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In this paper, we introduce a systematic methodology to discover state-space representations of dynamical systems from noisy data. Our approach utilizes a fusion of basis functions to adeptly estimate both the structure and parameters inherent in the systems under investigation. To strengthen the robustness of our methodology, we integrate estimation algorithms grounded in both deterministic and stochastic approaches. Through extensive simulations, we showcase the efficacy of our proposed method in accurately uncovering the state-space representations of diverse dynamical systems. The synergy of versatile basis functions and advanced estimation algorithms enhances our approach to navigate the complexity inherent in various systems, offering a reliable and accurate framework for the discovery of equations from empirical data. The results highlight the potential of the proposed method as a versatile tool for system identification, capable of providing accurate insights into the underlying dynamics of complex systems based on observed data.

Sensing is one of the most fundamental tasks for the monitoring, forecasting, and control of complex, spatio-temporal systems. In many applications, a limited number of sensors are mobile and move with the dynamics, with examples including wearable technology, ocean monitoring buoys, and weather balloons. In these dynamic systems (without regions of statistical-independence), the measurement time history encodes a significant amount of information that can be extracted for critical tasks. Most model-free sensing paradigms aim to map current sparse sensor measurements to the high-dimensional state space, ignoring the time-history all together. Using modern deep learning architectures, we show that a sequence-to-vector model, such as an LSTM (long, short-term memory) network, with a decoder network, dynamic trajectory information can be mapped to full state-space estimates. Indeed, we demonstrate that by leveraging mobile sensor trajectories with shallow recurrent decoder networks, we can train the network (i) to accurately reconstruct the full state space using arbitrary dynamical trajectories of the sensors, (ii) the architecture reduces the variance of the mean-squared error of the reconstruction error in comparison with immobile sensors, and (iii) the architecture also allows for rapid generalization (parameterization of dynamics) for data outside the training set. Moreover, the path of the sensor can be chosen arbitrarily, provided training data for the spatial trajectory of the sensor is available. The exceptional performance of the network architecture is demonstrated on three applications: turbulent flows, global sea-surface temperature data, and human movement biomechanics.

Analog computing is based upon using physical processes to solve formal mathematical problems. In the past, it was the predominant instrument of scientific calculations. Now, as the physical limits imposed on digital devices compel research into alternate computing paradigms, a reexamination of the potentialities of analog computing is warranted. This work studies the application of analog CMOS cells toward the simulation of dynamical systems, and, more generally, solving sets of coupled time-dependent ordinary differential equations. Following a brief review of the fundamentals of systems theory and analog computing, the main set of computing elements is introduced, each comprising analog cells designed in a 130 nm process. These are subsequently applied to the realization of practical, special-purpose analog computing modules. Illustrative systems from various fields are selected for simulation. Though by no means comprehensive, these case studies highlight the capabilities of contemporary analog computing, especially in solving nonlinear problems. Circuit simulations show good agreement with solutions obtained from high-order numerical methods, at least over a limited range of system parameters. The article concludes with a brief discussion of broader analog computing applications, offering future prospects toward further exploration of its potentialities and limitations in a wide range of domains.

Nonlinear fluid dynamical systems, such as thermoacoustic systems, aeroelastic systems are archetypical complex systems involving state transitions upon a change in bifurcation parameter. These state transitions in any certain direction are always undesirable and can radically alter the operational paradigms associated with these systems. Hence, predicting the impending dynamical state is paramount for avoiding such undesirable transitions. The hitherto research so far focused largely on metric-based and model-based indicators to foretell an impending transition and is often fraught with difficulties when deployed in practicable scenarios. In this study, we assuage this end of concern by proposing a model-agnostic data-driven method for automated classification of the dynamical states of nonlinear fluid dynamical systems. By using recurrence plots we transform the time series pertaining to the dynamical states into images and subsequently employ a convolution neural network (CNN) to classify the generated images. This study also proceeds to present cross-domain classifications via a trained deep learning (DL) model and successfully classify the dynamical states of one fluid dynamical system (say, thermoacoustic) with the dynamical states of another fluid dynamical system (say, aeroelastic). The underlying methodology for the above is based on open set (OS) domain adaptation - inherent to transfer learning schemes. Towards enhancing the confidence levels of our proposed methodology, we carry out four cross-domain numerical experiments, wherein we consistently get about 94 - 98% accuracy.

Finite-time Lyapunov exponents (FTLEs) provide a powerful approach to compute time-varying analogs of invariant manifolds in unsteady fluid flow fields. These manifolds are useful to visualize the transport mechanisms of passive tracers advecting with the flow. However, many vehicles and mobile sensors are not passive, but are instead actuated according to some intelligent trajectory planning or control law; for example, model predictive control and reinforcement learning are often used to design energy-efficient trajectories in a dynamically changing background flow. In this work, we investigate the use of FTLE on such controlled agents to gain insight into optimal transport routes for navigation in known unsteady flows. We find that these controlled FTLE (cFTLE) coherent structures separate the flow field into different regions with similar costs of transport to the goal location. These separatrices are functions of the planning algorithm’s hyper-parameters, such as the optimization time horizon and the cost of actuation. Computing the invariant sets and manifolds of active agent dynamics in dynamic flow fields is useful in the context of robust motion control, hyperparameter tuning, and determining safe and collision-free trajectories for autonomous systems. Moreover, these cFTLE structures provide insight into effective deployment locations for mobile agents with actuation and energy constraints to traverse the ocean or atmosphere.

