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Shared autonomous electric vehicles can provide on-demand transportation for passengers while also interacting extensively with the electric distribution system. This interaction is especially beneficial after a disaster when the large battery capacity of the fleet can be used to restore critical electric loads. We develop a dispatch policy that balances the need to continue serving passengers (especially critical workers) and the ability to transfer energy across the network. The model predictive control policy tracks both passenger and energy flows and provides maximum passenger throughput if any policy can. The resulting mixed integer linear programming problem is difficult to solve for large-scale problems, so a distributed solution approach is developed to improve scalability, privacy, and resilience. We demonstrate that the proposed heuristic, based on the alternating direction method of multipliers, is effective in achieving near-optimal solutions quickly. The dispatch policy is examined in simulation to demonstrate the ability of vehicles to balance these competing objectives with benefits to both systems. Finally, we compare several dispatch behaviors, demonstrating the importance of including operational constraints and objectives from both the transportation and electric systems in the model.

Data-driven modeling for dynamic systems has gained widespread attention in recent years. Its inverse formulation, parameter estimation, aims to infer the inherent model parameters from observations. However, parameter degeneracy, where different combinations of parameters yield the same observable output, poses a critical barrier to accurately and uniquely identifying model parameters. In the context of WECC composite load model (CLM) in power systems, utility practitioners have observed that CLM parameters carefully selected for one fault event may not perform satisfactorily in another fault. Here, we innovate a joint conditional diffusion model-based inverse problem solver (JCDI), that incorporates a joint conditioning architecture with simultaneous inputs of multi-event observations to improve parameter generalizability. Simulation studies on the WECC CLM show that the proposed JCDI effectively reduces uncertainties of degenerate parameters, thus the parameter estimation error is decreased by 42.1% compared to a single-event learning scheme. This enables the model to achieve high accuracy in predicting power trajectories under different fault events, including electronic load tripping and motor stalling, outperforming standard deep reinforcement learning and supervised learning approaches. We anticipate this work will contribute to mitigating parameter degeneracy in system dynamics, providing a general parameter estimation framework across various scientific domains.

In this paper, we provide a tutorial overview and an extension of a recently developed framework for data-driven control of unknown nonlinear systems with rigorous closed-loop guarantees. The proposed approach relies on the Koopman operator representation of the nonlinear system, for which a bilinear surrogate model is estimated based on data. In contrast to existing Koopman-based estimation procedures, we state guaranteed bounds on the approximation error using the stability- and certificate-oriented extended dynamic mode decomposition (SafEDMD) framework. The resulting surrogate model and the uncertainty bounds allow us to design controllers via robust control theory and sum-of-squares optimization, guaranteeing desirable properties for the closed-loop system. We present results on stabilization both in discrete and continuous time, and we derive a method for controller design with performance objectives. The benefits of the presented framework over established approaches are demonstrated with a numerical example.

The safety monitoring for nonlinear dynamical systems with embedded neural network components is addressed in this paper. The interval-observer-based safety monitor is developed consisting of two auxiliary neural networks derived from the neural network components of the dynamical system. Due to the presence of nonlinear activation functions in neural networks, we use quadratic constraints on the global sector to abstract the nonlinear activation functions in neural networks. By combining a quadratic constraint approach for the activation function with Lyapunov theory, the interval observer design problem is transformed into a series of quadratic and linear programming feasibility problems to make the interval observer operate with the ability to correctly estimate the system state with estimation errors within acceptable limits. The applicability of the proposed method is verified by simulation of the lateral vehicle control system.

In this paper, a data-driven approach is developed for controller design for a class of discrete-time large-scale systems, where a large-scale system can be expressed in an equivalent data-driven form and the decentralized controllers can be parameterized by the data collected from its subsystems, i.e., system state, control input, and interconnection input. Based on the developed data-driven method and the Lyapunov approach, a data-driven semi-definite programming problem is constructed to obtain decentralized stabilizing controllers. The proposed approach has been validated on a mass-spring chain model, with the significant advantage of avoiding extensive modeling processes.

This paper proposes a neural network hybrid modeling framework for dynamics learning to promote an interpretable, computationally efficient way of dynamics learning and system identification. First, a low-level model will be trained to learn the system dynamics, which utilizes multiple simple neural networks to approximate the local dynamics generated from data-driven partitions. Then, based on the low-level model, a high-level model will be trained to abstract the low-level neural hybrid system model into a transition system that allows Computational Tree Logic Verification to promote the model's ability with human interaction and verification efficiency.

This paper presents a confidence level-based distributionally information gap decision theory (CL-DIGDT) framework for the two-stage robust optimal operation of distribution networks, aiming at deriving an optimal operational scheme capable of addressing uncertainties related to renewable energy and load demands. Building on conventional IGDT, the proposed framework utilizes the confidence level to capture the asymmetric characteristics of uncertainties and maximize the risk-averse capability of the solution in a probabilistic manner. To account for the probabilistic consideration, the imprecise Dirichlet model is employed to construct the ambiguity sets of uncertainties, reducing reliance on precise probability distributions. Consequently, a two-stage robust optimal operation model for distribution networks using CL-DIGDT is developed. An iterative method is proposed to solve the model and determine the upper and lower bounds of the objective function. Case study demonstrates that the proposed approach yields a more robust and statistically optimized solution with required accuracy compared to existing method, contributing to a reduction in first-stage cost by 0.84%, second-stage average cost by 6.7%, and significantly increasing the reliability of the solution by 8%.

