Papers

This article introduces a numerical optimal control framework for minmax constrained density control for a class of noisy linear parabolic partial differential equations (PDEs), in particular the noisy heat equation. The goal is to transport an initial density to a target density while minimizing a specified cost with respect to control actions and maximizing it with respect to disturbances, all within a fixed time horizon while satisfying given convex path constraints. To address this, the spatial derivatives in the PDE are discretized using finite-difference approximations, transforming the problem into a system of ordinary differential equations in time. The admissible space of control and disturbance trajectories is then finitely parametrized, and the resulting optimal control problem is formulated as a convex semi-infinite program (SIP) under mild assumptions. By leveraging new numerical tools from convex SIP theory, we establish guarantees for exact solutions that account for constraint satisfaction under an infinite family of disturbance realizations, and we establish an optimization-based computationally efficient algorithm to recover these solutions. Comprehensive numerical examples to demonstrate and validate our findings are included.

Distributed target tracking is essential for scalable and robust sensing systems, as it enables multiple radar nodes to cooperatively estimate a target state without relying on a centralized fusion center. In this paper, we present a fully distributed framework for single-target tracking in frequency-modulated continuous-wave (FMCW) radar networks. Each monostatic radar node observes local range and Doppler measurements and exchanges information only with neighboring nodes. Two consensus optimization-based estimators are developed. First, a Distributed Maximum A Posteriori (D-MAP) estimator is formulated for batch-based tracking, where prior state information is incorporated and the resulting optimization problem is solved using consensus-based alternating direction method of multipliers (ADMM). Second, a Distributed Extended Kalman Filter (D-EKF) is proposed for recursive tracking, where each node performs local prediction and correction followed by consensus ADMM-based information exchange. We derive the posterior Cramér-Rao lower bound (PCRLB) as a theoretical performance benchmark. Our simulation results show that D-MAP improves the accuracy of the estimation over the distributed maximum-likelihood baseline. These results demonstrate that the proposed framework provides a scalable and robust alternative to centralized radar tracking, given only local inter-node communication.

This paper studies the problem of robust stabilization of linear input-state systems in discrete time. We prove that for any compact set of stabilizable systems, there exists a dynamic state-feedback controller that globally asymptotically stabilizes all systems in the set. In addition, we show that for some compact sets of stabilizable systems, no nonlinear static or linear dynamic state-feedback law can achieve this task. This proves that, in general, robust stabilization requires a feedback law that is both nonlinear and dynamic. We extend our study to robust exponential stabilization with a given rate of decay. Finally, for polytopic sets of systems, we introduce an algorithm for the design of robust feedback laws.

The increasing penetration of power electronic converters in industrial grids introduces stability challenges at frequencies well above the electromechanical range of traditional power systems. Commercial software tools typically perform eigenvalue-based stability analysis using root-mean-square (RMS) models that assume a quasi-stationary network, thereby neglecting electromagnetic dynamics of assets — which is standard practice in power system analysis. This paper presents a case study of an industrial grid where the conventional RMS-based eigenvalue analysis predicts stable operation, while a detailed electromagnetic transient (EMT) simulation reveals growing oscillations, indicating instability. To bridge this gap, we introduce an alternative modeling approach that formulates the network dynamics in the rotating $dq$ reference frame, retaining the differential equations of electromagnetic dynamics. The resulting model enables eigenvalue analysis that correctly identifies the unstable modes, consistent with the EMT simulation results. Our findings highlight a fundamental limitation of RMS-based stability assessment for converter-dominated grids and demonstrate that dynamic impedance models in the dq frame provide a viable path toward accurate small-signal analysis encompassing high-frequency converter-network interactions.

