Papers

End-of-life support of satellites is necessary to improve post-mission-disposal compliance rates for maintaining space environment. Deorbit mission with low thrust, e.g. a laser, induces a low-level deceleration on the target object that gradually lowers the target altitude. Since such a low-thrust trajectory is time-consuming, the risk of collision greatly influences the mission success rate. In this context, this paper assesses the collision risk during deorbit trajectories with low thrust. Furthermore, parametric studies for the relationship between the re-entry time and the risk of collision are performed.

HVAC systems represent a major share of building energy consumption. Traditional control strategies are limited in coordinating energy-comfort tradeoffs across multiple zones simultaneously. Reinforcement learning (RL) offers adaptive, data-driven control that optimizes performance over time. However, deploying learned neural network controllers in safety-critical building systems remains challenging due to lack of formal safety guarantees. We propose a safety-certified deep RL framework for multi-zone residential HVAC control. Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC) agents are trained in an EnergyPlus/Sinergym simulation to minimize energy consumption while maintaining thermal comfort. Post-training safety certification is performed on the PPO policy using Lipschitz-based forward invariance analysis, building on existing tools for the computation of Lipschitz constants for neural networks, to guarantee constraint satisfaction. Both agents are evaluated over an annual simulation cycle in an eight-zone variable refrigerant flow (VRF) testbed. The PPO agent achieves 67% comfort violation reduction compared to rule-based control, while the SAC agent achieves 27.6% energy savings. The PPO policy satisfies formal safety certification with a margin of $2.003^\circ$C. These results demonstrate the feasibility of combining reinforcement learning with post-training safety verification for multi-zone building control.

The setting or tuning of specifications for the inertial measurement unit (IMU) is tricky in sensor fusion. The underneath conundrum is caused by the fact that the working condition of IMU is more complex than the stationary calibration scenario. Since the noises and biases instabilities calibrated under static condition cannot accommodate other cases, the effective tuning of IMU parameters largely hinges on the experience or profound understanding of the system. In the current work, the setting method of IMU parameters based on Allan variance calibration is delved into within the Kalman filtering framework. Specifically, the relationship between the power sepctral density and Allan variance is leveraged in formulating the process uncertainty in continuous-time filtering. Three typical IMU-based sensor fusion systems, including INS/GNSS integration, LiDAR-inertial odometry, and visual-inertial odometry are considered to show the feasibility and effectiveness of this parameter setting process.

Active inference selects actions by minimising an expected free energy functional over predicted futures. However, adding an expectation over yet-unobserved outcomes means the free energy functional no longer has a Kullback-Leibler structure, which hinders message passing treatments of inference procedures. We propose an alternative formulation based on a Bethe free energy functional, fully supporting inference by message passing. The epistemic drive is maintained by imposing an information constraint, next to normalisation, marginalisation and form constraints, insisting that the mutual information between future observations, states and parameters given actions must be at least as large as the entropy of the goal prior. For a specific value of the corresponding Karush-Kuhn-Tucker multiplier, the stationary point of this constrained Bethe Lagrangian recovers the expected free energy solution. We show that, as the information demand is varied, the solved multiplier moves through its inactive, interior, and saturated regimes. In the inactive regime the agent's epistemic drive switches off entirely, while in the saturated regime it is maximal. We compare the performance of the constrained Bethe agent on three tasks against EFE and Q-MDP.

Circular healthcare supply chains are inherently complex, characterised by interdependencies among their actors and high uncertainty in product flows and performance. Current methods used to predict the outcomes of transitioning to circular economy (CE) are limited and mostly static. This paper demonstrates the use of simulation to assess the effect of introducing circular products and the implications across the healthcare supply chain accounting for variability. The laparoscopic scissors supply chain is chosen as a case study example. To the best of our knowledge, this is the first study that assesses the implications of introducing circular product (medical devices) designs at both the individual supply chain member and overall system level. The model can be also used to inform optimal inventory strategies for hospitals, to ensure that patient safety and hospital operations are maintained. Our findings suggest that adopting circular products can reduce the environmental impact, but to achieve significant reductions in both cost and emissions, it requires significant upfront investment. We discuss the theoretical and practical implications of our study in developing tools to support the transition to CE.