How spiking neuronal networks encode memories in their different time and spatial scales constitute a fundamental topic in neuroscience and neuro-inspired engineering. Much attention has been paid to large networks and long-term memory, for example in models of associative memory. Smaller circuit motifs may play an important complementary role on shorter time scales, where broader network effects may be of less relevance. Yet, compact computational models of spiking neural networks that exhibit short-term volatile memory and actively hold information until their energy source is switched off, seem not fully understood. Here we propose that small spiking neural circuit motifs may act as volatile memory components. A minimal motif consists of only two interconnected neurons – one self-connected excitatory neuron and one inhibitory neuron – and realizes a single-bit volatile memory. An excitatory, delayed self-connection promotes a bistable circuit in which a self-sustained periodic orbit generating spike trains co-exists with the quiescent state of no neuron spiking. Transient external inputs may straightforwardly induce switching between those states. Moreover, the inhibitory neuron may act as an autonomous turn-off switch. It integrates incoming excitatory pulses until a threshold is reached after which the inhibitory neuron emits a spike that then inhibits further spikes in the excitatory neuron, terminating the memory. Our results show how external bits of information (excitatory signal), can be actively held in memory for a pre-defined amount of time. We show that such memory operations are robust against parameter variations and exemplify how sequences of multidimensional input signals may control the dynamics of a many-bits memory circuit in a desired way.

Soft robots grew to prominence in large part because they promised a new and exciting means for engineers to develop robots that are both highly adaptable and safe for direct human interaction. However, despite showing substantial promise in this area, soft robots have not yet seen widespread adoption. Two major factors that have prevented the development of soft robots are the fundamental challenges of both the modeling and control of soft structures. While traditional robots enjoy a myriad of theoretical and computational tools for modeling and control, the options for soft robots are far more limited. In this work, we introduce a physics-based finite element simulation platform, Kraken, that can be used to accurately model the dynamic and oscillatory motions of soft robots. After a brief theoretical introduction to hyperelastic modeling and the finite element method, we show the utility of our approach by simulating the oscillations of a 1D hyperelastic actuator, the dynamics of a 2D hyperelastic pendulum, and a 3D spherical hyperelastic pendulum. We then demonstrate the accuracy of our approach by presenting the agreement of the simulated results with those obtained via physical experiments for three materials with different hyperelastic properties, with percent errors as low as 1%. Taken together, these results demonstrate that the aforementioned simulation platform is a critical step towards the fast and accurate simulation, prototyping, and control of soft robots.

Temporal network data are increasingly available in various domains, and often represent highly complex systems with intricate structural and temporal evolutions. Due to the difficulty of processing such complex data, it may be useful to coarse grain temporal network data into a numeric trajectory embedded in a low-dimensional space. We refer to such a procedure as temporal network embedding, which is distinct from procedures that aim at embedding individual nodes. Temporal network embedding is a challenging task because we often have access only to discrete time-stamped events between node pairs, and, in general, the events occur with irregular intervals, making the construction of the network at a given time a nontrivial question already. We propose a method to generate trajectories of temporal networks embedded in a low-dimensional space given a sequence of time-stamped events as input. We realize this goal by combining the landmark multidimensional scaling (LMDS), which is an out-of-sample extension of the well-known multidimensional scaling method, and the framework of tie-decay temporal networks. This combination enables us to obtain a continuous-time trajectory describing the evolution of temporal networks. We then study mathematical properties of the proposed temporal network embedding framework. Finally, we showcase the method with empirical data of social contacts to find temporal organization of contact events and loss of them over a single day and across different days.

This paper presents a pragmatic approach established on the hybridization of nature-inspired optimization algorithms and Bernstein Polynomials (BPs), achieving the optimum numeric solution for Nonlinear Optimal Control Problems (NOCPs) of dynamical systems. The approximated solution for NOCPs is obtained by the linear combination of BPs with unknown parameters. The unknown parameters are evaluated by the conversion of NOCP to an error minimization problem and the formulation of an objective function. The Fitness Dependent Optimizer (FDO) and Genetic Algorithm (GA) are used to solve the objective function, and subsequently the optimal values of unknown parameters and the optimum solution of NOCP are attained. The efficacy of the proposed technique is assessed on three real-world NOCPs, including Van der Pol (VDP) oscillator problem, Chemical Reactor Problem (CRP), and Continuous Stirred-Tank Chemical Reactor Problem (CSTCRP). The final results and statistical outcomes suggest that the proposed technique generates a better solution and surpasses the recently represented methods in the literature, which eventually verifies the efficiency and credibility of the recommended approach.

Modifying neural activity is a substantial goal in neuroscience that facilitates the understanding of brain functions and the development of medical therapies. Neurobiological models play an essential role, contributing to the understanding of the underlying brain dynamics. In this context, control systems represent a fundamental tool to provide a correct articulation between model stimulus (system inputs) and outcomes (system outputs). However, throughout the literature there is a lack of discussions on neurobiological models, from the formal control perspective. In general, existing control proposals applied to this family of systems, are developed empirically, without theoretical and rigorous framework. Thus, the existing control solutions, present clear and significant limitations. The focus of this work is to survey dynamical neurobiological models that could serve for closed-loop control schemes or for simulation analysis. Consequently, this paper provides a comprehensive guide to discuss and analyze control-oriented neurobiological models. It also provides a potential framework to adequately tackle control problems that could modify the behavior of single neurons or networks. Thus, this study constitutes a key element in the upcoming discussions and studies regarding control methodologies applied to neurobiological systems, to extend the present research and understanding horizon for this field. [ABSTRACT FROM AUTHOR]

Elastic Riemannian metrics have been used successfully for statistical treatments of functional and curve shape data. However, this usage suffers from a significant restriction: the function boundaries are assumed to be fixed and matched. In practice, functional data often comes with unmatched boundaries. It happens, for example, in dynamical systems with variable evolution rates, such as COVID-19 infection rate curves associated with different geographical regions. Here, we develop a Riemannian framework that allows for partial matching, comparing, and clustering of functions with phase variability and uncertain boundaries. We extend past work by (1) Defining a new diffeomorphism group $G$ G over the positive reals that is the semidirect product of a time-warping group and a time-scaling group; (2) Introducing a metric that is invariant to the action of $G$ G ; (3) Imposing a Riemannian Lie group structure on $G$ G to allow for an efficient gradient-based optimization for elastic partial matching; and (4) Presenting a modification that, while losing the metric property, allows one to control the amount of boundary disparity in the registration. We illustrate this framework by registering and clustering shapes of COVID-19 rate curves, identifying basic patterns, minimizing mismatch errors, and reducing variability within clusters compared to previous methods. [ABSTRACT FROM AUTHOR]

