic Subsystem Research Articles

Learning mechanism for a collective classifier based on competition driven by training examples

The purpose of this work is to modify the learning mechanism of a collective classifier in order to provide learning by population dynamics alone, without requiring an external sorting device. A collective classifier is an ensemble of non-identical simple elements, which do not have any intrinsic dynamics neither variable parameters; the classifier admits learning by adjusting the composition of the ensemble, which was provided in the preceding literature by selecting the ensemble elements using a sorting device. Methods. The population dynamics model of a collective classifier is extended by adding a “learning subsystem”, which is controlled by a sequence of training examples and, in turn, controls the strength of intraspecific competition in the population dynamics. The learning subsystem dynamics is reduced to a linear mapping with random parameters expressed via training examples. The solution to the mapping is an asymptotically stationary Markovian random process, for which we analytically find asymptotic expectation and show its variance to vanish in the limit under the specified assumptions, thus allowing an approximate deterministic description of the coupled population dynamics based on available results from the preceding literature. Results. We show analytically and illustrate it by numerical simulation that the decision rule of our classifier in the course of learning converges to the Bayesian rule under assumptions which are essentially in line with available literature on collective classifiers. The implementation of the required competitive dynamics does not require an external sorting device. Conclusion. We propose a conceptual model for a collective classifier, whose learning is fully provided by its own population dynamics. We expect that our classifier, similarly to the approaches taken in the preceding literature, can be implemented as an ensemble of living cells equipped with synthetic genetic circuits, when a mechanism of population dynamics with synthetically controlled intraspecific competition becomes available.

Institutional support for the development of the Donbas: a retrospective analysis performed upon the inclusion of the region Into the Russian Federation

Subject. Institutional support for the development of the Donbas during the process of its adaptation to the social and economic system of the Russian Federation. The state and development prospects of institutional subsystems: business, public authority, financial, and non-commercial sectors. Purpose. To study the main parameters of the institutional subsystems in the Donbas and determine the problems and the vectors of institutional, social, and economic development of the region in the situation of an armed conflict running parallel to its integration with Russian institutes. Research methods. The theoretical basis of the study was the dialectical method, which allowed us to consider social, economic, and institutional systems of the Donbas in the context of integration of the region in Russian social environment. To determine the problems of social, economic, and institutional development of the region, the monographic method was used. It allowed us to review the existing approaches to the state and prospects of the economy and the supporting institutes during the period of integration into the Russian Federation. A retrospective analysis was a key practical research method. It helped to determine the dynamics of the main institutional subsystems of the region from the point of view of modernisation of the functions implemented by such institutes as business, public authority, financial, and non-commercial sector during the armed conflict of 2014-2021 and the special military operation. Results and discussion. After its inclusion into the Russian Federation, the institutional support of the development of the Donbas requires a fundamental transformation in order to adapt to new conditions. The transformation should involve a comprehensive transformation of the social and economic model of the region and its infrastructure, diversification, and modernisation of the development institutes. At the same time, it is necessary to take into account the weaknesses of the institutional system of the region. They include external threats caused by the armed conflict which started in 2014 and caused damage to the business institute; a dramatic reduction in the number of stimuli for the functioning of financial institutes, especially for the innovative and investment activities; underdevelopment of non-commercial organisations; and a high level of bureaucracy in public institutions. The new institutional model should be aimed at decreasing the vulnerability of the region to geopolitical and economic crises and its dependence on heavy industry and coal mining, while developing high-tech industries and innovations, and investing in education, healthcare, and the retraining of the labour force. It is therefore necessary to develop reasonable adaptation strategies and programmes, including appointing responsible executives to the governmental and municipal bodies and developing mechanisms for the financing of projects aimed at the economic growth and well-being of the population.

Identification of switched dynamic system for electric multiple unit train modeling

A new iterative algorithm is proposed to identify the high-speed train model which is described as a switched dynamic system including a fast switched nonlinear static subsystem followed by a non-switched linear dynamic subsystem. The non-switched property of the linear part leads to a more complex dynamics of the whole system, compared to the switched Hammerstein system which linear part switches synchronously with its nonlinear part. The proposed iterative algorithm alternates between identifying the switched nonlinear static subsystem and the linear dynamic subsystem. The switched nonlinear subsystem identification incorporates a quadratic error bound constraint with respect to nominal values of model parameters, and is formulated as a second-order cone program. Properties of convergence and asymptotic unbiasedness of the obtained estimates are proved under some necessary assumptions. With real data and simulation examples, the proposed iterative algorithm produces convergent and asymptotically unbiased estimates. In the simulation results, incorporating the quadratic error bounds results in a fast convergence rate in the presence of a small noise variance, and also produces a smaller identification error in the presence of colored noises with a non-zero mean and a large variance.