Stochastic reach-avoid analysis of controlled dynamical systems is an important tool for safety-critical control under uncertainty, in which the reach-avoid probability is characterized by a Hamilton-Jacobi partial differential equation (PDE). However, solving this PDE using conventional numerical methods becomes computationally intractable as the system dimension increases. Physics-informed neural networks (PINNs) may converge to inaccurate local minima when trained primarily through PDE-residual minimization. Reinforcement learning (RL) offers a scalable alternative, but its learned value functions may be inaccurate or inconsistent with the governing PDE. This paper proposes a physics-informed RL (PIRL) framework that combines the complementary strengths of PINNs and RL for stochastic reach-avoid analysis. We develop a scheduled PIRL algorithm in which temporal-difference actor-critic learning first guides the critic toward a meaningful approximation of the reach-avoid value function. PDE-residual and boundary-condition losses are then introduced progressively to enforce consistency with the governing PDE and its boundary conditions. The proposed method mitigates the failure modes of conventional PINN techniques while achieving accuracy comparable to that of successfully trained PINNs. The effectiveness of the proposed framework is demonstrated through two case studies.

Linear temporal logic is a modal extension of propositional logic that allows one to state how a system should behave over time. Its canonical domain is the booleans, but discretely-valued judgements are of little use in steering softly-valued systems (neural policies, adaptive controllers, sequence models, etc). In such cases, the goal formula's (dis)satisfaction becomes a training signal, and differentiability becomes a prime concern. Candidate differentiable semantics abound, but navigating them is tricky. Implementations, where available, are shallow embeddings, demanding an upfront commitment to a single semantic algebra and its (usually implicit) conduct. The paper casts the reader as a functional programmer asked to come to terms with this predicament, and refusing. Out of that refusal comes an evaluation engine that is algebra-generic and amenable to differentiation, together with an executable specification of the algebras it can accept. Various algebras are implemented and audited for their behavior, both forward and backward. Each algebra turns out to be a choice of which direction to disappoint, and how. Everything described (and more) is part of the PyTorch library telos, to be found at https://github.com/konstantinosKokos/telos.

Hardware optimization is critical in the design of efficient wireless communication systems. Wireless communication hardware often consumes a significant fraction of the total system's power budget, with much of this power used in circuits that reduce various types of noise, particularly in the analog front end. The Single-Chip Micro Mote, or SCuM, uses a crystal-free radio architecture and makes design trade-offs that favor power consumption over noise performance while maintaining standards compatibility with popular Internet-of-Things (IoT) protocols such as IEEE 802.15.4 and Bluetooth Low Energy. In the continued development of SCuM, we recognize that the digital baseband hardware developed can be more closely optimized with the architecture of the chip. In this paper, we present an extensible Python-based RTL generator that is closely linked to simulation and testing environments. This approach provides flexibility for use on different hardware platforms, such as tape-outs and FPGA implementations, and has promise in AI-assisted design workflows.

The global transition toward green hydrogen is driving the deployment of gigawatt-scale electrolysis centers, introducing a novel, converter-dominated load class to the bulk power system. Unlike conventional industrial loads, these facilities utilize extensive power electronics interfaces with fast dynamics comparable to Inverter-Based Resources (IBRs). This paper presents a comprehensive grid impact assessment of large-scale hydrogen hubs, focusing on harmonic injection, voltage stability in low Short Circuit Ratio (SCR) environments, and frequency response capabilities. Adopting a "full-spectrum" open-source modeling approach, the study utilizes PandaPower for large-scale steady-state contingency assessment; ANDES for electromechanical dynamic simulations to evaluate Fast Frequency Response (FFR); and ParaEMT for high-fidelity electromagnetic transient analysis of harmonic distortion and Low Voltage Ride-Through (LVRT). A critical finding of this study is that standard load models, including the generic PERC1 (data center) model, are insufficient for hydrogen hubs. The paper recommends specific structural modifications to the PERC1 model - specifically regarding process safety latches and restart voltage thresholds - to accurately capture the risk of prolonged plant tripping. Based on these findings, the paper proposes a set of standardized connection requirements to ensure secure integration.

Time-critical interactive systems increasingly require ultra-low-latency device identification for multiple users, yet prevailing approaches such as passwords, QR codes, and RFID/NFC are constrained by human input, frame-based sensing, or near-contact range. This paper presents ECO-ID, an event-camera-based optical system for multi-user, ultra-low-latency identification over visible light communication (VLC). Leveraging microsecond-resolution, asynchronous observations of brightness transitions, ECO-ID employs a spatiotemporal coding design: disjoint LED subsets provide spatial separation among users, while user-specific timing delays encode identities without inter-user synchronization. The optical channel and event-driven sensing reduce full-scene capture relative to frame cameras and limit the RF attack surface, while enabling rapid token verification with freshness and replay protection. We implement a prototype and demonstrate that ECO-ID can practically achieve approximately 99.8% localization and 98.7% identification with 0.64 ms mean latency, while theoretically supporting identification at the scale of tens of concurrent users. Overall, ECO-ID provides a fast, privacy-conscious, and security-aware alternative for scalable multi-user identification in time-critical interactive environments.