The properties of dynamic interactions in mobile-interactive systems are still difficult to analyze because of the complexity of systems. Thus, we have proposed a new Petri net called the variable petri net (VPN) recently, which specializes in describing dynamic interactions in systems. To make better use of VPN, this article focuses on the model checking method of VPN. It introduces the algorithm to transform a VPN to a Kripke structure that can describe both the system running states and the system connection states in VPN, and the method to transform a property to a temporal logic formula based on VPN and its Kripke structure. The Kripke structure can be optimized by considering the specific property about the system connection states and then be used to perform the targeted verification to the property by using a model checker. A practical example is given to demonstrate the proposed methods. [ABSTRACT FROM AUTHOR]

This study focuses on the quasisynchronization problem for reaction–diffusion neural networks (RDNNs) in the presence of deception attacks. Under deception attacks, a time–space sampled-data (TSSD) control mechanism is proposed for RDNNs. Compared with traditional control strategies, the proposed control mechanism can not only save network bandwidth but also improve the cybersecurity of communications. Inspired by Halanay’s inequality, a new inequality is proposed, which can be effectively applied to the quasisynchronization problem for dynamical systems. Then, by using this inequality and the Lyapunov functional approach, quasisynchronization criteria are set for RDNNs. The desired control gain is gained from solving a group of linear matrix inequalities. Moreover, in the absence of deception attacks, the exponential synchronization problem is studied for RDNNs. In the end, simulation results are given to demonstrate the usefulness of the theoretical analysis. [ABSTRACT FROM AUTHOR]

In the conventional sliding mode control, the four-leg distribution static compensator (DSTATCOM) currents are controlled based on a current error in each phase. However, the four currents in a four-wire system can not be independently controlled variables and hence one of the four controllers is redundant in conventional schemes. Further, the current dynamics of a DSTATCOM converter are coupled through converter pole voltages in the natural reference frame. This leads to cross-coupling in the sliding variable equations with respect to four manipulated input variables. Considering the above points, in this paper, the current due to Thevenin’s equivalent load neutral-point voltage is considered as a fourth independent controlled variable and the corresponding system dynamic equations are presented. To get a decoupled feature, a new sliding surface function is structured. The performance of a DSTATCOM with the proposed control scheme under various operating conditions is validated through a detailed simulation study and experimental results, obtained from a laboratory prototype of four-leg DSTATCOM. [ABSTRACT FROM AUTHOR]

The increasing penetration of distributed renewable energy sources drastically alters the dynamic characteristics of distribution networks (DNs). Therefore, several equivalent models have been recently proposed, to analyze more accurately the complex behavior of modern DNs. However, relatively simple models are still commonly used in practice for dynamic power system studies. In addition, dynamic equivalent models for DNs are sensitive to different operating conditions and there is lack of systematic understanding of their performance. Scope of this paper is to propose a methodology for identifying the applicability range in terms of accuracy and generalization capability of several conventional and newly developed equivalent models for the dynamic analysis of modern DNs. A set of metrics is used for the modelling accuracy assessment and a sensitivity analysis framework is introduced to fully quantify the generalization capabilities of DN equivalent models. Based on the above, guidelines and recommendations for the development of robust equivalent models for DN analysis are proposed. [ABSTRACT FROM AUTHOR]

The major concern for the single-phase grid-connected inverter system is the phase and frequency error of the phase-locked loop (PLL) under various grid disturbances. This paper employs an effective cascaded band pass filter (CBPF) to eliminate the effects of the DC offset and harmonics present in the grid voltage. However, the presence of a prefilter slowed the dynamic performance of all-pass filter (APF) based PLL, implied to generate the orthogonal signal in the single-phase system. To improve the dynamic performance along with the harmonics filtering capability, a nonlinear loop filter (LF) based PLL with CBPF is proposed. The nonlinear LF contains a nonlinear function applied in proportional-integral control to improve the dynamic response of the PLL. Under various grid disturbances, the proposed controller has a faster dynamic response with less overshoot. Furthermore, the proposed PLL is capable of eliminating the effects of DC offset and subharmonics. The proposed PLL is compared with different state-of-the-art PLLs to assess the performance under various grid disturbances. The efficacy is validated experimentally as well as through simulation results. [ABSTRACT FROM AUTHOR]

In this brief, a novel event-triggered intermittent-based impulsive control (ETIIC) strategy is designed to stabilize a class of nonlinear systems, which possesses the strengths of event-triggered control, intermittent control and impulsive control. Particularly, ETIIC allows that impulsive instants appear only at the control intervals. Moreover, the control intervals can be activated by mean of the well-designed event-triggered mechanism that depends explicitly on threshold-value and check-period. A sufficient criterion is provided to ensure exponential stabilization of nonlinear systems via ETIIC, meanwhile, the non-Zeno behavior is confirmed in our results. At last, an example with numerical simulation is presented to illustrate the feasibility of ETIIC and the effectiveness of our results. [ABSTRACT FROM AUTHOR]

This brief investigates the finite-time control problem for discrete-time positive systems subject to event-triggered scheme and Markov jump parameters. Switching transition probability is used to extend the fixed transition probability, which is more suitable to describe the performance of Markov jump systems (MJSs). On the basis of the 1-norm, a developed event-triggered scheme is proposed to be associated with the sample signal and the actual signal. Furthermore, a linear Lyapunov function and an average dwell time approach are adopted to obtain sufficient conditions for guaranteeing stochastic finite-time stability of the underlying systems. Additionally, a matrix decomposition strategy is constructed to design a finite-time event-triggered control law such that the corresponding MJSs are positive and stochastically finite-time bounded. All conditions are proposed on the basis of linear programming. Finally, a local railway transportation model is provided to verify the practicability of the strategy. [ABSTRACT FROM AUTHOR]