Scalable supervisors for multi-agent discrete-event systems control groups of isomorphic agents through a common template. Under partial observation, such a supervisor is maximally permissive if the relabeling that maps the agents onto their template is relabeling observation consistent (ROC) and a local companion condition holds. Whether ROC is decidable was open. We show that it is PSPACE-complete: for nondeterministic plants with two observable and one unobservable event, and for deterministic plants with two observable and two unobservable events. On the tractable side, we characterize the relabelings that guarantee ROC for every plant, we give a polynomial-time algorithm for deterministic plants whose relabeling is injective on unobservable events, and we give sufficient conditions based on saturation and on simulation, with polynomial-time tests for both. We further show that ROC is compositional for components with pairwise disjoint templates, with equivalence if the alphabets are moreover pairwise disjoint, and that neither hypothesis can be dropped. Finally, we show that the structural condition proposed in the literature to guarantee ROC is incorrect, and repair the companion inclusion that the same proposition asserts.

This paper presents a comparative study of real-time controller design methods for dynamical control systems subject to deadline overruns. Numerous techniques have been proposed to handle missed deadlines, i.e., including models or information of possible deadline misses directly in the control design. These approaches substantially differ in their assumptions, supported execution semantics, required runtime information, and provided guarantees, making direct comparison challenging. We address this gap with a qualitative assessment of representative deadline-miss-aware control methods. We analyze the design space of such controllers and identify the critical assumptions on the real-time constraints, the control design criteria and implementation aspects. We expose how these different assumptions lead to the different trade-offs in applicability, and control performance and guarantees. We contribute a comparison tool enabling researchers to systematically benchmark new techniques against the state of the art. The study is supported by simulations on two case studies: a Furuta pendulum and an electric motor to illustrate the practical behavior of the different approaches under missed-deadline conditions.

This paper presents a mixed-integer linear programming (MILP) framework for joint electric vehicle (EV) and electric bus (eBus) charging-infrastructure planning with photovoltaic (PV) self-consumption. The model co-optimizes charger siting, sizing, technology selection, eBus-to-depot assignment, and hourly charging schedules. A deterministic MILP is first formulated as a nominal benchmark and then extended to a scenario-based robust min–max formulation under vehicle energy-demand uncertainty. The robust model uses shared first-stage infrastructure decisions and scenario-specific operating decisions, and minimizes infrastructure cost plus the worst-case scenario operating cost through an epigraph reformulation. Soft-feasibility penalties quantify unmet charging energy, terminal state-of-charge (SOC) shortfall, and capacity violations under stressed scenarios. A 50-node case study shows that route-segment eBus demand and PV weighting alter charger deployment, technology mix, and grid import, while V2G enables hard-feasible robust operation at sufficiently large penalty weights. These results highlight that effective long-term infrastructure planning must simultaneously account for diverse vehicle fleets, operational strategies that adapt to different scenarios, and node-level constraints on charging capacity.

Time-delays are known to have a detrimental effect on feedback systems. In the context of networked cyber-physical systems, delays can be injected by malicious adversaries. Detecting them early is an important challenge. This paper proposes a novel variation of Interacting Multiple Model filtering to detect delay injection attacks in feedback control systems, when hidden in an open loop setting. The detection scheme is formalised by treating delay as alternative modes of the system, and theoretical analysis of the stationary distribution informs a reduction to a three parameter model as well as the choices of hyper parameter values. The method is evaluated on a cruise control application, and shows detection within a few seconds and a low false alarm probability.

This paper addresses morning commute congestion caused by concentrated school-related trips in urban networks. We propose a bi-level optimization framework for regulating school start times in a multi-region urban network characterized by Macroscopic Fundamental Diagrams (MFDs), explicitly coupling system-level regulation with multi-class user-equilibrium-based departure-time choices. The Upper-Level problem jointly minimizes total time spent and deviations from current school schedules, while the Lower-Level problem models commuter behavior through a deterministic dynamic multi-class user equilibrium formulation incorporating alpha-beta-gamma preferences for travel time, earliness, and lateness costs. To address the computational challenges arising from the bilevel structure, non-convex traffic dynamics, and endogenous demand responses, an iterative algorithm alternating between the Upper- and Lower-Level problems is developed. The Upper-Level problem is approximated through a formulation solvable with standard mathematical programming solvers, while an iterative algorithm provides an approximate solution to the Lower-Level equilibrium problem. Numerical results demonstrate substantial congestion reductions and characterize the trade-off between school start-time flexibility and traffic efficiency. Sensitivity analyses further examine the effects of MFD uncertainty and scheduling preferences.