Interconnected systems have been widely studied, with a focus on interconnected systems whose subsystems are solely input-to-state stable (ISS) or passive systems. The focus of this work is on the interconnected systems that appear in adaptive gradient methods. In adaptive gradient methods, one subsystem seeks to move parameters of a cost function towards a minimizer while the other subsystem works to estimate some derivative information about the cost function to help determine the direction of the parameter update. This work studies instances of such interconnected systems and gives various Lyapunov function constructions for them using different techniques. Global asymptotic stability (GAS) of adaptive gradient optimizers follows from small-gain theory; the Lyapunov functions constructed here certify it explicitly.

Design structure matrices (DSMs) are used to comprehensively represent complex systems. They visualize and describe the dependencies between various variables, processes, states, and events. As such they are used in several system engineering approaches, such as requirement and interface management, fault detection, and supervisory control. Currently, a DSM is typically built from knowledge of experts. This may lead to an incomplete or imbalanced DSMs. For instance, elements and links might be missing or superfluous. In this article, we propose a novel method to acquire the DSM using state-of-the-art network identification methods. This demonstrates a proof-of-principle of identifying DSMs from data as an additional tool to the standard heuristic approach. In the future, we plan to embed DSMs in system design and supervisory controllers. We apply this technique to identify the DSM of a fusion reactor modelled by a five-chamber plasma model describing the transport in a tokamak.

Oscillation is a critical issue that power systems have long faced. Especially over the past two decades, with the large-scale inte-gration of renewable energy into the grid, oscillation problems have posed a serious threat to the secure operation of power systems. However, the current literature has not fully explained the oscillation mechanism of renewable energy integrated power systems (REIPSs). In this paper, the underlying mechanism of stimulated oscillations is explored, with novel analytical methods proposed. Firstly, it is explained from both mathematical formu-las and physical interpretations that for an oscillation mode characterized by a pair of complex conjugate poles, the oscilla-tion risk under disturbance depends on the relative positional relationship between the corresponding poles and all other poles and zeros on the complex plane, rather than their standalone locations, i.e., the stability perceived by classical theory. Then the underlying mechanism of high amplitude oscillations induced by closely-located poles under even slight disturbance is clarified. On this basis, a theoretical framework for stimulated oscilla-tions applicable to REIPSs, covering its definition, mechanism, and methods, is proposed. Finally, this paper discusses the rela-tionship between the stimulated oscillation theory proposed herein and the classical stability-based theory, revealing that the research findings surpass rather than negate the classical theo-ries.

Modern power systems face growing operational complexity driven by the integration of renewable energy sources, decentralization, and the need for real-time decision-making across a wide range of timescales. Addressing these challenges traditionally relies on model-based methods that, while accurate, can be too slow for operational demands. Machine learning (ML) has therefore emerged as a faster, data-driven alternative. As grid topology plays a central role in power system operation, graph machine learning (GML) methods offer a natural framework for incorporating topological dependencies as an inductive bias. We survey nearly 800 papers at the intersection of GML and power systems, covering forecasting, state estimation, optimization, control, fault diagnosis, and cybersecurity. Power systems constitute an unusually rich benchmark setting for GML, as they combine hard physical constraints, multi-scale dynamics, safety-critical requirements, and scarce labeled data within a single, well-defined domain. Conversely, power systems can benefit from utilizing GML to complement classical solvers, as GML provide scalable, topology-aware approximations with promising generalization and computational efficiency. We identify open challenges, including limited real-world deployment and the need for interpretable models in safety-critical settings. Despite the rapidly growing number of publications, standardized benchmarks and open datasets remain scarce, leaving many results difficult to reproduce and undermining the long-term scientific credibility of the field. We further derive a structured requirements catalog for ML-ready power grid benchmarks, intended to guide future dataset development and improve reproducibility across studies. We call on the community to prioritize dedicated benchmark studies and the release of open datasets and models.