The control of large-scale cyber-physical systems requires optimal distributed policies relying solely on limited communication with neighboring agents. However, computing stabilizing controllers for nonlinear systems while optimizing complex costs remains a significant challenge. Neural Networks (NNs), known for their expressivity, can be leveraged to parametrize control policies that yield good performance. However, NNs' sensitivity to small input changes poses a risk of destabilizing the closed-loop system. Many existing approaches enforce constraints on the controllers' parameter space to guarantee closed-loop stability, leading to computationally expensive optimization procedures. To address these problems, we leverage the framework of port-Hamiltonian systems to design continuous-time distributed control policies for nonlinear systems that guarantee closed-loop stability and finite $\mathcal{L}_2$ or incremental $\mathcal{L}_2$ gains, independent of the optimzation parameters of the controllers. This eliminates the need to constrain parameters during optimization, allowing the use of standard techniques such as gradient-based methods. Additionally, we discuss discretization schemes that preserve the dissipation properties of these controllers for implementation on embedded systems. The effectiveness of the proposed distributed controllers is demonstrated through consensus control of non-holonomic mobile robots subject to collision avoidance and averaged voltage regulation with weighted power sharing in DC microgrids., Comment: The paper has 15 pages, and has been submitted for a possible publication. arXiv admin note: text overlap with arXiv:2403.17785

Having a better understanding of how locational marginal prices (LMPs) change helps in price forecasting and market strategy making. This paper investigates the fundamental distribution of the congestion part of LMPs in high-dimensional Euclidean space using an unsupervised approach. LMP models based on the lossless and lossy DC optimal power flow (DC-OPF) are analyzed to show the overlapping subspace property of the LMP data. The congestion part of LMPs is spanned by certain row vectors of the power transfer distribution factor (PTDF) matrix, and the subspace attributes of an LMP vector uniquely are found to reflect the instantaneous congestion status of all the transmission lines. The proposed method searches for the basis vectors that span the subspaces of congestion LMP data in hierarchical ways. In the bottom-up search, the data belonging to 1-dimensional subspaces are detected, and other data are projected on the orthogonal subspaces. This procedure is repeated until all the basis vectors are found or the basis gap appears. Top-down searching is used to address the basis gap by hyperplane detection with outliers. Once all the basis vectors are detected, the congestion status can be identified. Numerical experiments based on the IEEE 30-bus system, IEEE 118-bus system, Illinois 200-bus system, and Southwest Power Pool are conducted to show the performance of the proposed method., Comment: Paper accepted for IEEE Transactions on Smart Grid. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses

It is recognized that the control of mixed-autonomy platoons comprising connected and automated vehicles (CAVs) and human-driven vehicles (HDVs) can enhance traffic flow. Among existing methods, Multi-Agent Reinforcement Learning (MARL) appears to be a promising control strategy because it can manage complex scenarios in real time. However, current research on MARL-based mixed-autonomy platoon control suffers from several limitations. First, existing MARL approaches address safety by penalizing safety violations in the reward function, thus lacking theoretical safety guarantees due to the black-box nature of RL. Second, few studies have explored the cooperative safety of multi-CAV platoons, where CAVs can be coordinated to further enhance the system-level safety involving the safety of both CAVs and HDVs. Third, existing work tends to make an unrealistic assumption that the behavior of HDVs and CAVs is publicly known and rationale. To bridge the research gaps, we propose a safe MARL framework for mixed-autonomy platoons. Specifically, this framework (i) characterizes cooperative safety by designing a cooperative Control Barrier Function (CBF), enabling CAVs to collaboratively improve the safety of the entire platoon, (ii) provides a safety guarantee to the MARL-based controller by integrating the CBF-based safety constraints into MARL through a differentiable quadratic programming (QP) layer, and (iii) incorporates a conformal prediction module that enables each CAV to estimate the unknown behaviors of the surrounding vehicles with uncertainty qualification. Simulation results show that our proposed control strategy can effectively enhance the system-level safety through CAV cooperation of a mixed-autonomy platoon with a minimal impact on control performance.

The installation of high-capacity fast chargers for electric vehicles (EVs) is posing a significant risk to the distribution grid as the increased demand from widespread residential EV charging could exceed the technical limits of the distribution system. Addressing this issue is critical, given that current infrastructure upgrades to enhance EV hosting capacity are both costly and time-consuming. Moreover, the inherent uncertainties associated with EV charging parameters make it challenging for power utilities to accurately assess the impact of EVs added to specific locations. To address these knowledge gaps, this study (a) introduces an algorithm to coordinate residential EV charging, and (b) proposes a comprehensive framework that evaluates all transformers within a feeder. The proposed method is applied to a real-world feeder, which includes 120 transformers of varying capacities. The results demonstrate that this approach effectively manages a substantial number of EVs without overloading any of the transformers, while also pinpointing locations that must be prioritized for future upgrades. This framework can serve as a valuable reference for utilities when conducting distribution system evaluations for supporting the growing EV penetration.

The implementation of cyber-physical systems in real-world applications is challenged by safety requirements in the presence of sensor threats. Most cyber-physical systems, in particular the vulnerable multi-sensor systems, struggle to detect the attack in observation signals. In this paper, we tackle this issue by proposing a Gaussian-Bernoulli Secure (GBS) estimator, which effectively transforms the assessment of sensor status into an optimal estimation problem concerning the system state and observation indicators. It encompasses two theoretical sub-problems: sequential state estimation with partial observations and estimation updates with disordered new observations. Within the framework of Kalman filter, we derive closed-form solutions for these two issues. However, due to their computational inefficiency, we propose the iterative approach employing proximal gradient descent to accelerate the estimation update. We conduct comprehensive experiments from three perspectives: computational efficiency, detection and estimation performance, and characterization of observation error. Our GBS estimator shows the improvements compared to other methods.