In some fields currently dominated by empirical approaches, such as state of health (SoH) prediction for lithium-ion batteries, phenomenological models motivated by quasi-physical thinking contain parameters to be estimated from experimental data. Often the structure of such models yields fully or partially confounded parameters, which are difficult or even impossible to estimate reliably. To preserve the desired model formulation and simultaneously improve the numerical conditioning for the problem we introduce a ridge regression scheme. An automated method is provided, based on information theoretic measures of model performance, which optimises the ridge regression hyper-parameter at each iteration. The formulae presented require fixed point iteration to solve for the hyper-parameter. Given a suitable starting value, analysis demonstrates convergence is very rapid. The optimal hyper-parameter selection mechanism is incorporated within an efficient regularised iterative generalised least squares mechanism, capable of fitting both heteroscedastic and serially correlated data as required. Simulation confirms the efficacy of the overall method.

An adaptive nonlinear optimal-control scheme is developed by combining receding-horizon iLQR, state estimation, online parameter identification, and actuator constraints. AMIGO (Adaptive Model-based Intelligent Guidance and Orchestration) organizes the computation into three Time Phases: identification, planning, and closed-loop control. A supervisory adaptive loop monitors predictive consistency during closed-loop operation and can repeat identification and planning when persistent parameter mismatch is detected. The nonlinear transition is evaluated by the fourth-order Runge-Kutta method (RK4), and model parameters are refined by the Levenberg-Marquardt method (LM). The method is illustrated by a Van der Pol oscillator, a quadcopter, and an autonomous lunar-lander descent.

Cable-suspended payload transport by multirotor UAVs is flexible but generates periodic swing disturbance that degrades tracking and risks instability. Existing anti-swing methods require additional sensors or precise identification of cable length and payload mass, limiting field deployment. We propose a swing-estimation and damping method using only the onboard IMU and throttle command, requiring no payload parameters. An extended Kalman filter extracts the periodic disturbance with the unknown pendulum frequency as an estimated state, and an active damping controller adds a correction angle to the attitude loop to dissipate pendulum energy. Flight experiments confirm robust damping across a tested range of cable-length and mass variations.

High-altitude platforms (HAPs) are emerging as persistent middle-layer infrastructures for space-air-ground integrated networks (SAGINs), offering a favorable compromise among coverage, latency, endurance, and deployment flexibility. Their role, however, is evolving beyond communication relaying toward the joint provision of sensing, storage, communication, computing, and intelligence (S^2C^2I). This survey presents a unified HAP-centric perspective on S^2C^2I integration. We first review HAP fundamentals, platform categories, and their principal roles in SAGINs, including wide-area access, relaying, backhaul, edge service, low-altitude aerial coordination, and cross-layer orchestration. We then develop an integrated architecture spanning multi-plane connectivity, payload functional splits, and a cloud-edge-HAP space continuum with hierarchical data, control, computing, and storage loops. The enabling technologies are systematically examined, covering heterogeneous RF, millimeter-wave, terahertz, free-space optical, and hybrid links; sensing payloads and integrated sensing and communication; onboard computing; storage and caching; and AI-based orchestration. We further synthesize standardization progress, open software and datasets, testbeds, field evidence, and a four-level evaluation methodology ranging from component validation to mission-level effectiveness. An emergency-response case study demonstrates that joint S^2C^2I orchestration substantially improves conjunctive service availability while reducing feeder-link traffic. Finally, we identify research opportunities in agentic AI, trustworthy autonomy, goal-oriented semantic operation and digital twins, and sustainable, certifiable, and open HAP-native systems. The resulting synthesis provides a coherent roadmap from platform design to network-wide deployment.