This paper proposes a two-layer model predictive control (MPC) framework for the real-time operation of data centers integrated with on-site photovoltaic generation, battery energy storage, waste heat recovery, and district heating. The upper layer employs scenario-based stochastic optimization to jointly optimize intraday market participation, workload scheduling, and energy management under uncertainty. The lower layer adopts an adaptive tube-based MPC strategy that compensates short-term disturbances while tracking the dispatch references given by the upper layer. The framework further integrates multi-horizon forecasting to support real-time decision making. Microservice-based simulation studies under representative clear-sky and overcast operating conditions demonstrate that the proposed framework accurately tracks dispatch plans despite fast photovoltaic and workload fluctuations. Compared with single-layer control strategies, the adaptive lower-layer controller substantially reduces real-time dispatch deviations and the associated imbalance costs. In addition, the proposed framework naturally adapts to seasonal operating conditions and responds to carbon-aware operating signals, offering a practical approach for economically efficient, sustainable, and grid-supportive operation of future data centers.

Multi-step rollouts are essential for model-based reinforcement learning (RL) and predictive control, yet learned dynamics models often become unstable when recursively applied, leading to divergence and unreliable policy updates. This paper proposes a model-agnostic hybrid dynamics framework that blends a provably contracting nominal model with a flexible excursion model through an uncertainty-guided switching law. The switching signal is derived from calibrated epistemic uncertainty and activates only when the system leaves the nominal region, ensuring that each model operates within its reliability regime. Under clearly stated smoothness and boundedness assumptions, we show that the resulting hybrid predictor yields globally bounded recursive multi-step rollouts: trajectories remain Lyapunov-stable in the nominal region and exhibit at most affine growth during excursions. To illustrate the theory in practice, we instantiate the hybrid dynamics framework within a model-based RL scheme that uses real one-step transitions for value learning and hybrid rollouts for policy improvement. Experiments on a nonlinear Duffing oscillator demonstrate stable long-horizon prediction and improved cost-effort trade-offs relative to a stabilizing baseline.

Learning-based Model Predictive Control (MPC) using Gaussian processes (GPs) is an effective approach for safe control in the presence of model mismatch. High-probability safety guarantees typically require uncertainty bounds that hold uniformly over the entire state–input domain, but existing bounds are available only for full GP regression. Since exact GP inference scales poorly with the number of data points, its deployment is impractical in large-data regimes. We close this gap by developing a scalable GP framework that admits the derivation of uniform uncertainty bounds. We formalize a deterministic trigonometric feature Gaussian process (DTF-GP), a finite-dimensional kernel approximation based on discretized trigonometric features that reduces GP regression to Bayesian linear regression in feature space. We derive a high-probability uniform uncertainty bound for the proposed DTF-GP and provide its closed-form solution for the squared-exponential kernel case. Finally, we integrate the DTF-GP into a learning-based MPC scheme and demonstrate that it provides high-probability safety guarantees and exploration performance comparable to a full GP while improving computational efficiency in large-data regimes.

Allocation schemes that greedily maximize a readiness metric over the actuator fiber bundle of an overactuated multirotor produce commands that jump between disconnected optimal strata, demanding actuator rates no motor can deliver; effort-minimizing schemes are continuous but cannot guarantee that wrench-rate authority stays above any certified level. We reconcile the two by treating authority as a forward-invariant quantity: a control barrier function on the log-determinant of the drag-aware actuator-authority co-metric, enforced at torque level by a quadratic program in the allocation null space. A single design inequality renders the certified set compact and strictly interior to the actuator box, with the readiness cost of any rotor deactivation given in closed form as $\ln(n/(n{-}m))$ for symmetric designs. Tracking is sacrificed only through an explicit alignment ratio, with wrench error bounded by $\mathcal{O}(\rho^{-1/2})$ and a robust variant handles motor-parameter uncertainty with a closed-form floor shift independent of the airframe matrix. On a hexarotor and a fully-actuated octorotor the closed-form gap matches simulation to machine precision; in the authority-scarce regime greedy maximization violates the certified floor and commits wrench errors up to eighty times larger than the proposed filter, which holds invariance of the certified set at negligible tracking cost.