Event-Triggered Path Following Robust Control of Underactuated Unmanned Surface Vehicles with Unknown Model Nonlinearity and Disturbances

An effective path-following controller is a guarantee for stable sailing of underactuated unmanned surface vehicles (USVs). This paper proposes an event-triggered robust control approach considering an unknown model nonlinearity, external disturbance, and event-triggered mechanism. The proposed method consists of guidance and dynamic control subsystems. Based on the tracking error dynamics equations, the guidance subsystem is designed to achieve the guidance law. For the dynamic control subsystem, the radial basis function neural networks (RBFNNs) are designed to approximate the unknown model nonlinearity and external disturbances to improve the robustness of the proposed method. In addition, an event-triggered mechanism is constructed to reduce the triggering times. The closed-loop system is proven to be stable, and the effectiveness of the proposed method is illustrated through simulation results.

Reliability evaluation of phased-mission systems exposed to random shocks

The paper aims to examine the reliability analysis problem of phased-mission systems that execute multiple consecutive non-overlapping mission phases in a random shock environment. Each phase is carried out by several subsystems which consist of non-repairable components connected in different topology configurations. The random shocks, modeled by a Poisson process, significantly affect the failure rate of components within the dynamic subsystems. The system configuration, success criteria, and component failure behavior may differ from phase to phase. The paper presents technical methodologies to evaluate the mission success probabilities of subsystems in various configurations and proposes a modularization method to compute the reliability of phased-mission systems by combining the mission success probabilities of all subsystems. Finally, a numerical example is provided to illustrate the proposed model and method.

Double tracking control for the complex dynamic network with an unavailable link state

This research investigates the double tracking control problem for the complex dynamic network (CDN) with an unavailable link state. Firstly, from the angle of a large-scale system, the dynamical model of CDN is described by the vector differential equations, which consists of node dynamic subsystem (NDS) and link dynamic subsystem (LDS), in which the weighted-values of links are regarded as the state variables of LDS. Secondly, to realise the double tracking control (DT-Control) of CDN, the presented DT-Control scheme in this paper includes the synthesis of controller for NDS and the coupling term in LDS, which can ensure that the two subsystems track the given reference targets. The tracking of NDS contains the synchronisation of nodes as the special case, and the tracking of LDS shows that the eventual topologic structure of CDN will be determined only by the given link reference signal. Due to the economic and technological limitations of sensors in the practice applications, this paper assumes that the state variables of LDS are unavailable in the DT-Control scheme. Finally, the engineering simulation example is given to verify the validity of DT-Control scheme proposed in this paper.

Hydrodynamic cumulation mechanism caused by quantum shell effects.

The computational and theoretical analysis carried out in this article demonstrates the existence of a nontrivial mechanism for the compression of a submicron-sized gas bubble formed by a gas of classical ions and a gas of degenerate electrons. This mechanism fundamentally differs from conventional compression mechanisms. It is shown that taking into account the quantum effect of a large spatial scale in the distribution of electrons qualitatively changes the character of cumulative processes. Because of a large-scale electric field caused by quantum shell effects, the compression process is characterized by the formation of multiple shock waves. The values of gas temperature and pressure achieved during compression occur higher by two orders of magnitude as compared with the classical adiabatic regime. The analysis is carried out within the framework of the following model: the dynamics of the electron subsystem is described by equations of a quantum electron fluid, while the hydrodynamic approximation is adopted for the ionic subsystem. The large-scale effect is taken into account by means of effective external field acting on electrons. The theoretical analysis carried out within this approach clarifies the nature of the cumulative process in the system under consideration; some quantitative characteristics obtained with numerical simulation are presented. The possibility of experimental observation of this cumulative mechanism is analyzed. It is suggested that the manifestation of the effect can be observed during laser compression of a system of submicron targets by measuring the neutron yield.