The fault detection issue is investigated for complex stochastic delayed systems in the presence of positivity constraints and semi-Markov switching parameters. By choosing a mode-dependent fault detection filter (FDF) as a residual generator, the corresponding fault detection is formulated as a positive $\mathscr {L}_{1}$ filter problem. Attention is focused on the design of a mode-dependent FDF to minimize the error between the residual signal and the fault signal. The designed FDF features good sensitivity of the faults and robustness against the external disturbances. Subsequently, by means of the linear copositive Lyapunov functional (LCLF), stochastic stability is proposed to satisfy an expected $\mathscr {L}_{1}$ -gain performance. Some solvability conditions for the desired mode-dependent FDF are established with the help of a linear programming approach. Finally, an application example of a data communication network model is provided to demonstrate the effectiveness of the theoretical findings. [ABSTRACT FROM AUTHOR]

The second-order scalar-weighted consensus problem of multiagent systems has been well explored. However, in some practical antagonistic interaction networks, the interdependencies of multidimensional states of the agents must be described by matrix coupling. In order to highlight the influence of matrix coupling in the antagonistic interaction network, we investigate the second-order matrix-weighted bipartite consensus problem on undirected structurally balanced signed networks. Under the proposed bipartite consensus protocol, an algebraic condition is obtained for achieving second-order bipartite consensus via utilizing matrix-valued Gauge transformation and stability theory. Then, using the obtained criteria, a more direct algebraic graph condition is given for reaching bipartite consensus. Besides, because of the existence of negative (positive) semidefinite connections, the matrix-weighted network may have clustering phenomena, which means that matrix weights play a critical role in achieving consensus. An algebraic graph condition for admitting cluster bipartite consensus is provided. By designing matrix weights in practical scenarios, the required number of clusters can be obtained. Finally, the theoretical results are verified by five simulation examples. [ABSTRACT FROM AUTHOR]

Dynamical systems coupled in the form of a complex networks have been exploited for modeling numerous real-world distributed systems. Due to the decentralized nature of dynamical networks, and inaccessibility or high control cost, in some applications, it is desired to employ pinning control techniques. A natural question that comes to mind is how one can select the proper pinned nodes and their feedback gains, (i.e., the performance metrics) to optimize the convergence rate to the homogeneous stationary state (i.e., the performance index) while the total pinning cost is kept below a given limit. This optimization problem has been converted into a semidefinite programming problem, and the optimal feedback gains have been derived analytically. Based on these results, for a given set of pinned nodes, an algorithm for determining the optimal feedback gains has been developed. Furthermore, it is shown that the edges between the pinned nodes do not impact the optimal results, and adding an edge between free nodes or between free and pinned nodes modifies the optimal convergence rate in nondecreasing order. Based on the derived analytical results, several interesting properties have been discovered. For symmetric networks, the nodes within a vertex orbit have the same optimal feedback gains, which are independent of the edges within each vertex orbit. For a number of topologies, closed-form formulas are provided for the optimal results. For a network of Chua systems, the impact of cut number is illustrated by comparing the optimal results for different topologies. [ABSTRACT FROM AUTHOR]

A description of electromagnetics by means of the reduced-basis method (RBM), which gives physical insight, is detailed. Contrary to what has been previously done to get further insights from the electromagnetic behavior, a reliable reduced-order model completely describing a microwave circuit in a frequency band of interest is carried out. Following a theoretical analysis starting from time-harmonic Maxwell’s equations, we identify the dominant contributions to electromagnetics in a band of analysis. Making use of the reliable reduced-order model, we present the electromagnetic information in that reduced-order model in a more insightful manner, using a specific dynamical system representation of electromagnetics. As a result, a full-wave coupling matrix completely describing electromagnetics in the band of analysis is identified, where no narrowband approximation is considered, such as it is the case in classical coupling matrix circuit theory. No approximation is taken into account other than carrying out the analysis in a given frequency band, which can be arbitrarily large. The proposed approach provides a way to understand electromagnetics, where a full-wave coupling matrix allows the description of electromagnetics in a band of interest as a simple physically insightful circuit, and not the other way around. Finally, several real-life microwave circuits, such as a dual-mode filter and diplexer, will illustrate the capabilities and efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]

The paper proposes a control scheme to improve the dynamic response of power systems through the automatic regulators of converter-based Distributed Energy Resources (DERs). In this scheme, both active and reactive power control of DERs are varied to regulate both frequency and voltage, as opposed to current practice where frequency and voltage controllers are decoupled. To assess the proposed control against the current state-of-art, the paper also defines a metric that captures the combined effect of frequency/voltage response at any given bus of the network. Results indicate that the proposed control strategy leads to a significant improvement in the stability and performance of the overall power system. These results are based on a comprehensive case study carried out by employing a modified version of the IEEE 39-bus benchmark system, where a portion of the synchronous machines is substituted by converter-interfaced DERs. The impact on the proposed control of load models, the $R/X$ ratio of network lines, as well as the level of DER penetration to the grid, are properly evaluated and conclusions are duly drawn. [ABSTRACT FROM AUTHOR]

Synchrophasor data provide unprecedented opportunities for inferring power system dynamics, such as estimating voltage angles, frequencies, and accelerations along with power injection at all buses. Aligned to this goal, this work puts forth a novel framework for learning dynamics after small-signal disturbances by leveraging Gaussian processes (GPs). We extend results on learning of a linear time-invariant system using GPs to the multi-input multi-output setup. This is accomplished by decomposing power system dynamics into a set of single-input single-output linear systems with narrow frequency pass bands. The proposed learning technique captures time derivatives in continuous time, accommodates data streams sampled at different rates, and can cope with missing data and heterogeneous levels of accuracy. While Kalman filter-based approaches require knowing all system inputs, the proposed framework handles readings of system inputs, outputs, their derivatives, and combinations thereof collected from an arbitrary subset of buses. Relying on minimal system information, it further provides uncertainty quantification in addition to point estimates of system dynamics. Numerical tests verify that this technique can infer dynamics at non-metered buses, impute and predict synchrophasors, and locate faults under linear and non-linear system models under ambient and fault disturbances. [ABSTRACT FROM AUTHOR]

