Drift Dynamics Research Articles

Multiplayer hierarchical decision‐making for discrete‐time nonlinear networks of service via value iteration adaptive dynamic programming

AbstractIn this article, the multiplayer hierarchical decision‐making problem for discrete‐time nonlinear networks of service is studied from the perspective of Nash–Stackelberg–Nash games. A novel two‐level value iteration adaptive dynamic programming algorithm is developed to solve the coupled nonlinear Hamilton–Jacobi–Bellman equations associated with the game problem, which neither requires the system drift dynamics to be known as a priori nor requests the initial strategies to be admissible. Moreover, both the value function sequences and the strategy sequences generated by the algorithm converge to their theoretical optimality. An implementation framework for the algorithm is constructed by leveraging the regularized least‐squares method, and a convergence criterion that guarantees the admissibility of the approximated strategies is also proposed. The effectiveness of the proposed algorithm and implementation framework are finally demonstrated through two numerical simulation examples.

Efficient Real-Time Droplet Tracking in Crop-Spraying Systems

Spray systems in agriculture serve essential roles in the precision application of pesticides, fertilizers, and water, contributing to effective pest control, nutrient management, and irrigation. These systems enhance efficiency, reduce labor, and promote environmentally friendly practices by minimizing chemical waste and runoff. The efficacy of a spray is largely determined by the characteristics of its droplets, including their size and velocity. These parameters are not only pivotal in assessing spray retention, i.e., how much of the spray adheres to crops versus becoming environmental runoff, but also in understanding spray drift dynamics. This study introduces a real-time deep learning-based approach for droplet detection and tracking which significantly improves the accuracy and efficiency of measuring these droplet properties. Our methodology leverages advanced AI techniques to overcome the limitations of previous tracking frameworks, employing three novel deep learning-based tracking methods. These methods are adept at handling challenges such as droplet occlusion and varying velocities, ensuring precise tracking in real-time potentially on mobile platforms. The use of a high-speed camera operating at 2000 frames per second coupled with innovative automatic annotation tools enables the creation of a large and accurately labeled droplet dataset for training and evaluation. The core of our framework lies in the ability to track droplets across frames, associating them temporally despite changes in appearance or occlusions. We utilize metrics including Multiple Object Tracking Accuracy (MOTA) and Multiple Object Tracking Precision (MOTP) to quantify the tracking algorithm’s performance. Our approach is set to pave the way for innovations in agricultural spraying systems, offering a more efficient, accurate, and environmentally responsible method of applying sprays and representing a significant step toward sustainable agricultural practices.

Evolving cybersecurity frontiers: A comprehensive survey on concept drift and feature dynamics aware machine and deep learning in intrusion detection systems

Intrusion Detection Systems (IDS) have become pivotal in safeguarding information systems against evolving threats. Concurrently, Concept Drift presents a significant challenge in machine learning, affecting the adaptability and accuracy of predictive models in dynamic environments. Understanding the synergy between IDS and Concept Drift is crucial for developing robust security systems. The motivation behind this survey is driven by the emerging complexities in cyber threats and the dynamic nature of data streams, which necessitate advanced IDS capable of adapting to Concept and Feature Drift. Our analysis reveals a glaring omission in the existing literature—the integration of Concept Drift and Feature Drift within IDS. Most studies have focused on Concept Drift in a general context or on IDS but have yet to comprehensively consider the implications of data dynamics. This oversight has led to a fragmented understanding and suboptimal approaches to tackling modern cyber threats. To address this, we propose a comprehensive review that delves into the role of machine learning in IDS, explicitly focusing on Concept and Feature Drift. We have proposed a framework that includes all the necessary components for a drift-aware IDS. The framework incorporates dynamic feature selection, adaptive learning algorithms, and continuous monitoring techniques to handle Concept Drift and Feature Drift effectively. The survey highlights state-of-the-art methodologies and current challenges in integrating these concepts. The methodology involves an exhaustive analysis of published works from 2019 to 2024, comparing and contrasting various models and approaches. This includes a detailed examination of Concept Drift-aware IDS methods, dynamic feature selection techniques, and the impact of high dimensionality in IDS. These quantitative improvements underscore the necessity for developing adaptive and resilient IDS. The survey uncovers under-represented areas in current research, paving the way for future investigations. By highlighting these gaps and providing comparative data, the survey sets a clear direction for upcoming research efforts to foster the development of more dynamic and adaptable IDS solutions. The quantitative experimental evaluation of the proposed framework is planned to be conducted in a future article, where we will assess its effectiveness and performance in real-world scenarios.