We study resilient leader-follower consensus of multi-agent systems (MASs) in the presence of adversarial agents, where agents' communication is modeled by time-varying topologies. The objective is to develop distributed algorithms for the nonfaulty/normal followers to track an arbitrary reference value propagated by a set of leaders while they are in interaction with the unknown adversarial agents. Our approaches are based on the weighted mean subsequence reduced (W-MSR) algorithms with agents being capable to communicate with multi-hop neighbors. Our algorithms can handle agents possessing first-order and second-order dynamics. Moreover, we characterize necessary and sufficient graph conditions for our algorithms to succeed by the novel notion of jointly robust following graphs. Our graph condition is tighter than the sufficient conditions in the literature when agents use only one-hop communication (without relays). Using multi-hop relays, we can enhance robustness of leader-follower networks without increasing communication links and obtain further relaxed graph requirements for our algorithms to succeed. Numerical examples are given to verify the efficacy of our algorithms., Comment: 15 pages

This paper addresses the challenge of edge caching in dynamic environments, where rising traffic loads strain backhaul links and core networks. We propose a Proximal Policy Optimization (PPO)-based caching strategy that fully incorporates key file attributes such as size, lifetime, importance, and popularity, while also considering random file request arrivals, reflecting more realistic edge caching scenarios. In dynamic environments, changes such as shifts in content popularity and variations in request rates frequently occur, making previously learned policies less effective as they were optimized for earlier conditions. Without adaptation, caching efficiency and response times can degrade. While learning a new policy from scratch in a new environment is an option, it is highly inefficient and computationally expensive. Thus, adapting an existing policy to these changes is critical. To address this, we develop a mechanism that detects changes in content popularity and request rates, ensuring timely adjustments to the caching strategy. We also propose a transfer learning-based PPO algorithm that accelerates convergence in new environments by leveraging prior knowledge. Simulation results demonstrate the significant effectiveness of our approach, outperforming a recent Deep Reinforcement Learning (DRL)-based method.

Molecular communication (MC) in microfluidic channels faces significant challenges in signal detection due to the stochastic nature of molecule propagation and dynamic, noisy environments. Conventional detection methods often struggle under varying channel conditions, leading to high bit error rates (BER) and reduced communication efficiency. This paper introduces ART-Rx, a novel Adaptive Real-Time Threshold Receiver for MC that addresses these challenges. Implemented within a conceptual system-on-chip (SoC), ART-Rx employs a Proportional-Integral-Derivative (PID) controller to dynamically adjust the detection threshold based on observed errors in real time. Comprehensive simulations using MATLAB and Smoldyn compare ART-Rx's performance against a statistically optimal detection threshold across various scenarios, including different levels of interference, concentration shift keying (CSK) levels, flow velocities, transmitter-receiver distances, diffusion coefficients, and binding rates. The results demonstrate that ART-Rx significantly outperforms conventional methods, maintaining consistently low BER and bit error probabilities (BEP) even in high-noise conditions and extreme channel environments. The system exhibits exceptional robustness to interference and shows the potential to enable higher data rates in CSK modulation. Furthermore, because ART-Rx is effectively adaptable to varying environmental conditions in microfluidic channels, it offers a computationally efficient and straightforward approach to enhance signal detection in nanoscale communication systems. This approach presents a promising control theory-based solution to improve the reliability of data transmission in practical MC systems, with potential applications in healthcare, brain-machine interfaces (BMI), and the Internet of Bio-Nano Things (IoBNT)., Comment: 14 pages, 7 figures, submitted to IEEE Transactions on Molecular, Biological, and Multi-Scale Communications (TMBMC)

This paper explores the use of autonomous unmanned vehicles for supporting power grid operations. With built-in batteries and the capability to carry additional battery energy storage, the rising number of autonomous vehicles can represent a substantial amount of capacity that is currently underutilized in the power grid. Unlike traditional electric vehicles which require drivers, the operations of autonomous vehicles can be performed without human intervention. To guide idle vehicles to support power grids autonomously, we propose a tractable optimization-based method for effectively integrating these ``mobile batteries'' into grid operations. During real-time operations, the vehicles are strategically routed to target locations to help maintain system power balance and reduce operating costs. Numerical studies have confirmed both the validity and scalability of the proposed algorithm for efficiently integrating autonomous vehicles into routine power system operations.

Proprietary closed-source software is still the norm in advanced process control. Transparency and reproducibility are key aspects of scientific research. Free and open-source toolkit can contribute to the development, sharing and advancement of new and efficient control approaches, and the industrial sector will certainly benefit from them. This paper presents ModelPredictiveControl.jl, an open-source software package for designing model predictive controllers in the Julia programming language. It is designed to be easy to use and modular, while providing advanced features like nonlinear control and moving horizon estimation. It relies on powerful control system and mathematical optimization frameworks to simplify the construction and testing of state estimators and predictive controllers. It also integrates with the standard plotting library to quickly visualize closed-loop data. The paper presents the main functionalities and illustrates them with two case studies in simulation. The first example is a continuously stirred tank reactor described by linear dynamics. The second one implements a nonlinear, an economic, and a successive linearization model predictive controllers for an inverted pendulum. The solving times are benchmarked against equivalent implementations in MATLAB to show the efficiency of the package., Comment: 11 pages, 11 figures, 1 table

Pandora temporal fault tree, as one notable extension of the fault tree, introduces temporal gates and temporal laws. Pandora Temporal Fault Tree(TFT) enhances the capability of fault trees and enables the modeling of system failure behavior that depends on sequences. The calculation of system failure probability in Pandora TFT relies on precise probabilistic information on component failures. However, obtaining such precise failure data can often be challenging. The data may be uncertain as historical records are used to derive failure data for system components. To mitigate this uncertainty, in this study, we proposed a method that integrates fuzzy set theory with Pandora TFT. This integration aims to enable dynamic analysis of complex systems, even in cases where quantitative failure data of components is unreliable or imprecise. The proposed work introduces the development of Fuzzy AND, Fuzzy OR, Fuzzy PAND, and Fuzzy POR logic gates for Pandora TFT. We also introduce a fuzzy importance measure for criticality analysis of basic events. All events in our analysis are assumed to have exponentially distributed failures, with their failure rates represented as triangular fuzzy numbers. We illustrate the proposed method through a case study of the Aircraft Fuel Distribution System (AFDS), highlighting its practical application and effectiveness in analyzing complex systems. The results are compared with existing results from Petri net and Bayesian network techniques to validate the findings.