Time Delay Reservoir (TDR) is a hardware-friendly machine learning approach from two perspectives. First, it can prevent the connection overhead of neural networks with increasing neurons. Second, through its dynamic system representation, TDR can also be implemented in hardware by different systems. However, it performs poorly on tasks that involve long-term dependency. In this work, we first introduce a higher-order delay unit, which is capable of accumulating and transferring the long history states in an adaptive manner to further enhance the reservoir memory. Particle Swarm Optimisation is applied to optimize the enhanced degree of memory adaptivity. Our experiments demonstrate its superiority both for short- and long-term memory datasets over seven existing approaches. In light of the hardware-friendly feature of TDR, we further propose a memristive implementation of our adaptive memory-enhanced TDR, where a dynamic memristor and the memristor-based delay element are applied to construct the reservoir. Through circuit simulation, the feasibility of our proposed memristive implementation is verified. The comparisons with different hardware reservoirs show that our proposed memristive implementation is effective both for short- and long-term memory datasets, while exhibiting benefits in terms of smaller circuit area and lower power consumption compared with traditional hardware reservoirs. [ABSTRACT FROM AUTHOR]

With the full development of intelligent mobile communications, wireless mixed reality (MR) provides a more visually immersive experience and stronger interaction with environments than virtual reality (VR) and augmented reality (AR). However, the asymmetric characteristic of wireless MR traffic creates a huge challenge to current mobile networks. Dynamic time division duplex (D-TDD) is considered as a promising technology to improve wireless MR users’ quality of experience (QoE) due to its potentials and advantages in delivering asymmetric traffic. Therefore, in this paper, we propose a QoE-driven distributed multidimensional resource allocation (MRA) supplemented by inter-cell interference (ICI) mitigation scheme for wireless MR in multi-cell D-TDD systems. First, to improve QoE of MR users, we formulate the joint optimization of subframe configuration, channel assignment and computation offloading as a mixed-integer nonlinear programming problem. A novel fully-decentralized multi-agent deep Q-network (DQN) algorithm is developed to solve the problem. Then, to mitigate ICI, a water filling based power control algorithm is investigated to minimize the total power of each small base station and its associated MR users. Simulation results demonstrate that our proposed scheme improves QoE of MR users in a realizable way as compared to existing schemes. [ABSTRACT FROM AUTHOR]

Convolutional and recurrent neural networks (RNNs) have been widely used to achieve state-of-the-art performance on classification tasks. However, it has also been noted that these networks can be manipulated adversarially with relative ease, by carefully crafted additive perturbations to the input. Though several experimentally established prior works exist on crafting and defending against attacks, it is also desirable to have rigorous theoretical analyses to illuminate conditions under which such adversarial inputs exist. This article provides both the theory and supporting experiments for real-time attacks. The focus is specifically on recurrent architectures and inspiration is drawn from dynamical systems’ theory to naturally cast this as a control problem, allowing dynamic computation of adversarial perturbations at each timestep of the input sequence, thus resembling a feedback controller. Illustrative examples are provided to supplement the theoretical discussions. [ABSTRACT FROM AUTHOR]

Perimeter control is to manipulate traffic flows in different regions through adjusting traffic signals at the border of the regions. Traditionally, the complete trips in a region are assumed to be dependent on the current accumulated vehicles in this region. This assumption is invalid because the vehicle needs time to complete its trip in a region. In order to solve this shortcoming, some recent studies have introduced a time-delay dynamical system to describe the dynamical behaviour of the accumulated vehicles in a region. However, in these studies, the perimeter control problem is formulated as a tracking problem with a given optimal reference point. In reality, such an optimal reference point is unavailable in advance. This paper will fill this gap through formulating perimeter control as an optimal control problem governed by a state-dependent time delay system. The control parametrization technique and an exact penalty method are introduced to solve such a challenging optimal control problem. Model predictive control is applied to obtain close-loop solution through solving a series of online optimal control problems. Some experiments are performed to demonstrate the effectiveness of our method. [ABSTRACT FROM AUTHOR]

This article presents a closed-form approach to constraining a flow within a given volume and around objects. The flow is guaranteed to converge and to stop at a single fixed point. The obstacle avoidance problem is inverted to enforce that the flow remains enclosed within a volume defined by a polygonal surface. We formally guarantee that such a flow will never contact the boundaries of the enclosing volume or obstacles. It asymptotically converges toward an attractor. We further create smooth motion fields around obstacles with edges (e.g., tables). Both obstacles and enclosures may be time-varying, i.e., moving, expanding, and shrinking. The technique enables a robot to navigate within enclosed corridors while avoiding static and moving obstacles. It was applied on an autonomous robot (QOLO) in a static complex indoor environment and tested in simulations with dense crowds. The final proof of concept was performed in an outdoor environment in Lausanne. The QOLO-robot successfully traversed a marketplace in the center of town in the presence of a diverse crowd with a nonuniform motion pattern. [ABSTRACT FROM AUTHOR]

This article reveals the power and frequency oscillation mechanism of the islanded cascaded-type virtual synchronous generators (VSGs), and proposes a decentralized mutual damping suppression method. In the proposed scheme, the mutual damping term is constructed via the decentralized manner with transmission line current. It contributes to increasing damping and accelerating the frequency convergence of cascaded-type VSGs in the dynamic process. As a result, the power and frequency oscillations are restrained, and the system dynamic performance is improved. Further, the system stability based on the Lyapunov energy function method is proved. Finally, the effectiveness of the proposed decentralized mutual damping control is verified by simulations and experimental results. [ABSTRACT FROM AUTHOR]