Microplastics and riverine macroinvertebrate communities in a multiple-stressor context: A mesocosm approach

Growing use of synthetic materials has increased the number of stressors that can degrade freshwater ecosystems. Many of these stressors are relatively new and poorly understood, such as microplastics which are now ubiquitous in freshwater systems. The effects of microplastics on freshwater biota must be investigated further in order to better manage and mitigate their impacts. Our experiment provides the first empirical evaluation of stream invertebrate community dynamics in response to microplastics of different concentrations and sizes, in combination with fine sediment, a pervasive known stressor in running waters. In a 7-week streamside experiment using 64 flow-through circular mesocosms, we investigated the effects of exposure to three simulated microplastic influxes (polyethylene microspheres at four levels between 0 and 28,800 items/event) and the addition of fine sediment (to simulate a polluted stream environment). Invertebrate drift was monitored for 48 h immediately after each microplastic influx, and benthic invertebrate communities were sampled after 28 days of microplastic and sediment manipulations. Microplastic concentration, size and fine sediment all had significant factor main effects on several invertebrate drift response metrics, whereas few microplastic main effects were seen in the benthic community. However, interactive stressor effects were common in different combinations between sediment, microplastic size and concentration, suggesting multiple-stressor relationships between microplastics and fine sediment. Microplastic ingestion was witnessed in four of 12 taxa analysed: Hydrobiosidae, Deleatidium spp., Potamopyrgus antipodarum and Archichauliodes diversus. Our findings provide insights into how microplastics affect drift and benthic community dynamics of stream invertebrates in a field-realistic experimental setting and highlight areas requiring further study. These include investigations of invertebrate drift dynamics in response to other types of microplastics, the role invertebrate size may play in determining their vulnerability to microplastic pollution, and framing more microplastic research in a field-realistic multiple-stressor context.

Optimal Asymptotic Tracking Control for Nonzero-Sum Differential Game Systems with Unknown Drift Dynamics via Integral Reinforcement Learning

This paper employs an integral reinforcement learning (IRL) method to investigate the optimal tracking control problem (OTCP) for nonlinear nonzero-sum (NZS) differential game systems with unknown drift dynamics. Unlike existing methods, which can only bound the tracking error, the proposed approach ensures that the tracking error asymptotically converges to zero. This study begins by constructing an augmented system using the tracking error and reference signal, transforming the original OTCP into solving the coupled Hamilton–Jacobi (HJ) equation of the augmented system. Because the HJ equation contains unknown drift dynamics and cannot be directly solved, the IRL method is utilized to convert the HJ equation into an equivalent equation without unknown drift dynamics. To solve this equation, a critic neural network (NN) is employed to approximate the complex value function based on the tracking error and reference information data. For the unknown NN weights, the least squares (LS) method is used to design an estimation law, and the convergence of the weight estimation error is subsequently proven. The approximate solution of optimal control converges to the Nash equilibrium, and the tracking error asymptotically converges to zero in the closed system. Finally, we validate the effectiveness of the proposed method in this paper based on MATLAB using the ode45 method and least squares method to execute Algorithm 2.

Path following of underactuated surface vessels based active disturbance rejection control considering lateral drift

To address the challenges posed by unmodeled dynamics, lateral drift, external disturbances, and the difficulty in measuring velocities in underactuated surface vessels (USVs) motion systems, an active disturbance rejection control (ADRC) with two linear extended state observers (LESO) is proposed for USV path following. Firstly, we employ the Backstepping technique to create a virtual heading angle while estimating lateral drift and unmodeled dynamics using LESO. Subsequently, the ADRC algorithm is employed to control the heading angle. Since measuring the velocities of USV is challenging, velocity observers are introduce to estimate surge and sway velocities. Finally, we utilize the MMG model with high precision to track straight-line and curved paths, respectively. Simulation results validate that our developed controller accurately follows the reference path even in the presence of disturbances, affirming the effectiveness of our proposed control strategy.