This chapter contains a self-contained introduction to the significance of Bayes' formula in the context of nonlinear filtering problems. Both discrete-time and continuous-time settings of the problem are considered in a unified manner. In control theory, the focus on optimization-based solution approaches is stressed together with a discussion of historical developments in this area (from 1960s onwards). The heart of this chapter contains a presentation of a novel optimal transportation formulation for the Bayes formula (developed recently by the first author) and its relationship to some of the prior joint work (feedback particle filter) from the authors. The presentation highlights how optimal transportation theory is leveraged to overcome some of the numerical challenges of implementing Bayes' law by enabling the use of machine learning (ML) tools.

We consider a class of Wasserstein distributionally robust Nash equilibrium problems, where agents construct heterogeneous data-driven Wasserstein ambiguity sets using private samples and radii, in line with their individual risk-averse behaviour. By leveraging relevant properties of this class of games, we show that equilibria of the original seemingly infinite-dimensional problem can be obtained as a solution to a finite-dimensional Nash equilibrium problem. We then reformulate the problem as a finite-dimensional variational inequality and establish the connection between the corresponding solution sets. Our reformulation has scalable behaviour with respect to the data size and maintains a fixed number of constraints, independently of the number of samples. To compute a solution, we leverage two algorithms, based on the golden ratio algorithm. The efficiency of both algorithmic schemes is corroborated through extensive simulation studies on an illustrative example and a stochastic portfolio allocation game, where behavioural coupling among investors is modeled., Comment: 14 pages, 5 figures

Disturbance rejection in high-precision control applications can be significantly improved upon via online convex optimization (OCO). This includes classical techniques such as recursive least squares (RLS) and more recent, regret-based formulations. However, these methods can cause instabilities in the presence of model uncertainty. This paper introduces a safety filter for systems with OCO in the form of adaptive finite impulse response (FIR) filtering to ensure robust disturbance rejection. The safety filter enforces a robust stability constraint on the FIR coefficients while minimally altering the OCO command in the $\infty$-norm cost. Additionally, we show that the induced $\ell_\infty$-norm allows for easy online implementation of the safety filter by directly limiting the OCO command. The constraint can be tuned to trade off robustness and performance. We provide a simple example to demonstrate the safety filter., Comment: Submitted to the 2025 European Control Conference. This paper builds on the work done in arXiv:2405.07037

Despite modular conditions to guarantee stability for large-scale systems have been widely studied, few methods are available to tackle the case of networks with multiple equilibria. This paper introduces small-gain like sufficient conditions for 2-contraction of large-scale interconnected systems on the basis of a family of upper-bounds to the $L_2$ gains that arise from the gains computed on individual channels of the second additive variational equation. Such a condition guarantee the 2-additive compound of the system's Jacobian to be exponentially contractive, thus implying convergence towards equilibria of the system's solutions. The gains are obtained by solving suitable Linear Matrix Inequalities. Three interconnected Thomas' systems are considered in order to illustrate the application of the theory and the degree of conservatism.

While general-purpose computing follows Von Neumann's architecture, the data movement between memory and processor elements dictates the processor's performance. The evolving compute-in-memory (CiM) paradigm tackles this issue by facilitating simultaneous processing and storage within static random-access memory (SRAM) elements. Numerous design decisions taken at different levels of hierarchy affect the figure of merits (FoMs) of SRAM, such as power, performance, area, and yield. The absence of a rapid assessment mechanism for the impact of changes at different hierarchy levels on global FoMs poses a challenge to accurately evaluating innovative SRAM designs. This paper presents an automation tool designed to optimize the energy and latency of SRAM designs incorporating diverse implementation strategies for executing logic operations within the SRAM. The tool structure allows easy comparison across different array topologies and various design strategies to result in energy-efficient implementations. Our study involves a comprehensive comparison of over 6900+ distinct design implementation strategies for EPFL combinational benchmark circuits on the energy-recycling resonant compute-in-memory (rCiM) architecture designed using TSMC 28 nm technology. When provided with a combinational circuit, the tool aims to generate an energy-efficient implementation strategy tailored to the specified input memory and latency constraints. The tool reduces 80.9% of energy consumption on average across all benchmarks while using the six-topology implementation compared to baseline implementation of single-macro topology by considering the parallel processing capability of rCiM cache size ranging from 4KB to 192KB.

This study investigates remote synchronization in scale-free networks of coupled nonlinear oscillators inspired by synchronization observed in the brain's cortical regions and power grid. We employ the Master Stability Function (MSF) approach to analyze network stability across various oscillator models. Synchronization results are obtained for a star network using linearization techniques and extended to arbitrary networks with benchmark oscillators, verifying consistent behavior. Stable synchronous solutions emerge as the Floquet multiplier decreases and the MSF becomes negative. Additionally, we demonstrate remote synchronization in a star network, where peripheral oscillators communicate exclusively through a central hub, drawing parallels to neuronal synchronization in the brain. Experimental validation is achieved through an electronic circuit testbed, supported by nonlinear ODE modeling and LTspice simulation. Future work will extend the investigation to arbitrary network topologies, further elucidating synchronization dynamics in complex systems.

Researchers and industries are increasingly drawn to quantum computing for its computational potential. However, validating new quantum algorithms is challenging due to the limitations of current quantum devices. Software simulators are time and memory-consuming, making hardware emulators an attractive alternative. This article introduces AMARETTO (quAntuM ARchitecture EmulaTion TechnOlogy), designed for quantum computing emulation on low-tier Field-Programmable gate arrays (FPGAs), supporting Clifford+T and rotational gate sets. It simplifies and accelerates the verification of quantum algorithms using a Reduced-Instruction-Set-Computer (RISC)-like structure and efficient handling of sparse quantum gates. A dedicated compiler translates OpenQASM 2.0 into RISC-like instructions. AMARETTO is validated against the Qiskit simulators. Our results show successful emulation of sixteen qubits on a AMD Kria KV260 SoM. This approach rivals other works in emulated qubit capacity on a smaller, more affordable FPGA, Comment: paper accepted at the IEEE International Conference on Electronics Circuits and Systems 2024 conference, 4 pages, 6 figures

In this paper, we explore the conditions for asymptotic stability of the hybrid closed-loop system resulting from the interconnection of a nonlinear plant, an intelligent sensor that generates finite-time convergent estimates of the plant state, and a controller node that receives opportunistic samples from the sensor node when certain model-based event-triggering conditions are met. The proposed method is endowed with a degree of separation, in the sense that the controller design is independent of the sensor design. This is achieved under mild regularity conditions imposed on the hybrid closed-loop system and the existence of persistently flowing solutions. We demonstrate the versatility of the method by implementing it on: 1) a sampled-data controller for regulation of linear plants; 2) a synergistic controller for attitude stabilization of rigid bodies. The effectiveness of these novel controllers is demonstrated through numerical simulations.