Shared autonomy requires principled mechanisms for allocating and transferring control between a human and an autonomous agent. Existing approaches often rely on blending control inputs or heuristic switching rules, which lack theoretical guarantees and fail to account for the dynamics of authority transfer. This paper develops a cooperative game-theoretic framework for authority switching in shared autonomy. We formulate the control switching problem as an identical-interest dynamic game in which authority transitions are embedded into the system dynamics, yielding optimal switching policies rather than ad hoc rules. We establish the existence and characterization of team-optimal policies in pure strategies under stochastic human override, accounting for asymmetric authority where humans retain override capability. For linear-quadratic systems, we derive closed-form recursions for the optimal switching policies and value functions, enabling efficient computation independent of the continuous state. We validate the framework on scalar and multi-dimensional linear systems, demonstrating how optimal switching adapts to varying system dynamics, cost structures, and override probabilities. The results reveal fundamental trade-offs between human adaptability and autonomous efficiency, illustrating the practical benefits of grounding shared autonomy in cooperative game theory.

We study an Estimated Time of Arrival (ETA)-based traffic-coordination framework for Urban Air Mobility corridors with merging at constrained waypoints (CWPs), where approved ETAs at CWPs serve as Required Times of Arrival (RTAs). Vehicle operators submit ETA plans at the merging point for approval by corridor-management authorities before corridor entry. Corridor entry is then scheduled by enforcing pairwise ETA gaps that maintain inter-vehicle separation on shared corridor sections. We develop two trajectory bounds to compute sufficient ETA gaps: a worst-case bound based on prescribed speed limits, and a stochastic bound based on probabilistic position envelopes under acceleration uncertainty. Using these bounds, we formulate sufficient ETA-gap computation and first-come, first-served corridor entrance scheduling. Simulations show that ETA coordination improves safety over an unscheduled baseline. The worst-case bound provides stronger robustness under higher disturbance levels, whereas the stochastic bound allows higher throughput under mild disturbances while relying on probabilistic modeling assumptions.

Shared autonomy requires principled mechanisms for allocating and transferring control between a human and an autonomous agent. Existing approaches often rely on blending control inputs or heuristic switching rules, which lack theoretical guarantees and fail to account for the dynamics of authority transfer. This paper develops a cooperative game-theoretic framework for authority switching in shared autonomy. We formulate the control switching problem as an identical-interest dynamic game in which authority transitions are embedded into the system dynamics, yielding optimal switching policies rather than ad hoc rules. We establish the existence and characterization of team-optimal policies in pure strategies under stochastic human override, accounting for asymmetric authority where humans retain override capability. For linear-quadratic systems, we derive closed-form recursions for the optimal switching policies and value functions, enabling efficient computation independent of the continuous state. We validate the framework on scalar and multi-dimensional linear systems, demonstrating how optimal switching adapts to varying system dynamics, cost structures, and override probabilities. The results reveal fundamental trade-offs between human adaptability and autonomous efficiency, illustrating the practical benefits of grounding shared autonomy in cooperative game theory.

Differentiable nonlinear model predictive control (NMPC) provides a principled way to embed optimal control structure into end-to-end learning paradigms, but its practical use is often limited by the computational and memory costs of both forward optimization and backward sensitivity propagation. This brief proposes PANDA, a matrix-free solver for differentiable NMPC. In the forward pass, PANDA combines proximal-gradient iterations with quasi-Newton acceleration and introduces an adaptive stepsize enlargement mechanism to mitigate the conservativeness of monotone stepsize reduction. The resulting stepsize behavior and its effect on local convergence are theoretically analyzed. In the backward pass, PANDA performs implicit differentiation from the residual equation and computes adjoint sensitivities using Krylov-subspace iterative methods together with automatic-differentiation-based Matrix-Vector product operators, thereby avoiding explicit Hessian and Jacobian construction. The method is evaluated on a nonconvex trailer NMPC problem embedded in an imitation learning task. The results show that PANDA achieves much faster forward and backward computation and lower memory overhead than representative differentiable optimization solvers, while maintaining effective imitation learning performance.

This letter investigates reliability constrained hybrid beamforming for transceiver separated multistatic integrated sensing and communication in vehicular networks. A target position Cramer Rao bound minimization problem is formulated under outage probability, transmit-power, and analog constant modulus constraints. To handle the constrained non convex problem, we develop a proportional-integral Lagrangian proximal policy optimization algorithm. Simulation results show that the proposed algorithm keeps the average outage probability at or below the reliability threshold, around 8%-10%, improves constraint satisfaction, and achieves stable sensing performance.