Distributed optimal control of nonlinear multi‐agent systems based on integral reinforcement learning

AbstractIn this article, a distributed optimal control approach is proposed for a class of affine nonlinear multi‐agent systems (MASs) with unknown nonlinear dynamics. The game theory is used to formulate the distributed optimal control problem into a differential graphical game problem with synchronized updates of all nodes. A data‐based integral reinforcement learning (IRL) algorithm is used to learn the solution of the coupled Hamilton–Jacobi (HJ) equation without prior knowledge of the drift dynamics, and the actor‐critic neural networks (A‐C NNs) are used to approximate the control law and the cost function, respectively. To update the parameters synchronously, the gradient descent algorithm is used to design the weight update laws of the A‐C NNs. Combining the IRL and the A‐C NNs, a distributed consensus optimal control method is designed. By using the Lyapunov stability theory, the developed optimal control method can show that all signals in the considered system are uniformly ultimately bounded (UUB), and the systems can achieve Nash equilibrium when all agents update their controllers simultaneously. Finally, simulation results are given to illustrate the effectiveness of the developed optimal control approach.

Reconfigurable Resistive Switching Memory for Telegraph Code Sensing and Recognizing Reservoir Computing Systems.

Reservoir computing (RC) system is based upon the reservoir layer, which non-linearly transforms input signals into high-dimensional states, facilitating simple training in the readout layer-a linear neural network. These layers require different types of devices-the former demonstrated as diffusive memristors and the latter prepared as drift memristors. The integration of these components can increase the structural complexity of RC system. Here, a reconfigurable resistive switching memory (RSM) capable of implementing both diffusive and drift dynamics is demonstrated. This reconfigurability is achieved by preparing a medium with a 3D ion transport channel (ITC), enabling precise control of the metal filament that determines memristor operation. The 3D ITC-RSM operates in a volatile threshold switching (TS) mode under a weak electric field and exhibits short-term dynamics that are confirmed to be applicable as reservoir elements in RC systems. Meanwhile, the 3D ITC-RSM operates in a non-volatile bipolar switching (BS) mode under a strong electric field, and the conductance modulation metrics forming the basis of synaptic weight update are validated, which can be utilized as readout elements in the readout layer. Finally, an RC system is designed for the application of reconfigurable 3D ITC-RSM, and performs real-time recognition on Morse code datasets.

Accelerated Gradient Approach For Deep Neural Network-Based Adaptive Control of Unknown Nonlinear Systems.

Recent connections in the adaptive control literature to continuous-time analogs of Nesterov's accelerated gradient method have led to the development of new real-time adaptation laws based on accelerated gradient methods. However, previous results assume that the system's uncertainties are linear-in-the-parameters (LIP). To compensate for non-LIP uncertainties, our preliminary results developed a neural network (NN)-based accelerated gradient adaptive controller to achieve trajectory tracking for nonlinear systems; however, the development and analysis only considered single-hidden-layer NNs. In this article, a generalized deep NN (DNN) architecture with an arbitrary number of hidden layers is considered, and a new DNN-based accelerated gradient adaptation scheme is developed to generate estimates of all the DNN weights in real-time. A nonsmooth Lyapunov-based analysis is used to guarantee the developed accelerated gradient-based DNN adaptation design achieves global asymptotic tracking error convergence for general nonlinear control affine systems subject to unknown (non-LIP) drift dynamics and exogenous disturbances. A comprehensive set of simulation studies are conducted on a two-state nonlinear system, a robotic manipulator, and a complex 20-D nonlinear system to demonstrate the improved performance of the developed method. Our simulation studies demonstrate enhanced tracking and function approximation performance from both DNN architectures and accelerated gradient adaptation.

Representational drift as a result of implicit regularization

Recent studies show that, even in constant environments, the tuning of single neurons changes over time in a variety of brain regions. This representational drift has been suggested to be a consequence of continuous learning under noise, but its properties are still not fully understood. To investigate the underlying mechanism, we trained an artificial network on a simplified navigational task. The network quickly reached a state of high performance, and many units exhibited spatial tuning. We then continued training the network and noticed that the activity became sparser with time. Initial learning was orders of magnitude faster than ensuing sparsification. This sparsification is consistent with recent results in machine learning, in which networks slowly move within their solution space until they reach a flat area of the loss function. We analyzed four datasets from different labs, all demonstrating that CA1 neurons become sparser and more spatially informative with exposure to the same environment. We conclude that learning is divided into three overlapping phases: (i) Fast familiarity with the environment; (ii) slow implicit regularization; and (iii) a steady state of null drift. The variability in drift dynamics opens the possibility of inferring learning algorithms from observations of drift statistics.