This paper studies a graph-based sensor deployment approach in wireless sensor networks (WSNs). Specifically, in today's world, where sensors are everywhere, detecting various attributes like temperature and movement, their deteriorating lifetime is indeed a very concerning issue. In many scenarios, these sensors are placed in extremely remote areas, where maintenance becomes challenging. As a result, it is not very wise to depend on a single sensor to obtain data from a particular terrain or place. Hence, multiple sensors are deployed in these places, such that no problem arises if one or few of them fail. In this work, this problem of intelligent placement of sensors is modelled from the graph theoretic point of view. We propose a new sensor deployment approach here, which results in lesser sensor density per unit area and less number of sensors as compared to the existing benchmark schemes. Finally, the numerical results also support our claims and provide insights regarding the selection of parameters that enhance the system performance., Comment: Submitted for a possible publication

To ensure secure and reliable communication in wireless systems, authenticating the identities of numerous nodes is imperative. Traditional cryptography-based authentication methods suffer from issues such as low compatibility, reliability, and high complexity. Physical-Layer Authentication (PLA) is emerging as a promising complement due to its exploitation of unique properties in wireless environments. Recently, Machine Learning (ML)-based PLA has gained attention for its intelligence, adaptability, universality, and scalability compared to non-ML approaches. However, a comprehensive overview of state-of-the-art ML-based PLA and its foundational aspects is lacking. This paper presents a comprehensive survey of characteristics and technologies that can be used in the ML-based PLA. We categorize existing ML-based PLA schemes into two main types: multi-device identification and attack detection schemes. In deep learning-based multi-device identification schemes, Deep Neural Networks are employed to train models, avoiding complex processing and expert feature transformation. Deep learning-based multi-device identification schemes are further subdivided, with schemes based on Convolutional Neural Networks being extensively researched. In ML-based attack detection schemes, receivers utilize intelligent ML techniques to set detection thresholds automatically, eliminating the need for manual calculation or knowledge of channel models. ML-based attack detection schemes are categorized into three sub-types: Supervised Learning, Unsupervised Learning, and Reinforcement Learning. Additionally, we summarize open-source datasets used for PLA, encompassing Radio Frequency fingerprints and channel fingerprints. Finally, this paper outlines future research directions to guide researchers in related fields., Comment: 111 pages, 9 figures

The flexibility of industrial power consumption plays a key role in the transition to renewable energy systems, contributing to grid stability, cost reduction and decarbonization efforts. This paper presents a novel methodology to quantify and optimize the flexibility of electricity consumption in manufacturing plants. The proposed model is applied to actual cement and steel plant configurations. Comparative simulations performed with the model reveal significant differences in flexibility and cost-effectiveness, driven by factors such as production capacity, downstream process demand, storage capacity, and operational constraints. A comprehensive sensitivity analysis further clarifies the impact of various parameters on production optimization and flexibility savings. Specifically, as demand approaches production levels, flexibility decreases. Although increasing storage capacity typically reduces production costs, the benefits diminish above a certain threshold. The results provide valuable information for industrial operators wishing to improve operational efficiency, reduce costs and increase the flexibility of their operations.

Symbiotic control synergistically integrates fixed-gain control and adaptive learning architectures to mitigate system uncertainties more predictably than adaptive learning alone and without requiring prior knowledge of uncertainty bounds as compared to fixed-gain control alone. Specifically, increasing the fixed-gain control parameter achieves a desired level of closed-loop system performance while the adaptive law simultaneously learns and suppresses the system uncertainties. However, stability margins can be reduced when this parameter is large and this paper aims to address this practical challenge. To this end, we propose a new fixed-gain control architecture predicated on a low-pass filter approach to regulate stability margins in the symbiotic control framework. In addition to the presented system-theoretical results focusing on the stability of the closed-loop system, we provide two illustrative numerical examples to demonstrate how the low-pass filter parameters are chosen for the stability margin regulation problem without significantly compromising the closed-loop system performance.

Laplacian flows model the rate of change of each node's state as being proportional to the difference between its value and that of its neighbors. Typically, these flows capture diffusion or synchronization dynamics and are well-studied. Expanding on these classical flows, we introduce a pseudoinverse Laplacian flow system, substituting the Laplacian with its pseudoinverse within complex-valued networks. Interestingly, for undirected graphs and unsigned weight-balanced digraphs, Laplacian and the pseudoinverse Laplacian flows exhibit an interdependence in terms of consensus. To show this relation, we first present the conditions for achieving consensus in the pseudoinverse Laplacian flow system using the property of real eventually exponentially positivity. Thereafter, we show that the pseudoinverse Laplacian flow system converges to consensus if and only if the Laplacian flow system achieves consensus in the above-mentioned networks. However, these are only the sufficient conditions for digraphs. Further, we illustrate the efficacy of the proposed approach through examples, focusing primarily on power networks.

Contraction analysis offers, through elegant mathematical developments, a unified way of designing observers for a general class of nonlinear systems, where the observer correction term is obtained by solving an infinite dimensional inequality that guarantees global exponential convergence. However, solving the matrix partial differential inequality involved in contraction analysis design is both analytically and numerically challenging and represents a long-lasting challenge that prevented its wide use. Therefore, the present paper proposes a novel approach that relies on an unsupervised Physics Informed Neural Network (PINN) to design the observer's correction term by enforcing the partial differential inequality in the loss function. The performance of the proposed PINN-based nonlinear observer is assessed in numerical simulation as well as its robustness to measurement noise and neural network approximation error.