Spatial aliasing occurs when two or more distinct locations produce highly similar place-cell representations, primarily due to environmental symmetry or repetitive structures. This issue is most pronounced when place representations are constructed solely from boundary vector cell (BVC) inputs, because symmetric or repetitive structures can yield indistinguishable sensory patterns across multiple locations in an environment. This work introduces grid cell signals to mitigate spatial aliasing in such settings. Because grid cells contribute periodic, internally generated spatial signals that vary independently of environmental geometry, they play a key role in disambiguating perceptually identical locations. We integrate multiple modules of analytically constructed grid cells with BVC-driven place cells and show that this leads to a 94–99% reduction in spatial aliasing relative to a BVC-only baseline across three environments: an open environment without obstacles; an environment with a cross-shaped central obstacle creating high visual symmetry; and a maze environment. The greatest improvement occurs in the environment with the highest visual symmetry. These results indicate that grid cells provide information complementary to boundary-based inputs, yielding more reliable place representations in geometrically ambiguous environments.

This paper is concerned with robust instability of uncertain network systems. We consider the multi-agent system described as a network of single-input-single-output agents with identical nominal dynamics subject to heterogeneous perturbations. The network description is formalized as a feedback interconnection of a diagonal uncertainty, nominal identical agents, and a static interconnection matrix. Assuming that the nominal network is unstable, we seek the robust instability radius (RIR), defined as the smallest norm of the stable uncertainty that renders the network stable. Conditions for the network stability are developed, and upper and lower bounds on the RIR are derived. When the network connectivity matrix is rank one and all diagonal entries share the same sign or are zero, we give conditions under which the RIR is exactly characterized by a small gain argument.

This paper investigates robust instability in nominally unstable uncertain networked dynamical systems, where all nominal agents share an identical single-input-single-output (SISO) linear time-invariant (LTI) system and each agent is subject to independent perturbations. This setting is motivated by the problem of sustaining periodic oscillations in nonlinear dynamics, for which exact analysis is generally intractable. We identify three classes of network structures including cyclic and certain rank-deficient networks for which the robust instability problem can be reduced to the analysis of a single representative SISO system. We derive sufficient conditions that exactly characterize the robust instability radius for these network classes. Finally, we demonstrate the practical utility of the proposed results by analyzing oscillatory behavior in a genetic regulatory network.

Advancements in emerging intelligent transportation systems (ITS) have shown immense benefit in reducing congestion, emissions, and accidents and enable lower-cost alternatives to highway lane expansion. However, there currently exists no standard metric by which agencies can assess the improvement potential from these interventions. As a result, they may fail to deploy infrastructure where it will be most promising or risk investing in infrastructure that does not meaningfully enhance performance. Our objective is to guide ITS deployment by developing a metric that quantifies the upper bound of congestion improvement from controlling speed limits on a highway. We propose controllable congestion, a metric that quantifies the maximum achievable reduction in system delay. To estimate controllable congestion, we develop a nonlinear optimization framework grounded in a reformulation of the METANET macroscopic traffic model and solved using model predictive control (MPC). Using both a synthetic scenario and highway data from the I-24 SMART Corridor in Tennessee, we find that controllable congestion is largely independent of total delay, revealing that for two days with identical travel times, controllable congestion can vary from 13% to 80%. We further show that the realizable share of this upper bound depends on the operational constraints imposed. On I-24, the minimum posted speed limit has little effect on controllable congestion, while frequency of speed limit update and maximum difference between adjacent gantries have large impacts. This framework allows highway operators to distinguish between congestion that is structurally unavoidable and congestion that is highly responsive to ITS, enabling more cost-effective deployment of control-based traffic infrastructure.

Construction electric vehicles (CEVs) are a promising clean alternative to diesel-powered construction equipment, but their adoption is constrained by sparse onsite charging infrastructure, limited CEV mobility, and insufficient understanding of their power consumption. We address these gaps through a field-data-driven framework coupling CEV power estimation with mobile-charging-aware work scheduling. First, using a real-world construction demonstration at the University of California, San Diego, we develop and validate a per-subactivity power estimation model for a compact electric excavator. Manually labeled video is synchronized with coarse battery state-of-charge (SOC) telematics, and constrained nonnegative least squares is used to recover each subactivity's average power consumption. The model predicts held-out test data within $17\%$ normalized mean absolute error (NMAE), and the accompanying dataset is released publicly. Second, leveraging the subactivity power estimates, we formulate a mixed-integer program that jointly optimizes CEV work and charging schedules together with the location, timing, and charging/discharging of mobile charging stations (MCSs) serving the CEVs. The optimization accounts for energy and demand charges, carbon emissions, unmet work penalties, MCS travel, and the physical and operational constraints of the CEVs and MCSs. Across realistic scenarios drawn from the demonstration, the proposed co-optimization attains the lowest operating cost in every case, being $7$–$96\%$ below the best-performing baseline, while solving most instances to proven optimality within an hour. Dataset and scripts are available at https://github.com/ghosh-avik/CEV-MCS-Power-Estimation-and-Joint-Scheduling.