Representational drift as a result of implicit regularization.

Recent studies show that, even in constant environments, the tuning of single neurons changes over time in a variety of brain regions. This representational drift has been suggested to be a consequence of continuous learning under noise, but its properties are still not fully understood. To investigate the underlying mechanism, we trained an artificial network on a simplified navigational task. The network quickly reached a state of high performance, and many units exhibited spatial tuning. We then continued training the network and noticed that the activity became sparser with time. Initial learning was orders of magnitude faster than ensuing sparsification. This sparsification is consistent with recent results in machine learning, in which networks slowly move within their solution space until they reach a flat area of the loss function. We analyzed four datasets from different labs, all demonstrating that CA1 neurons become sparser and more spatially informative with exposure to the same environment. We conclude that learning is divided into three overlapping phases: (i) Fast familiarity with the environment; (ii) slow implicit regularization; and (iii) a steady state of null drift. The variability in drift dynamics opens the possibility of inferring learning algorithms from observations of drift statistics.

Visual boundary cues suffice to anchor place and grid cells in virtual reality

The hippocampal formation contains neurons responsive to an animal's current location and orientation, which together provide the organism with a neural map of space.1,2,3 Spatially tuned neurons rely on external landmark cues and internally generated movement information to estimate position.4,5 An important class of landmark cue are the boundaries delimiting an environment, which can define place cell field position6,7 and stabilize grid cell firing.8 However, the precise nature of the sensory information used to detect boundaries remains unknown. We used 2-dimensional virtual reality (VR)9 to show that visual cues from elevated walls surrounding the environment are both sufficient and necessary to stabilize place and grid cell responses in VR, when only visual and self-motion cues are available. By contrast, flat boundaries formed by the edges of a textured floor did not stabilize place and grid cells, indicating only specific forms of visual boundary stabilize hippocampal spatial firing. Unstable grid cells retain internally coherent, hexagonally arranged firing fields, but these fields "drift" with respect to the virtual environment over periods >5 s. Optic flow from a virtual floor does not slow drift dynamics, emphasizing the importance of boundary-related visual information. Surprisingly, place fields are more stable close to boundaries even with floor and wall cues removed, suggesting invisible boundaries are inferred using the motion of a discrete, separate cue (a beacon signaling reward location). Subsets of place cells show allocentric directional tuning toward the beacon, with strength of tuning correlating with place field stability when boundaries are removed.

Vortex splitting in two-dimensional fluids and non-neutral electron plasmas with smooth vorticity profiles

Initially, elliptical, quasi-two-dimensional (2D) fluid vortices can split into multiple pieces if the aspect ratio is sufficiently large due to the growth and saturation of perturbations known as Love modes on the vortex edge. Presented here are experiments and numerical simulations, showing that the aspect ratio threshold for vortex splitting is significantly higher for vortices with realistic, smooth edges than that predicted by a simple “vortex patch” model, where the vorticity is treated as piecewise constant inside a deformable boundary. The experiments are conducted by exploiting the E × B drift dynamics of collisionless, pure electron plasmas in a Penning–Malmberg trap, which closely model 2D vortex dynamics due to an isomorphism between the Drift–Poisson equations describing the plasmas and the Euler equations describing ideal fluids. The simulations use a particle-in-cell method to model the evolution of a set of point vortices. The aspect ratio splitting threshold ranges up to about twice as large as the vortex patch prediction and depends on the edge vorticity gradient. This is thought to be due to spatial Landau damping, which decreases the vortex aspect ratio over time and, thus, stabilizes the Love modes. Near the threshold, asymmetric splitting events are observed in which one of the split products contains much less circulation than the other. These results are relevant to a wide range of quasi-2D fluid systems, including geophysical fluids, astrophysical disks, and drift-wave eddies in tokamak plasmas.