This paper proposes a feedback linearization controller for a grid-following inverter (GFLI) that uses a conventional Phase-Locked Loop (PLL) as the synchronization unit. The proposed controller enhances the GFLI synchronization by expanding the PLL domain of attraction to the whole plane that is limited to a small region around the equilibrium point in a conventional PLL, provided that the grid impedance and voltage are known. Linearizing the overall closed-loop system, the proposed controller provides linear system attributes for the PLL, leading to an infinite domain of attraction and only one equilibrium point. Additionally, a state-feedback controller is integrated within the feedback linearization controller so that it enables the system to have an adjustable dynamic response. Finally, it is shown that the system is robust against parametric uncertainties. The performance of the proposed control design is validated in Matlab/Simulink and experiment. It is verified that the proposed controller expands the domain of attraction and enhances the system dynamic response. [ABSTRACT FROM AUTHOR]

In this paper, we address the finite-time and fixed-time synchronization of a general class of switched neural networks (SNNs) with time delays subject to stochastic disturbances. Considering two types of switching in this class of SNNs: 1) intra-SNN state-dependent switching and 2) inter-SNN Markovian switching, we develop three control laws and derive three sets of sufficient conditions for both finite-time and fixed-time synchronization of SNNs subject to stochastic disturbances. We make two remarks on the effects of control-law parameters on synchronization settling time. Moreover, we derive several upper bounds of synchronization settling time and evaluate their pros and cons. Finally, we elaborate on two numerical examples to illustrate the viability of the theoretical results. [ABSTRACT FROM AUTHOR]

Control augmentation can significantly boost the performance of systems with human-in-the-loop. However, the benefit of such designs has yet been fully realized because many parameters of human internal vehicle models are inaccurate. Here, a control augmentation framework is studied to assist human operators in controlling a system to precisely follow desired commands. There are two steps involved in this framework: (1) a Hidden Markov Model based estimator for unknown parameters in a human internal vehicle model; and (2) a regulator based on the identified human internal vehicle model to reduce tracking errors. A general form of the human internal vehicle model is applied to describe the operator’s understanding about the system dynamics. A recursive, closed-form solution is derived for a class of dynamical systems so that the computational cost can be significantly reduced. The algorithm is validated in a simulated, pilot-in-the-loop quadrotor scenario. [ABSTRACT FROM AUTHOR]

This article investigates the trajectory tracking problem of underactuated mechanical systems (UMSs) with companion nonholonomic servo constraints and uncertainties. For such motion tasks, the existing approaches in the literature attempt unrealistically to furnish a reliable closed-form solution, rendering it difficult to have high-quality tracking performance with theoretical support. In addition, the uncertainties typically pose substantial difficulty in the controller synthesis. Here, by invoking the methodology of fuzzy sets, the uncertainties in the UMSs are elegantly represented; and with this, the formulation becomes such that a closer link between the uncertain dynamical model of the UMSs and the real world is established. The reference trajectories are regarded appropriately as servo constraints, and subsequently an adaptive robust controller is designed to accomplish the trajectory tracking task from a specific viewpoint of servo constraint tracking. As supported by rigorous proofs, the closed-form solution to the proposed controller is obtained with guaranteed Lyapunov stability. Leveraging on the closed-form solution, the global optimizer to the controller gain parameter can be determined, which is shown to exhibit several important properties including existence and uniqueness. Finally, a numerical example is presented to demonstrate the effectiveness of the designed method. [ABSTRACT FROM AUTHOR]

In this article an autonomous distributed-consensus system is evaluated with regard to its efficacy for establishing a common detection statistic among a network of passive acoustic hydrophones. The notion of consensus among sensors is briefly reviewed, and an algorithm motivated by diffusion of information among network neighbors is developed within the context of spectral graph theory. Relative entropy derived at each node provides information that the network averages to reach a joint detection decision. As the expected value of a log-likelihood ratio, relative entropy is the appropriate detection statistic for this application because it is nondimensional, additive, and insensitive to calibration. These properties facilitate the fusion of information among heterogeneous sensors. Seven nodes of an acoustical receiver network were moored on the New Jersey coastal shelf in May 2019, while towed and autonomous sources provided target signals at 900–1050 Hz. The convergence rate of the system to a detection statistic was found to be limited by slow acoustic communications, and convergence to a global consensus was often disrupted by communication breakdowns. When failures occurred, the network successfully demonstrated robustness by dissociating into viable subnetworks, each of which converged to a valid detection statistic. [ABSTRACT FROM AUTHOR]

In this paper, we provide sufficient conditions for dissipativity and local asymptotic stability of discrete-time dynamical systems parametrized by deep neural networks. We leverage the representation of neural networks as pointwise affine maps, thus exposing their local linear operators and making them accessible to classical system analytic and design methods. This allows us to “crack open the black box” of the neural dynamical system’s behavior by evaluating their dissipativity, and estimating their stationary points and state-space partitioning. We relate the norms of these local linear operators to the energy stored in the dissipative system with supply rates represented by their aggregate bias terms. Empirically, we analyze the variance in dynamical behavior and eigenvalue spectra of these local linear operators with varying weight factorizations, activation functions, bias terms, and depths.

Spatio-temporal forecasting is of great importance in a wide range of dynamic systems applications, such as earth science, transport planning, etc. These applications rely on accurate predictions of spatio-temporal structured data reflecting real-world phenomena. A stunning characteristic is that the dynamical system is not only driven by some physics laws but also impacted by the localized factor in spatial and temporal regions. One of the major challenges is to infer the underlying causes, which generate the perceived data stream and propagate the involved causal dynamics through the distributed observing units. Another challenge is that the success of machine learning-based predictive models requires massive annotated data for model training. However, the acquisition of high-quality annotated data is objectively manual and tedious as it needs a considerable amount of human intervention, making it infeasible in fields that require high levels of expertise. To tackle these challenges, we advocate a spatio-temporal physics-coupled neural networks (ST-PCNN) model to learn the underlying physics of the dynamical system and further couple the learned physics to assist the learning of the recurring dynamics. To deal with data-acquisition constraints, an active learning mechanism with Kriging for actively acquiring the most informative data is proposed for ST-PCNN training in a partially observable environment. Our experiments on both synthetic and real-world datasets exhibit that the proposed ST-PCNN with active learning converges to near-optimal accuracy with substantially fewer instances.