This paper proposes a multi-level Bayesian calibration approach that fuses information from heterogeneous sources and accounts for uncertainties in modeling and measurements for time-dependent multi-component systems. The developed methodology has two elements: quantifying the uncertainty at component and system levels, by fusing all available information, and corrected model prediction. A multi-level Bayesian calibration approach is developed to estimate component-level and system-level parameters using measurement data that are obtained at different time instances for different system components. Such heterogeneous data are consumed in a sequential manner, and an iterative strategy is developed to calibrate the parameters at the two levels. This calibration strategy is implemented for two scenarios: offline and online. The offline calibration uses data that is collected over all the time-steps, whereas online calibration is performed in real-time as new measurements are obtained at each time-step. Analysis models and observation data for the thermo-mechanical behavior of gas turbine engine rotor blades are used to analyze the effectiveness of the proposed approach.

Safe coordination in heterogeneous machine-to-machine (M2M) robotic systems is challenging when robots differ in sensing capabilities, environmental awareness, and motion execution roles. This paper presents a centralized safety-aware M2M framework for cooperative goal-directed navigation in a heterogeneous mobile robot team comprising a vision-capable quadruped and a camera-less robotic vehicle. The objective is to guide both platforms toward a goal region while avoiding static and dynamic obstacles and preventing unsafe inter-robot interactions. Under the principle of shared perception, the vision-capable robot provides semantic environmental awareness through a centralized server over an MQTT broker, enabling the camera-less platform to navigate using this shared scene representation alongside its own odometry, IMU, and state feedback. A vision-language model (VLM) interprets the visual stream, and the extracted semantic data is mapped into conservative metric geometric constraints, including inflated obstacle sets, safe corridors, and goal regions. A large language model (LLM) proposes high-level task allocations, while physical command authority is restricted to a robot-specific zonotope reachability gate. This verification engine propagates independent reachable tubes to evaluate obstacle avoidance, safe-corridor containment, and inter-robot separation predicates before approving commands. Online experiments across clear-path and dynamic-obstacle scenarios show that the pipeline reliably approves safe motion, triggers conservative replanning or holding maneuvers upon constraint violation, and enforces a strict architectural separation between advisory semantic reasoning and formally verified motor execution.

This article investigates the role of polyaniline as a conductive polymer in the active layer of perovskite solar cells. Samples were created by incorporating polyaniline into the transport layers to assess its impact on enhancing efficiency and conductivity. The application of this polymer across various layers of the cell structure led to improved stability and performance. Given its high doping capability, polyaniline was examined in detail, particularly focusing on two types of oxidation doping and its integration into the hole transport layer. Graphene oxide and reduced graphene oxide were chosen as comparative models, and their performance was evaluated against the standard polyaniline configuration. Laboratory results revealed that power conversion efficiency increased by 17.5% with graphene oxide and by 36.8% with reduced graphene oxide. Furthermore, short-circuit current density improved by 9.8% and 23.1%, respectively. These findings are consistent with existing studies in the field and support the validity of the approach.