Parameter estimation of Wiener-Hammerstein system based on multi-population self-adaptive differential evolution algorithm

PurposeThe Wiener-Hammerstein nonlinear system is made up of two dynamic linear subsystems in series with a static nonlinear subsystem, and it is widely used in electrical, mechanical, aerospace and other fields. This paper considers the parameter estimation of the Wiener-Hammerstein output error moving average (OEMA) system.Design/methodology/approachThe idea of multi-population and parameter self-adaptive identification is introduced, and a multi-population self-adaptive differential evolution (MPSADE) algorithm is proposed. In order to confirm the feasibility of the above method, the differential evolution (DE), the self-adaptive differential evolution (SADE), the MPSADE and the gradient iterative (GI) algorithms are derived to identify the Wiener-Hammerstein OEMA system, respectively.FindingsFrom the simulation results, the authors find that the estimation errors under the four algorithms stabilize after 120, 30, 20 and 300 iterations, respectively, and the estimation errors of the four algorithms converge to 5.0%, 3.6%, 2.7% and 7.3%, which show that all four algorithms can identify the Wiener-Hammerstein OEMA system.Originality/valueCompared with DE, SADE and GI algorithm, the MPSADE algorithm not only has higher parameter estimation accuracy but also has a faster convergence speed. Finally, the input–output relationship of laser welding system is described and identified by the MPSADE algorithm. The simulation results show that the MPSADE algorithm can effectively identify parameters of the laser welding system.

An open-end high-power microwave-induced fracturing system for hard rock

Microwave pre-treatment is considered as a promising technique for alleviating cutter wear. This paper introduces a high-power microwave-induced fracturing system for hard rock. The test system consists of a high-power microwave subsystem (100 kW), a true triaxial testing machine, a dynamic monitoring subsystem, and an electromagnetic shielding subsystem. It can realize rapid microwave-induced fracturing, intelligent tuning of impedance, dynamic feedback under strong microwave fields, and active control of microwave parameters by addressing the following issues: the instability and insecurity of the system, the discharge breakdown between coaxial lines during high-power microwave output, and a lack of feedback of rock-microwave response. In this study, microwave-induced surface and borehole fracturing tests under true triaxial stress were carried out. Experimental comparisons imply that high-power microwave irradiation can reduce the fracturing time of hard rock and that the fracture range (160 mm) of a 915-MHz microwave source is about three times that of 2.45 GHz. After microwave-induced borehole fracturing, many tensile cracks occur on the rock surface and in the borehole: the maximum reduction of the P-wave velocity is 12.8%. The test results show that a high-power microwave source of 915 MHz is more conducive to assisting mechanical rock breaking and destressing. The system can promote the development of microwave-assisted rock breaking equipment.

Machine Learning-Based Model Predictive Control of Two-Time-Scale Systems

In this study, we present a general form of nonlinear two-time-scale systems, where singular perturbation analysis is used to separate the dynamics of the slow and fast subsystems. Machine learning techniques are utilized to approximate the dynamics of both subsystems. Specifically, a recurrent neural network (RNN) and a feedforward neural network (FNN) are used to predict the slow and fast state vectors, respectively. Moreover, we investigate the generalization error bounds for these machine learning models approximating the dynamics of two-time-scale systems. Next, under the assumption that the fast states are asymptotically stable, our focus shifts toward designing a Lyapunov-based model predictive control (LMPC) scheme that exclusively employs the RNN to predict the dynamics of the slow states. Additionally, we derive sufficient conditions to guarantee the closed-loop stability of the system under the sample-and-hold implementation of the controller. A nonlinear chemical process example is used to demonstrate the theory. In particular, two RNN models are constructed: one to model the full two-time-scale system and the other to predict solely the slow state vector. Both models are integrated within the LMPC scheme, and we compare their closed-loop performance while assessing the computational time required to execute the LMPC optimization problem.

SEASNAKE: Impact - Marine operations modelling for evidence-based results detailing the impact of using a new fully dynamic cable design for ocean energy devices.

The SEASNAKE project - an OceanERA-NET project – is aiming for developing fully dynamic cables for ocean energy. Through new design and application of novel coatings the project is designing a dynamic cable that better suits the conditions and user requirements. In order to fully understand the benefits, the Wave Venture TEMPEST™ techno-economic analysis software will be used to simulate the performance of a proposed wave energy farm with a special focus on the contribution of the dynamic cable subsystem. The results obtained from the simulations will not only provide a deep understanding of the reliability and cost-risk areas regarding the use of dynamic cables, but will also allow a comparison with a baseline scenario consisting of a more traditional cabling system. The latter will be key to identify the advantages of the SEASNAKE project and its commercial viability. KPIs (Key Performance Indicators) will include farm availability and power production downtime as a direct result of cable related issues. The reliability, maintainability and survivability of the cabling subsystem will be tested and the KPIs will give clear indication on the performance benefits and ultimately the impact on the LCOE will be of greatest consideration.