We reveal hidden social media machinery that has allowed misinformation to thrive among mainstream users, but which is missing from current policy discussions. Specifically, we show how mainstream parenting communities on Facebook have been subject to a powerful, two-pronged misinformation machinery during the pandemic, that has pulled them closer to extreme communities and their misinformation. The first prong involves a strengthening of the bond between mainstream parenting communities and pre-Covid conspiracy theory communities that promote misinformation about climate change, fluoride, chemtrails and 5G. Alternative health communities have acted as the critical conduits. The second prong features an adjacent core of tightly bonded, yet largely under-the-radar, anti-vaccination communities that continually supplied Covid-19 and vaccine misinformation to the mainstream parenting communities. Our findings show why Facebook’s own efforts to post reliable information about vaccines and Covid-19 have not been efficient; why targeting the largest communities does not work; and how this machinery could generate new pieces of misinformation perpetually. We provide a simple yet exactly solvable mathematical theory for the system’s dynamics. It predicts a new strategy for controlling mainstream community tipping points. Our conclusions should be applicable to any social media platform with in-built community features, and open up a new engineering approach to addressing online misinformation and other harms at scale.

This letter proposes a simple and inexpensive control of distributed energy resources aimed at improving the power system dynamic performance. The rationale behind the proposed control relies on a recent interpretation of the frequency in the differential geometry framework. A comparison with well-established controls in terms of eigensensitivity and time-domain performance is carried out to show the effectiveness of the proposed control. [ABSTRACT FROM AUTHOR]

This article proposes an improved multiple synchronous reference frame (MSRF) current harmonic regulation strategy for dual three phase permanent magnet synchronous machine drive system. A new vector space decomposition transformation combined with auxiliary currents is designed to decouple the current fundamental with current harmonics, and the auxiliary currents can be established by the π/9 phase shifting of the physical currents in ABC set and the π/18 phase shifting of the physical currents in XYZ set. By employing the proposed current decomposition, it is easy to detect them using low pass filters in MSRFs. A generic complex vector proportional integral regulator is then employed to control the detected current harmonics to track the current harmonic references. Compared with the existing MSRF-based methods, the proposed method provides a new current harmonic detection with reduced delay effect, which contributes to enhancing the dynamic performance of current harmonic regulation, and also stabilizing the control system. Comprehensive experimental results are given to validate the effectiveness of the proposed control strategy. [ABSTRACT FROM AUTHOR]

For safety critical transportation applications like all-electric ship propulsion systems (SPS), the reliable system operation is definitely the primary concern. In this article, a fault-tolerant back-to-back power converter based on the four-switch three-phase (FSTP) topology is applied to a doubly fed induction machine (DFIM) based all-electric SPS. A power compensation-oriented space vector modulation direct power control (PCSVM-DPC) strategy with dc-bus voltage unbalance elimination is proposed to ride through the single bridge arm open-circuit fault in the source-side converter (SSC) and improve the system dynamic performance simultaneously. Besides, the DFIM-SPS is endowed with excellent steady-state performance by applying the proposed control strategy. The dc-bus voltage deviation is suppressed based on the calculation of active and reactive power compensation terms to increase the dc-bus voltage utilization rate. Moreover, good post-fault system operation performance can be achieved with fast active power and electromagnetic torque tracking speeds. The proposed PCSVM-DPC strategy for the fault-tolerant FSTP SSC based DFIM-SPS is easy to implement without high computational burden. The SVM-DPC method is also presented for the load-side converter. The proposed control strategy is verified by experiments on a 1.5-kW DFIM-SPS experimental platform. [ABSTRACT FROM AUTHOR]

This article considered the persistent real-time localization problem of dynamic multiagent systems with repetitive running characteristics under directed graph. The trajectory length of the agent is randomly varying caused by system constraints and external environment. A novel distributed iterative learning localization estimation method with full historical average data compensation is designed. The average value of all available historical data in the previous operations is used to compensate the incomplete trajectory. The designed diagnosis mechanism and search mechanism are used to determine whether the agent has stopped running and to screen out the available historical data. In order to reduce the computation burden, an improved distributed localization algorithm with receding horizon historical average data compensation is proposed. The asymptotic convergence of the estimation algorithms in the sense of mathematical expectation is derived through the rigorous analysis. Meanwhile, the influence of the incomplete repetitive trajectory of the agent on estimation error and convergence rate is also analyzed. The experimental result based on QBot-2e robot platform verifies the realizability of the proposed method. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) are being actively adopted as a solution to sustainable transportation. However, a bottleneck remains with charging, where two of the main problems are the long charging time and the range anxiety of EV drivers. In this research, we investigate the deployment of dynamic charging systems, i.e., electrified roads that wirelessly charge EVs on the go, with a view to accelerating EV adoption rate. We propose a traffic-based deployment strategy, statistically quantify its impact, and apply the strategy to two case studies of real traffic in New York City (USA) and Xi’an (China). We find that our analytical estimates not only closely match the real data, but they also suggest that dynamic charging considerably extends the driving range of popular EV models in urban mobility. For example, when only 5% of the existing roads in New York City are equipped with this technology, an EV model such as the Nissan Leaf will approximately maintain its battery level without stopping to recharge. If the percentage of charging roads is increased to 10%, then the Leaf will gain nearly 10% of its battery after every 40 kilometers of driving. Our framework provides a solution to public and private organizations that support and facilitate vehicle electrification through charging infrastructure. [ABSTRACT FROM AUTHOR]