Highly directional 60-GHz Internet-of-Things (IoT) links can exploit naturally occurring indoor surfaces to sustain connectivity under blockage. Identifying viable non-line-of-sight (NLoS) paths, however, can require extensive RF beam training. This paper investigates whether LiDAR can reduce this search overhead by providing a surface-aware prior without assuming a direct mapping between optical return and mmWave reflection. The proposed framework uses LiDAR-derived geometry and return statistics to rank candidate propagation directions, while RF measurements remain responsible for final beam selection. The experimental validation is organized in three stages to separate descriptor robustness, cross-modal association, and beam-search performance. Controlled LiDAR measurements first quantify how geometric and radiometric surface descriptors vary with acquisition geometry. Matched LiDAR and 60-GHz measurements in an L-shaped corridor then determine whether these descriptors are associated with the measured surface-mediated RF response under a prescribed NLoS interaction. Finally, a separate room-scale campaign evaluates the resulting prior using exhaustive TX-RX beam maps without prescribing the underlying propagation mechanism. The measurements show that descriptor reliability depends on acquisition geometry and point-cloud representation, and that LiDAR and RF surface responses exhibit cross-modal association without supporting deterministic RF-power prediction. In the room experiment, local three-ring 3-D planarity retains a beam within 3 dB of exhaustive search at 74.5% of the measured locations while reducing RF beam-pair probing by 72% relative to exhaustive probing over the candidate search region. These results establish LiDAR-derived local surface structure as a communication-oriented prior for concentrating RF probing and reducing mmWave beam-search uncertainty.

This work proposes a hierarchical Model Predictive Supervisory Control (MPSC) framework for multi-agent systems with shared resources. MPSC integrates receding-horizon cost-optimal control with Supervisory control theory (SCT) based supervision that enforces safety, nonblockingness, and resource exclusivity. Scalability arises from hierarchical and scalable supervisor and automaton templates, enabling distributed execution without monolithic synthesis. Using this framework, this work develops an urban Unmanned aircraft system Traffic Management (UTM) model. The model supports pickup-and-delivery missions under time-varying demand efficiently.

Tool-using large language-model agents can complete a task while exercising authority that the user did not grant or the task does not need, causing excess-authority errors. Traditional permission gating systems alone for validating agent environments are insufficient. We study whether post-training can teach a 4B-parameter model to choose task-conditioned authority in executable terminal and Model Context Protocol (MCP) environments to complement those measures. We propose a framework where each action is audited before execution and again from observed effects along six dimensions of risk. This auditing is conducted using deterministic verifiers that score completion, evidence, exact state, prohibited attempts, and safe success. In conjunction with predefined task-specific sufficient-authority envelopes, we determine task-specific excess privilege values for trajectories, which are then optimized for in post-training. We find that after training using this framework on Qwen3.5-4B over 1,500 tasks, the selected seed reaches 98.48% safe success across 2,896 evaluation episodes spanning all 500 held-out tasks, compared with 64.36% for the base policy, and reduces excess-authority error events from 4.56% to 0.79%. Furthermore, external tests show capability retention and prompt-directed improvement. A 400 task continuation study also found evidence of generalization, reducing excess-authority events by 6.99 percentage points while maintaining previous capabilities. We conclude learned restraint through least-privilege aware post-training is therefore useful as an additional control layer for tool-using agents in executable terminal and MCP environments, but it does not replace permission gates and sandboxing.

Transient stability assessment determines whether a power system retains synchronism after a large disturbance. Machine-learning surrogates can accelerate it, but progress is limited by the lack of open datasets that combine dynamic ground truth, network graph structure, and machine parameters. We release a benchmark of 20,000 three-phase-to-ground fault scenarios on the IEEE 9-bus system. Each scenario couples an AC power-flow operating point with a detailed electromagnetic-transient simulation of the post-fault response. Every record provides the network as an attributed graph (nine buses, eighteen directed branches, ten node and twelve edge features), the full rotor-angle and speed trajectories of the three generators, the static machine constants, the fault description, and a center-of-inertia binary stability label. Wide load and generation scalings across eighteen fault locations yield a near-balanced distribution (48.96% stable, 51.04% unstable). Generation is deterministic and fully reproducible through fixed seeds and public code. The dataset is distributed on IEEE DataPort under a persistent DOI and supports stability classification, trajectory prediction, margin and critical-clearing-time estimation, and the comparison of topology-aware, physics-based, and hybrid learning methods.