Dynamic modeling and optimization of mechanical drift for compliant parallel micromanipulation driven by V-shape linear ultrasonic motors

Designing micro-mechanisms that satisfy large strokes, high precision, and multiple degrees of freedom has consistently been a research focus and challenge in the field of micromanipulation systems. Linear ultrasonic motors (LUMs), with advantages such as millimeter-level travel, nanometer-level motion resolution, and power-off self-locking, significantly contribute to the advancement in the mechanism design of micromanipulation systems. However, the challenge of mechanical drift lacks a comprehensive theoretical analysis to explain its cause and limits its application. This paper presents a mechanical drift modeling method for a compliant parallel micromanipulation (CPM) driven by a LUM. The model primarily focuses on the creep characteristics of the flexure clamp element in the LUM. Based on the stiffness matrix analysis method of the compliant parallel mechanism kinematics, the mechanical drift dynamics model of the CPM is established. The influence of the main structural parameters on the CPM mechanical drift is investigated. The research indicates that the creep of flexure clamps in the CPM is the main factor leading to the mechanical drift. Optimization of mechanical drift is achieved by choosing flexure clamps with higher tangential stiffness for LUM and flexure hinge joints with greater flexibility for CPM. Finally, experimental studies reveal that under equivalent conditions, the mechanical drift of the optimized CPM was significantly reduced than before, proving the rationality of the theoretical model and optimization method.

An analytical adaptive optimal control approach without solving HJB equation for nonlinear systems with input constraints

AbstractThis paper presents an analytical method to solve the optimal control problem for affine nonlinear systems with unknown drift dynamics. A new non‐quadratic cost function over an infinite horizon is presented that considers input constraints and includes the cost of the feed‐forward component of the control law. The mean value theorem for vector‐valued functions has been used to derive an integral form of this theorem. Based on this theorem, a rigorous proof is provided demonstrating that the cost function can be converted into another form. In the presence of input constraints, this converted form enables extracting the optimal control solution without solving the HJB equation. Additionally, unknown nonlinearity effects in drift dynamics are compensated in the control input. This is accomplished by estimating the unknown drift dynamics via an adaptive neural network (NN) approach. It is proven that the states and weights of NN are uniformly ultimately bounded based on a Lyapunov technique. The necessary and sufficient conditions are provided that ensure the optimality of the infinite horizon optimal control problem with a discount factor. As a result, it is demonstrated that the proposed approach satisfies the optimality criteria. To evaluate the effectiveness of the proposed approach, simulation examples are provided.

Functional characterization of archaic-specific variants in mitonuclear genes: insights from comparative analysis in S. cerevisiae.

Neanderthal and Denisovan hybridisation with modern humans has generated a non-random genomic distribution of introgressed regions, the result of drift and selection dynamics. Cross-species genomic incompatibility and more efficient removal of slightly deleterious archaic variants have been proposed as selection-based processes involved in the post-hybridisation purge of archaic introgressed regions. Both scenarios require the presence of functionally different alleles across Homo species onto which selection operated differently according to which populations hosted them, but only a few of these variants have been pinpointed so far. In order to identify functionally divergent archaic variants removed in humans, we focused on mitonuclear genes, which are underrepresented in the genomic landscape of archaic humans. We searched for non-synonymous, fixed, archaic-derived variants present in mitonuclear genes, rare or absent in human populations. We then compared the functional impact of archaic and human variants in the model organism Saccharomyces cerevisiae. Notably, a variant within the mitochondrial tyrosyl-tRNA synthetase 2 (YARS2) gene exhibited a significant decrease in respiratory activity and a substantial reduction of Cox2 levels, a proxy for mitochondrial protein biosynthesis, coupled with the accumulation of the YARS2 protein precursor and a lower amount of mature enzyme. Our work suggests that this variant is associated with mitochondrial functionality impairment, thus contributing to the purging of archaic introgression in YARS2. While different molecular mechanisms may have impacted other mitonuclear genes, our approach can be extended to the functional screening of mitonuclear genetic variants present across species and populations.