Nonlinear stochastic modeling is useful for describing complex engineering systems. Meanwhile, neuromorphic (brain-inspired) computing paradigms are being developed to tackle tasks that are challenging and resource-intensive on digital computers. An emerging scheme is reservoir computing, which exploits nonlinear dynamical systems for temporal information processing. This brief introduces reservoir computers with output feedback as stationary and ergodic infinite-order nonlinear autoregressive models. We highlight the versatility of this approach by employing classical and quantum reservoir computers to model synthetic and real datasets, further exploring their potential for control applications. [ABSTRACT FROM AUTHOR]

Diabetes is a widespread disease characterized by chronic hyperglycemia so that diabetic individuals usually require the administration of exogenous insulin for survival. As a consequence, in the context of the so-called artificial pancreas, many glucose control methods have been presented in the last few years. In this work, we focus on type-2 diabetes and propose a novel model-based glucose control technique based on the use of symbolic models, which are finite approximations of complex dynamical systems. This framework allows taking into account nonlinearities and delays in the dynamics, uncertainties, and input bounds, as well as nonidealities coming from the interaction between physical plant and digital environment. The methodology is extensively validated over a virtual patient model, broadly accepted as a substitute to animal trials in the preclinical testing of closed-loop glucose control strategies. The results show the effectiveness and the robustness of the approach. [ABSTRACT FROM AUTHOR]

Picking up objects and tossing them on a conveyor belt are activities generated on a daily basis in industry. These tasks are still done largely by humans. This article proposes a unified motion generator for a bimanual robotic system that enables two seven-degree-of-freedom robotic arms to grab and toss an object in one swipe. Unlike classical approaches that grab the object with quasi-zero contact velocity, the proposed approach is able to grasp the object while in motion. We control the contact forces prior to and following impact so as to stabilize the robots’ grip on the object. We show that such swift grasping speeds up the pick-and-place process and reduces energy expenditure for tossing. Continuous control of the reach, grab, and toss motion is achieved by combining a sequence of time-invariant dynamical systems (DSs) in a single control framework. We introduce a state-dependent modulation function to control the generated velocity in different directions. The framework is validated in simulation and on a real dual-arm system. We show that we can precisely toss objects within a workspace of ${0.2}\,{\times}\,{0.4}{\text{m}}^{2}$. Moreover, we show that the algorithm can adapt on-the-fly to changes in object location. [ABSTRACT FROM AUTHOR]

Complex dynamical systems rely on the correct deployment and operation of numerous components, with state-of-the-art methods relying on learning-enabled components in various stages of modeling, sensing, and control at both offline and online levels. This article addresses the runtime safety monitoring problem of dynamical systems embedded with neural-network components. A runtime safety state estimator in the form of an interval observer is developed to construct the lower bound and upper bound of system state trajectories in runtime. The developed runtime safety state estimator consists of two auxiliary neural networks derived from the neural network embedded in dynamical systems, and observer gains to ensure the positivity, namely, the ability of the estimator to bound the system state in runtime, and the convergence of the corresponding error dynamics. The design procedure is formulated in terms of a family of linear programming feasibility problems. The developed method is illustrated by a numerical example and is validated with evaluations on an adaptive cruise control system. [ABSTRACT FROM AUTHOR]

A correlation between fuzzy logic systems (FLS) and zeroing neural networks (ZNN) design is investigated. It is shown that the gain parameter included in ZNN design can be dynamically adjusted over time by means of an appropriate value derived as the output of a properly defined FLS, which includes appropriately defined membership functions and fuzzy logic rules. Dynamical systems which are applicable to time-varying rank-deficient matrices are proposed. Convergence properties are investigated and illustrative simulation experiments are performed. Presented simulation experiments confirm the superiority of the FLS proposed in this article with respect to previously proposed FLS for dynamic adjustment of gain parameters. Furthermore, the superiority of the FLS-based ZNN model over the corresponding ZNN models based on the classical approach in defining the varying-gain parameter is demonstrated. [ABSTRACT FROM AUTHOR]

The existing prescribed performance control (PPC) strategies exhibit the fragility and nonguarantee of the prescribed performance when they are applied to dynamic systems with actuator saturation, and moreover, all of them are unable to quantitatively design prescribed performance. This article aims at remedying those deficiencies by proposing a new nonfragile PPC method for waverider vehicles (WVs) such that the quantitative prescribed performance can be guaranteed for tracking errors in the presence of actuator saturation. First, readjusting performance functions are developed to achieve quantitative prescribed performance and prevent the fragile problem. Then, low-complexity fuzzy neural control protocols are presented for velocity subsystem and altitude subsystem of WVs, while there is no need of recursive back-stepping design. Furthermore, auxiliary systems are designed to generate effective compensations on control constraints, which contributes to the guarantee of the desired prescribed performance, being proved via Lyapunov synthese. Finally, compared simulation results are given to validate the superiority. [ABSTRACT FROM AUTHOR]

To evaluate the stochastic responses of a newly designed virtual track train (VTT), a spatial dynamics model of VTT is developed theoretically in the paper. The structural characteristics of VTT; tyre-road dynamic interactions and road geometric random irregularity are considered. Additionally, a human-seat subsystem is built and integrated into the VTT system. This comprehensive dynamics model of the VTT enables detailed investigations on the system dynamic performance under complicated excitations. The Monte Carlo method is further adopted to assess the stochastic vibration characteristics of the VTT excited by random road irregularity. The results indicate that the vibration characteristics of the VTT are dominated by road irregularity. The lateral vibration of different carbodies of the VTT has little difference, while the vertical vibration of carbody 2 is worse than carbodies 1 and 3. Besides, the running speeds have significant influences on the running stability and ride comfort of the VTT. The increasing running speed and road irregularity level significantly increase the probability of running stability and ride comfort exceeding the threshold, which affects the operation of VTT. Furthermore, the proposed model is capable of being used for dynamics-related assessment and parameter optimisation design. [ABSTRACT FROM AUTHOR]