Demand-responsive transport (DRT) is typically routed by solving Dynamic Vehicle Routing Problems (DVRPs), where individual vehicle trajectories are adjusted on incoming requests. This limits demand consolidation and thus efficiency. On the other hand, Conventional Public Transport (CPT) bus systems are based on a network of lines and users find their routes on it, which provides high demand consolidation. However, such a network is built offline and cannot adapt to the demand. We propose a public transport management strategy that reconciles efficiency and adaptivity by dynamically designing a structured network of lines via a receding-horizon optimization approach. Using real-world trip requests, we show that we nearly double the fraction of served requests compared to DVRP-based routing, and we serve more requests than CPT with lower user trip times.

Stochastic mean-field control (MFC) provides a fundamental framework for coordinating large populations of interacting agents under uncertainty, with a wide range of applications. Existing numerical and deep-learning methods solve one MFC problem instance at a time and must be re-optimized whenever the task changes. In this work, we formulate stochastic MFC as an operator-learning problem and develop, to the best of our knowledge, the first mesh-free, self-supervised neural operator for stochastic MFC. The main challenge is that the diffusion term in the controlled Fokker–Planck equation precludes deterministic transport-map representations. We address this challenge by combining the probability-flow ODE with an invertible normalizing-flow-based transformer, which recasts the dynamics as a deterministic continuity equation and enables closed-form score evaluation through the exact inverse and analytical log-determinant of the normalizing flow, with $\mathcal{O}(d)$ cost per particle for networks of fixed size. Through transformer-based in-context learning, task prompts, represented by compact distribution parameters or raw particle clouds, condition the transport map, enabling a single pretrained operator to solve unseen tasks in one forward pass. The resulting Normalizing Flow Invertible Solution Transformer (NFIST) is trained end-to-end by minimizing the stochastic control objective directly, requiring no precomputed numerical solutions for training. We further prove the consistency of the proposed operator-learning formulation with task-by-task optimization. Numerical experiments on stochastic optimal control, Schrödinger bridge, systemic-risk control, and obstacle-avoiding path planning demonstrate effective zero-shot generalization while substantially reducing the computational cost of solving large families of stochastic MFC problems.

In the digital twin paradigm, online parameter updating is essential. Unlike a static model, a digital twin must continuously adapt to the evolving dynamics of the system it represents. Adaptive observers, which jointly estimate states and parameters from online data, are therefore an increasingly important tool. In this work, we formulate a mode-wise adaptive observer for a class of autonomous switched nonlinear systems with switched unknown parameters. The main challenges are twofold: removing the disturbance that switching injects into the parameter-error dynamics and guaranteeing persistence of excitation over a finite-time window without relying on an input signal. To address them, we assign a dedicated adaptive observer to each mode, active only on its corresponding interval, which directly removes the zero-input disturbance caused by switching. We then introduce a finite-window persistence-of-excitation condition together with a minimum dwell-time condition, under which the parameter estimation error is contractive and the state estimation error is bounded within the active interval of each mode. The performance of the proposed approach is illustrated with an academic example and with a practical example of evolutionary therapies.

In this work, we introduce the generalized reach-avoid (GRA) task, which both extends and unifies the canonical tasks of Hamilton-Jacobi Reachability (HJR). We show that the GRA not only serves as a common primitive in this class of fundamental tasks, but also strictly extends the fundamental tasks that can be solved with HJR. Moreover, the GRA formulation enables one to compute the value functions of certain composite tasks, including ones from timed temporal logic, by decomposing the value function of the composite task into value functions of GRA tasks. We additionally show that the GRA is also a natural primitive to consider from a PDE perspective, as it can be used to represent all sufficiently regular solutions of the HJ-PDE that is canonical to HJR. Collectively, the results in this work show the theoretical and practical utility of this task within the increasingly important framework of HJR.

Recent publications have started to explore the application of Geometric Algebra (GA) to the modeling, analysis and control of dynamical systems and, in particular, electrical circuits. Since a crucial element there is to transform the ordinary differential equations governing the dynamical system which models the systems' behavior from the real domain to the Laplace domain, a definition of the Laplace transform in GA is needed. In the present work, we extend previous works dealing with extension to some hiper-complex algebras by introducing a definition of the Laplace transform within the framework of Geometric Algebra (GA). In particular, our definition and its properties are applicable to geometric algebras with signature lower or equal than 5.