The influence of channel morphology and hydraulic complexity on larval pallid sturgeon (Scaphirhynchus albus) drift and dispersal dynamics in the Fort Peck Segment, Upper Missouri River: insights from particle tracking simulations

Longitudinal dispersal of migratory fish species can be interrupted by factors that fragment rivers, such as dams and reservoirs with incompatible habitats, and indirect alterations to variables, such as water temperature or turbidity. The endangered pallid sturgeon (Scaphirhynchus albus) population in the Upper Missouri River Basin in North Dakota and Montana is an example of such fragmentation and alteration due to the construction of dams. We applied a high-resolution, 2+-dimensional modelling framework composed of hydrodynamic and Lagrangian particle tracking components to simulate pallid sturgeon larval drift and dispersal along a 33-km section of the Upper Missouri River to evaluate three main issues: a comparison between multidimensional models and traditional 1-dimensional models, the sensitivity of hydrodynamics to channel morphology, and the implications of channel morphology on retention and transport-time metrics for larval fish. The results indicate that multidimensional models better represent breakthrough curves of transporting larvae compared to 1-dimensional models, especially for the long tail of slow drifters in the population. Results also indicate that channel morphology and hydraulic complexity play significant roles in larval dispersal with certain flow conditions and channel features increasing larval retention and providing potential management options to increase survival rates by adjusting flow conditions during spawning events. For example, modelling indicates increased retention times at discharges 23–38% daily flow exceedance, coincident with emergence of mid-channel sandbars. Findings additionally emphasize the need for improved understanding of biological factors that affect larval drift and dispersal.

Model Predictive Control with Deep Neural Network Based Integral Sliding Modes Generation for a Class of Uncertain Nonlinear Systems

In this paper, a Model Predictive Control (MPC) scheme incorporating Integral Sliding Mode (ISM) is presented to deal with a class of uncertain nonlinear systems. Classical design of ISM control components requires the knowledge of the system nominal dynamics. In the present paper, it is assumed that the nominal dynamics is unknown, and that matched disturbances affect the system state evolution. In the proposed approach, two Deep Neural Networks (DNNs) are employed to estimate the drift dynamics and control effectiveness matrix of the system. The approximation errors introduced by the DNNs, along with the matched disturbances acting on the system, are compensated by the DNN-based ISM control. The weights of the DNNs are tuned relying on adaptation laws derived from Lyapunov analysis, providing theoretical guarantees. A solution to the considered control problem based on the joint use of the DNN-based ISM control and MPC is discussed. The proposal is assessed in simulation on the classical Duffing oscillator with satisfactorily results.

Deep Luenberger observer-based consistency tracking for nonlinear heterogeneous multi-agent systems with uncertain drift dynamics

In recent years, multi-agent systems (MASs) technology, represented by swarms of unmanned aerial vehicles (UAVs), has been continuously developed and become a hot topic in academic research. Combining the theory of deep learning (DL) and Luenberger observer, this paper puts forward the tracking control problem for the nonlinear heterogeneous MASs with uncertain drift dynamics. Concretely, a novel class of deep Luenberger observer (DLO) is constructed for state estimation and a novel deep robust controller (DRC) is designed based on the proposed DLOs. Finally, a numerical simulation demonstrates that the proposed methodology is valid, more general and performs better compared to traditional robust control methods.

Suboptimal Leader-to-Coordination Control for Nonlinear Systems With Switching Topologies: A Learning-Based Method.

In the cooperative control for multiagent systems (MASs), the key issues of distributed interaction, nonlinear characteristics, and optimization should be considered simultaneously, which, however, remain intractable theoretically even to this day. Considering these factors, this article investigates leader-to-formation control and optimization for nonlinear MASs using a learning-based method. Under time-varying switching topology, a fully distributed state observer based on neural networks is designed to reconstruct the dynamics and the state trajectory of the leader signal with arbitrary precision under jointly connected topology assumption. Benefitted from the observers, formation for MASs under switching topologies is transformed into tracking control for each subsystem with continuous state generated by the observers. An augmented system with discounted infinite LQR performance index is considered to optimize the control effect. Due to the complexity of solving the Hamilton-Jacobi-Bellman equation, the optimal value function is approximated by a critic network via the integral reinforcement learning method without the knowledge of drift dynamics. Meanwhile, an actor network is also presented to assure stability. The tracking errors and estimation weighted matrices are proven to be uniformly ultimately bounded. Finally, two illustrative examples are given to show the effectiveness of this method.