Advanced Modeling, Analysis and Control for Electrified Vehicles

Electrified vehicles, especially fully driven electric ground vehicles, are expected to provide significantly increased traffic mobility and road utilization with faster response times, lower levels of fuel consumption, less environmental pollution, electrified power sources and actuators, and the benefits of greater driving safety and convenience integrated with diverse, dynamic subsystems [...]

Thermal Transport in Nanoelectronic Devices Cooled by On-Chip Magnetic Refrigeration.

On-chip demagnetization refrigeration has recently emerged as a powerful tool for reaching microkelvin electron temperatures in nanoscale structures. The relative importance of cooling on-chip and off-chip components and the thermal subsystem dynamics are yet to be analyzed. We study a Coulomb blockade thermometer with on-chip copper refrigerant both experimentally and numerically, showing that dynamics in this device are captured by a first-principles model. Our work shows how to simulate thermal dynamics in devices down to microkelvin temperatures, and outlines a recipe for a low-investment platform for quantum technologies and fundamental nanoscience in this novel temperature range.

INVASION AND SUPERINFECTION IN DETERMINISTIC AND STOCHASTIC TWO-STRAIN DENGUE MODELS WITH DEMOGRAPHIC AND SEASONAL VARIATION

The effects of seasonality on disease invasion and superinfection are investigated in dengue epidemic models with two viral strains. A two-strain host–vector nonautonomous ODE is formulated with seasonal periodicity in either vector recruitment or transmission. The basic reproduction number is derived and the dynamics of the single-strain periodic subsystem are summarized. Conditions for uniform persistence and existence of a single-strain periodic solution are verified and the invasion reproduction number is derived. A time-nonhomogeneous, continuous-time Markov chain (CTMC) is formulated from the ODE framework. The basic and invasion reproduction numbers apply to the ODE and CTMC. In the case of invasion, after one strain is established and the invasion reproduction number exceeds one, the ODE predicts a successful invasion, but in the CTMC, there is a periodic probability of invasion that depends on time and size of the invasion. A multitype branching process approximation of the CTMC provides an analytical method to predict the periodic probability of invasion. The numerical results show that the peak time of invasion is related to the current endemic strain and the dominant seasonal driver but it often precedes the peak time of the seasonal driver.

Modularize-and-Conquer: A Generalized Impact Dynamics and Safe Precollision Control Framework for Floating-Base Tree-Like Robots

Flexible and versatile mobile robotic coworkers are becoming an indispensable commodity for helping humans with repetitive or physically demanding work. A key challenge with these systems is respecting the strict safety requirements in shared and collaborative workspaces. This inevitably requires solving their whole-body dynamics to obtain the necessary inertial impact properties. In this article, we present an integrated impact dynamics and safe precollision control framework to address the discussed challenge. We propose a novel modular dynamics approach that provides efficient formulations for reusing the uncoupled subsystem dynamics when evaluating the coupled system. Our approach is generalized for deriving the whole-body impact dynamics of any articulated floating-base robot. Furthermore, it outperforms classical monolithic approaches for computing the dynamics, making it favorable for systems with more than two dynamic subsystems while allowing decentralized computations. Finally, based on the proposed modular and generalized impact dynamics and extending our previous work, we introduce the generalized safe motion unit as a unified safety scheme for floating-base robotic structures with branched manipulation extremities. The proposed concepts are evaluated on an exemplary wheeled mobile manipulator, considering realistic use cases in simulation and real-world experiments. The obtained results validated the efficacy of our framework and developed methods.

Correcting instructive electric potential patterns in multicellular systems: External actions and endogenous processes

BackgroundTransmembrane electrical potential differences in cells modulate the spatio-temporal distribution of signaling ions and molecules that are instructive for downstream signaling pathways in multicellular systems. The local coupling between bioelectricity and protein transcription patterns allows dynamic subsystems (modules) of cells that share the same bioelectrical state to show similar biochemical downstream processes. MethodsWe simulate theoretically how the integration-segregation pattern formed by the different multicellular modules that define a biosystem can be controlled by multicellular potentials. To this end, we couple together the model equations of the bioelectrical network to those of the genetic network. ResultsThe coupling provided by the intercellular junctions and the external microenvironment allows the restoration of the target bioelectrical pattern by changing the transcription rate of specific ion channels, the post-translational blocking of these channels, and changes in the environmental ionic concentrations. ConclusionsThe simulations show that the single-cell feedback between bioelectrical and transcriptional processes, together with the coupling provided by the intercellular junctions and the environment, can correct large-scale patterns by means of suitable external actions. General significanceThis study provides a theoretical advancement in the understanding of how the multicellular bioelectric coupling may guide repolarizing interventions for regenerating a tissue, with potential implications in biomedicine.