The integration of Distributed Energy Resources (DERs) into power distribution systems has made microgrids foundational to grid modernization. These DERs, connected through power electronic inverters, create power electronics dominated grid architecture, introducing unique challenges for fault detection. While external line faults are widely studied, inverter faults remain a critical yet underexplored issue. This paper proposes various data mining techniques for the effective detection and localization of inverter faults-essential for preventing catastrophic grid failures. Furthermore, the difficulty of differentiating between system anomalies and internal inverter faults within Power Electronics-Driven Grids (PEDGs) is addressed. To enhance grid resilience, this work applies advanced artificial intelligence methods to distinguish anomalies from true internal faults, identifying the specific malfunctioning switch. The proposed FaultNet-ML methodology is validated on a 9-bus system dominated by inverters, illustrating its robustness in a PEDG environment., Comment: 5 pages, 2 figures, submitted to 2025 IEEE Power and Energy Society General Meeting (PESGM 2025), Austin, TX

In this paper, we investigate the performance of an integrated sensing and communication (ISAC) system within a cell-free massive multiple-input multiple-output (MIMO) system. Each access point (AP) operates in the millimeter-wave (mmWave) frequency band. The APs jointly serve the user equipments (UEs) in the downlink while simultaneously detecting a target through dedicated sensing beams, which are directed toward a reconfigurable intelligent surface (RIS). Although the AP-RIS, RIS-target, and AP-target channels have both line-of-sight (LoS) and non-line-of-sight (NLoS) parts, it is assumed only knowledge of the LoS paths is available. A key contribution of this study is the consideration of clutter, which degrades the target detection if not handled. We propose an algorithm to alternatively optimize the transmit power allocation and the RIS phase-shift matrix, maximizing the target signal-to-clutter-plus-noise ratio (SCNR) while ensuring a minimum signal-to-interference-plus-noise ratio (SINR) for the UEs. Numerical results demonstrate that exploiting clutter subspace significantly enhances detection probability, particularly at high clutter-to-noise ratios, and reveal that an increased number of transmit side clusters impair detection performance. Finally, we highlight the performance gains achieved using a dedicated sensing stream., Comment: submitted to IEEE ICC25

This paper presents a step towards a formal controller design method for autonomous agents based on knowledge awareness to improve decision-making. Our approach is to first create an organized repository of information (a knowledge base) for autonomous agents which can be accessed and then translated into temporal specifications. Secondly, to develop a controller with formal guarantees that meets a combination of mission-specific objective and the specification from the knowledge base, we utilize an abstraction-based controller design (ABCD) approach, capable of managing both nonlinear dynamics and temporal requirements. Unlike the conventional offline ABCD approach, our method dynamically updates the controller whenever the knowledge base prompts changes in the specifications. A three-dimensional nonlinear car model navigating an urban road scenario with traffic signs and obstacles is considered for validation. Results show the effectiveness of the method in guiding the autonomous agents to the target while complying with the knowledge base and the mission-specific objective.

The indoor environment greatly affects health and well-being; enhancing health and reducing energy use in these settings is a key research focus. With advancing Information and Communication Technology (ICT), recommendation systems and reinforcement learning have emerged as promising methods to induce behavioral changes that improve indoor environments and building energy efficiency. This study employs text-mining and Natural Language Processing (NLP) to examine these approaches in building control and occupant interaction. Analyzing approximately 27,000 articles from the ScienceDirect database, we found extensive use of recommendation systems and reinforcement learning for space optimization, location recommendations, and personalized control suggestions. Despite broad applications, their use in optimizing indoor environments and energy efficiency is limited. Traditional recommendation algorithms are commonly used, but optimizing indoor conditions and energy efficiency often requires advanced machine learning techniques like reinforcement and deep learning. This review highlights the potential for expanding recommender systems and reinforcement learning applications in buildings and indoor environments. Areas for innovation include predictive maintenance, building-related product recommendations, and optimizing environments for specific needs like sleep and productivity enhancements based on user feedback.

In future power systems, the detailed structure and dynamics may not always be fully known. This is due to an increasing number of distributed energy resources, such as photovoltaic generators, battery storage systems, heat pumps and electric vehicles, as well as a shift towards active distribution grids. Obtaining physically-based models for simulation and control synthesis can therefore become challenging. Differential equations, where the right-hand side is represented by a neural network, i.e., neural ordinary differential equations (NODEs), have a great potential to serve as a data-driven black-box model to overcome this challenge. This paper explores their use in identifying the dynamics of droop-controlled grid-forming units based on inputs and state measurements. In numerical studies, various NODE structures used with different numerical solvers are trained and evaluated. Moreover, they are compared to the sparse identification of nonlinear dynamics (SINDy) method. The results demonstrate that even though SINDy yields more accurate models, NODEs achieve good prediction performance without prior knowledge about the system's nonlinearities which SINDy requires to work best.

This paper considers the application of Model Predictive Control (MPC) to a weighted coverage path planning (WCPP) problem. The problem appears in a wide range of practical applications, such as search and rescue (SAR) missions. The basic setup is that one (or multiple) agents can move around a given search space and collect rewards from a given spatial distribution. Unlike an artificial potential field, each reward can only be collected once. In contrast to a Traveling Salesman Problem (TSP), the agent moves in a continuous space. Moreover, he is not obliged to cover all locations and/or may return to previously visited locations. The WCPP problem is tackled by a new Model Predictive Control (MPC) formulation with so-called Coverage Constraints (CCs). It is shown that the solution becomes more effective if the solver is initialized with a TSP-based heuristic. With and without this initialization, the proposed MPC approach clearly outperforms a naive MPC formulation, as demonstrated in a small simulation study.