Development of a dynamical model and energy analysis for wheel loader

The objective of this paper is to develop a fully integrated model for the wheel loader, including the subsystem dynamics of the engine, drivetrain, working circuit, steering circuit, and vehicle. It leads to a high-order strongly nonlinear system, and all state variables are coupled together to form a Multi-Input and Multi-Output (MIMO) system. A control architecture is proposed to decouple the MIMO system into several Single-Input and Single-Output (SISO) systems. A tracking problem has been formulated to validate this fully integrated model with the field test data. The accuracy of the model is verified by the 2.3% difference between the measured and simulated fuel consumption. Meanwhile, an energy distribution analysis is conducted to reveal the energy consumption and energy loss of each portion of the wheel loader. Such a model can be used to plan the working pattern, guide the driving habits of human operators, or refine the underlying architecture, leading to the ultimate goal of reducing total fuel consumption and improving productivity.

On the generalized Bézier-based integration approach for co-simulation applications

This paper investigates the efficiency of a recently developed numerical approach for the integration of the dynamics of co-simulation subsystems. In this regard, a robust Bézier-based multi-step integration approach is implemented in the subsystem solvers instead of conventional approaches, like the Euler method. Although such simplistic integration formulas proved to be accurate and easy to incorporate, they often suffer from instability when using large step sizes, particularly in solving stiff differential equations. On the other side, multistep methods can show better accuracy and convergence properties, which make them proper candidates for the solution of co-simulated dynamics. This paper puts forward the use of the Bézier integration formulas in the integration of Multibody system (MBS) dynamics, with a particular focus on co-simulation algorithms. To evaluate the proposed method efficiency, some straightforward benchmark problems are considered as case studies, such as a linear oscillator and two nonlinear hydraulic cranes. This study reveals the ability of the Bézier formula to keep the mechanical energy balance of the overall system. As a consequence, the stability of co-simulation results is improved. Furthermore, implementing the Bézier formulas minimizes the residual energy, which consequently reduces the energy errors in the co-simulation environment.

Attitude Synchronization of a Group of Rigid Bodies Using Exponential Coordinates.

Currently, managing a group of satellites or robot manipulators requires coordinating their motion and work in a cooperative way to complete complex tasks. The attitude motion coordination and synchronization problems are challenging since attitude motion evolves in non-Euclidean spaces. Moreover, the equation of motions of the rigid body are highly nonlinear. This paper studies the attitude synchronization problem of a group of fully actuated rigid bodies over a directed communication topology. To design the synchronization control law, we exploit the cascade structure of the rigid body's kinematic and dynamic models. First, we propose a kinematic control law that induces attitude synchronization. As a second step, an angular velocity-tracking control law is designed for the dynamic subsystem. We use the exponential coordinates of rotation to describe the body's attitude. Such coordinates are a natural and minimal parametrization of rotation matrices which almost describe every rotation on the Special Orthogonal group SO(3). We provide simulation results to show the performance of the proposed synchronization controller.

Adaptive robust control for reliable trajectory tracking of autonomous vehicle in uncertain driving environment

External disturbances, parameter perturbance, data delay and control lag provoke significant model mismatches. If not properly compensated, they can greatly deteriorate the control performance of autonomous vehicle, such as reduction of tracking accuracy or even loss of stability in extreme. However, existing approaches barely consider these uncertainties together. In the light of this, an adaptive strategy is presented for trajectory tracking control of autonomous vehicle to simultaneously cope with aforementioned factors. First of all, given the dynamic or kinematic characteristics among path, vehicle and steering actuator, an integrated dynamic model is constructed. To handle the control lag of the steering actuator, a first-order model is utilized to approximate the dynamics of the steering subsystem, which is then integrated into the vehicle dynamics to reformulate the tracking model as a lag-free one. Then, the hierarchical robust tracking controller is proposed to acquire reliable control commands. To prevent the system breakdown in the presence of data delay, the delay-dependent criterion is designed via linear parameter varying technique and integral inequality approach. Moreover, the controllers also consider both the H∞ index and the guaranteed cost one to guarantee the effectiveness and robustness of tracking commands. Subsequently, to enhance the adaptability of algorithm, a feedback gains scheduling mechanism is proposed to adaptively tune tracking commands among different robust gains leveraging the phase plane approach. Finally, several comparative cases are conducted in the hardware-in-the-loop platform to verify that proposed strategy has better capability on trajectory tracking in uncertain driving conditions.