In power systems, unpredictable events like extreme weather, equipment failures, and cyberattacks present significant challenges to ensuring safety and reliability. Ensuring resilience in the face of these uncertainties is crucial for reliable and efficient operations. This paper presents a tri-level optimization approach for robust power system operations that effectively address worst-case attacks. The first stage focuses on optimizing economic dispatch under normal operating conditions, aiming to minimize generation costs while maintaining the supply-demand balance. The second stage introduces an adversarial attack model, identifying worst-case scenarios that maximize the system's vulnerability by targeting distributed generation (DG). In the third stage, mitigation strategies are developed using fast-response energy storage systems (ESS) to minimize disruptions caused by these attacks. By integrating economic dispatch, vulnerability assessment, and mitigation into a unified framework, this approach provides a robust solution for enhancing power system resilience and safety against evolving adversarial threats. The approach is validated using the IEEE-33 node distribution system to demonstrate its effectiveness in achieving both cost efficiency and system resilience., Comment: This work has been submitted for possible publication

Guided missile accuracy and precision is negatively impacted by seeker delay, more specifically by the delay introduced by a mechanical seeker gimbal and the computational time taken to process the raw data. To meet the demands and expectations of modern missiles systems, the impact of this hardware limitation must be reduced. This paper presents a new observer design that predicts the future state of a seeker signal, augmenting the guidance system to mitigate the effects of this delay. The design is based on a novel two-step differentiator, which produces the estimated future time derivatives of the signal. The input signal can be nonlinear and provides for simple integration into existing systems. A bespoke numerical guided missile simulation is used to demonstrate the performance of the observer within a missile guidance system. Both non-manoeuvring and randomly manoeuvring target engagement scenarios are considered.

The programmable metasurface is regarded as one of the most promising transformative technologies for next-generation wireless system applications. Due to the lack of effective perception ability of the external electromagnetic environment, there are numerous challenges in the intelligent regulation of wireless channels, and it still relies on external sensors to reshape electromagnetic environment as desired. To address that problem, we propose an adaptive metasurface (AMS) which integrates the capabilities of acquiring wireless environment information and manipulating reflected electromagnetic (EM) waves in a programmable manner. The proposed design endows the metasurfaces with excellent capabilities to sense the complex electromagnetic field distributions around them and then dynamically manipulate the waves and signals in real time under the guidance of the sensed information, eliminating the need for prior knowledge or external inputs about the wireless environment. For verification, a prototype of the proposed AMS is constructed, and its dual capabilities of sensing and manipulation are experimentally validated. Additionally, different integrated sensing and communication (ISAC) scenarios with and without the aid of the AMS are established. The effectiveness of the AMS in enhancing communication quality is well demonstrated in complex electromagnetic environments, highlighting its beneficial application potential in future wireless systems.

Many cyber-physical systems can naturally be formulated as switched systems with constrained switching. This includes systems where one of the signals in the feedback loop may be lost. Possible sources for losses are shared or unreliable communication media in networked control systems, or signals which are discarded, e.g., when using a shared computation device such as a processor in real-time control applications. The use of switched systems with constrained switching is not limited to cyber-physical systems but, includes many other relevant applications such as power systems and modeling virus mutations. In this chapter, we introduce a framework for analyzing and designing controllers which guarantee robust quadratic performance for switched systems with constrained switching. The possible switching sequences are described by the language of a labeled graph where the labels are linked to the different subsystems. The subsystems are allowed to have different input and output dimensions, and their state-space representations can be affected by a broad class of uncertainties in a rational way. The proposed framework exploits ideas from dissipativity-based linear control theory to derive analysis and synthesis inequalities given by linear matrix inequalities. We demonstrate how the proposed framework can be applied to the design of controllers for uncertain weakly hard real-time control systems - a system class naturally appearing in networked and real-time control.

We consider a real-time state reconstruction system for industrial metaverse. The time-varying physical process states in real space are captured by multiple sensors via wireless links, and then reconstructed in virtual space. In this paper, we use the spatial-temporal correlation of the sensor data of interest to infer the real-time data of the target sensor to reduce the mean squared error (MSE) of reconstruction for industrial metaverse under short-packet transmission (SPT). Both synchronous and asynchronous transmission modes for multiple sensors are considered. It is proved that the average MSE of reconstruction and average block error probability (BLEP) have a positive correlation under inference with synchronous transmission scheme, and they have a negative correlation in some conditions under inference with asynchronous transmission scheme. Also, it is proved that the average MSE of reconstruction with inference can be significantly lower than that without inference, even under weak mean squared spatial correlation (MSSC). In addition, closed-form MSSC thresholds are derived for the superiority regions of the inference with synchronous transmission and inference with asynchronous transmission schemes, respectively. Adaptations of blocklength and time shift of asynchronous transmission are conducted to minimize the average MSE of reconstruction. Simulation results show that the two schemes significantly outperform the no inference case, with an average MSE reduction of more than 50%.

Efficient multiple setpoint tracking can enable advanced biotechnological applications, such as maintaining desired population levels in co-cultures for optimal metabolic division of labor. In this study, we employ reinforcement learning as a control method for population setpoint tracking in co-cultures, focusing on policy-gradient techniques where the control policy is parameterized by neural networks. However, achieving accurate tracking across multiple setpoints is a significant challenge in reinforcement learning, as the agent must effectively balance the contributions of various setpoints to maximize the expected system performance. Traditional return functions, such as those based on a quadratic cost, often yield suboptimal performance due to their inability to efficiently guide the agent toward the simultaneous satisfaction of all setpoints. To overcome this, we propose a novel return function that rewards the simultaneous satisfaction of multiple setpoints and diminishes overall reward gains otherwise, accounting for both stage and terminal system performance. This return function includes parameters to fine-tune the desired smoothness and steepness of the learning process. We demonstrate our approach considering an $\textit{Escherichia coli}$ co-culture in a chemostat with optogenetic control over amino acid synthesis pathways, leveraging auxotrophies to modulate growth.

Interstellar objects (ISOs), astronomical objects not gravitationally bound to the sun, could present valuable opportunities to advance our understanding of the universe's formation and composition. In response to the unpredictable nature of their discoveries that inherently come with large and rapidly changing uncertainty in their state, this paper proposes a novel multi-spacecraft framework for locally maximizing information to be gained through ISO encounters with formal probabilistic guarantees. Given some approximated control and estimation policies for fully autonomous spacecraft operations, we first construct an ellipsoid around its terminal position, where the ISO would be located with a finite probability. The large state uncertainty of the ISO is formally handled here through the hierarchical property in stochastically contracting nonlinear systems. We then propose a method to find the terminal positions of the multiple spacecraft optimally distributed around the ellipsoid, which locally maximizes the information we can get from all the points of interest (POIs). This utilizes a probabilistic information cost function that accounts for spacecraft positions, camera specifications, and ISO position uncertainty, where the information is defined as visual data collected by cameras. Numerical simulations demonstrate the efficacy of this approach using synthetic ISO candidates generated from quasi-realistic empirical populations. Our method allows each spacecraft to optimally select its terminal state and determine the ideal number of POIs to investigate, potentially enhancing the ability to study these rare and fleeting interstellar visitors while minimizing resource utilization., Comment: IEEE Aerospace Conference, Preprint Version, Accepted: November 2024

We propose a learning-based Control Barrier Function (CBF) to reduce conservatism in collision avoidance of car-like robots. Traditional CBFs often use Euclidean distance between robots' centers as safety margin, neglecting headings and simplifying geometries to circles. While this ensures smooth, differentiable safety functions required by CBFs, it can be overly conservative in tight environments. To address this limitation, we design a heading-aware safety margin that accounts for the robots' orientations, enabling a less conservative and more accurate estimation of safe regions. Since the function computing this safety margin is non-differentiable, we approximate it with a neural network to ensure differentiability and facilitate integration with CBFs. We describe how we achieve bounded learning error and incorporate the upper bound into the CBF to provide formal safety guarantees through forward invariance. We show that our CBF is a high-order CBF with relative degree two for a system with two robots whose dynamics are modeled by the nonlinear kinematic bicycle model. Experimental results in overtaking and bypassing scenarios reveal a 33.5 % reduction in conservatism compared to traditional methods, while maintaining safety. Code: https://github.com/bassamlab/sigmarl, Comment: 8 pages, 5 figures

As AI systems become integral to critical operations across industries and services, ensuring their reliability and safety is essential. We offer a framework that integrates established reliability and resilience engineering principles into AI systems. By applying traditional metrics such as failure rate and Mean Time Between Failures (MTBF) along with resilience engineering and human reliability analysis, we propose an integrate framework to manage AI system performance, and prevent or efficiently recover from failures. Our work adapts classical engineering methods to AI systems and outlines a research agenda for future technical studies. We apply our framework to a real-world AI system, using system status data from platforms such as openAI, to demonstrate its practical applicability. This framework aligns with emerging global standards and regulatory frameworks, providing a methodology to enhance the trustworthiness of AI systems. Our aim is to guide policy, regulation, and the development of reliable, safe, and adaptable AI technologies capable of consistent performance in real-world environments.

As one of the most promising technologies in industry, the Digital Twin (DT) facilitates real-time monitoring and predictive analysis for real-world systems by precisely reconstructing virtual replicas of physical entities. However, this reconstruction faces unprecedented challenges due to the everincreasing communication overhead, especially for digital robot arm reconstruction. To this end, we propose a novel goal-oriented semantic communication (GSC) framework to extract the GSC information for the robot arm reconstruction task in the DT, with the aim of minimising the communication load under the strict and relaxed reconstruction error constraints. Unlike the traditional reconstruction framework that periodically transmits a reconstruction message for real-time DT reconstruction, our framework implements a feature selection (FS) algorithm to extract the semantic information from the reconstruction message, and a deep reinforcement learning-based temporal selection algorithm to selectively transmit the semantic information over time. We validate our proposed GSC framework through both Pybullet simulations and lab experiments based on the Franka Research 3 robot arm. For a range of distinct robotic tasks, simulation results show that our framework can reduce the communication load by at least 59.5% under strict reconstruction error constraints and 80% under relaxed reconstruction error constraints, compared with traditional communication framework. Also, experimental results confirm the effectiveness of our framework, where the communication load is reduced by 53% in strict constraint case and 74% in relaxed constraint case. The demo is available at: https://youtu.be/2OdeHKxcgnk., Comment: Submitted to IEEE for potential publication

Robotic grasping requires safe force interaction to prevent a grasped object from being damaged or slipping out of the hand. In this vein, this paper proposes an integrated framework for grasping with formal safety guarantees based on Control Barrier Functions. We first design contact force and force closure constraints, which are enforced by a safety filter to accomplish safe grasping with finger force control. For sensory feedback, we develop a technique to estimate contact point, force, and torque from tactile sensors at each finger. We verify the framework with various safety filters in a numerical simulation under a two-finger grasping scenario. We then experimentally validate the framework by grasping multiple objects, including fragile lab glassware, in a real robotic setup, showing that safe grasping can be successfully achieved in the real world. We evaluate the performance of each safety filter in the context of safety violation and conservatism, and find that disturbance observer-based control barrier functions provide superior performance for safety guarantees with minimum conservatism. The demonstration video is available at https://youtu.be/Cuj47mkXRdg.

Singularities, manifesting as special configuration states, deteriorate robot performance and may even lead to a loss of control over the system. This paper addresses the kinematic singularity concerns in robotic systems with model mismatch and actuator constraints through control barrier functions (CBFs). We propose a learning-based control strategy to prevent robots entering singularity regions. More precisely, we leverage Gaussian process (GP) regression to learn the unknown model mismatch, where the prediction error is restricted by a deterministic bound. Moreover, we offer the criteria for parameter selection to ensure the feasibility of CBFs subject to actuator constraints. The proposed approach is validated by high-fidelity simulations on a 2 degrees-of-freedom (DoFs) planar robot., Comment: This work has been submitted to ECC 2025 for possible publication