We study decentralized stochastic gradient tracking over a time-varying network of $N$ agents under a uniform window-mixing condition. Products of $\tau$ consecutive doubly stochastic mixing matrices contract disagreement by a factor $\lambda<1$, although individual matrices need not contract disagreement strictly and individual communication graphs may be disconnected. We construct a time-varying quadratic norm that turns this window contraction into an exact one-step Lyapunov identity. This leads to coupled one-step recursions for the centroid and disagreement errors, without unrolling the dynamics over communication windows. For smooth strongly convex objectives, the leading stochastic term is $\widetilde{\mathcal O}(1/(NK))$; for smooth convex objectives, it is $\mathcal O(1/\sqrt{NK})$. Both match their centralized mini-batch counterparts and yield linear speedup after a network-dependent transient.

Circular manufacturing systems require functional evaluation of used components based on their physical state. Existing approaches describe this state from separate perspectives, such as design, manufacturing, and degradation, resulting in fragmented and incompatible representations. As a consequence, the physical state cannot be reliably linked to the functional behavior of the corresponding subsystem, which is a prerequisite for informed R-strategy decisions. This paper introduces the Unified Embodiment Description (UED), a state-dependent representation of mechanical components structured into two coupled layers. The first layer is a unified characteristic space, which adapts and extends as new lifecycle effects emerge. The second layer consists of functionally derived tolerance regions that link these embodiment characteristics to the functional behavior of the surrounding subsystem. A supporting UED method guides the model-building process of both layers. The UED is demonstrated in a case study on the spindle shaft of an angle grinder, in which manufacturing variations and degradation patterns such as polishing wear and scratches are quantified. These embodiment changes are embedded into the unified characteristic space and translated into functionally derived tolerance regions through experimental testing of the spindle-bearing subsystem. The results show that embodiment changes induced over the lifecycle can be consistently integrated within the unified characteristic space and that the relations between embodiment and functional behavior can be quantified to support end of life decisions. Overall, the UED provides a foundation for embodiment modeling that adapts to component state and enables decision-making based on functional evaluation for used components in circular manufacturing systems.

As Space-Based Information Networks (SBINs) evolve toward high-capacity, intelligence-centric paradigms, integrating sub-Terahertz (sub-THz) communication into Low Earth Orbit (LEO) satellite constellations has emerged as a critical enabler for ultra-broadband and resilient global connectivity. By exploiting the ultra-wide bandwidth of sub-THz links to reduce transmission delays, resource-constrained ground devices can seamlessly offload compute-intensive tasks to LEO edge servers. However, satellite motion, short visibility windows, and limited onboard resources make offloading decisions highly time-varying. Existing distributed offloading schemes typically require repeated inter-device state exchange and poorly adapt to time-varying LEO topology or traffic conditions. To address these limitations, a decentralized game-theoretic offloading framework empowered by large language models (LLMs) and counterfactual inference is proposed in this paper. First, a realistic offloading system is established by integrating time-varying 3D-Walker topology. Second, a game-theoretic scheme using counterfactual inference is introduced to deduce unobserved states from local histories, eliminating global information reliance. Finally, an LLM-empowered semantic fusion algorithm is integrated into the counterfactual inference to enhance adaptability through zero-shot reasoning and self-reflection. Numerical results show that L-COIN reduces offloading cost by 10.9% to 27.7% relative to state-of-the-art baselines.

This study proposes a relative orbit control law for laser debris removal missions considering the uncertainties of laser ablation and atmospheric drag. A removal spacecraft irradiates laser pulses to a target debris to generate the ablation force for deorbiting. The deorbiting force lowers the target altitude, and the removal spacecraft must follow it to maintain its relative position for continuous laser irradiation. The difficulty stems from uncertainties of the magnitude of laser ablation and external disturbances such as atmospheric drag. To tackle this problem, this study derives an adaptive control method using the Gaussian process regression to cancel the uncertainties with a nonparametric regression model. Numerical simulations verify the proposed control law under the uncertainties of laser ablation and atmospheric drag. The proposed control law can contribute to the realization of a safer and more secure mission not only for laser debris removal missions, but also for other on-orbit services.

The rapid growth of resident space objects in Earth's orbit has intensified the need for advanced space situational awareness and space domain awareness to manage satellite traffic and prevent collisions. Attitude estimation is critical for accurate state propagation, as non-gravitational forces like solar radiation pressure and atmospheric drag depend on the object's attitude. This study explores using light curves, time variation of an object's brightness, to estimate a space object's attitude. Light curve inversion, traditionally used in astronomy, faces challenges when applied to resident space objects due to their non-convex shapes and specular reflections. Conventional methods for attitude estimation often assume known shape and surface parameters, which are usually unknown for space debris generated by a collision or breakup. To address this issue, this study proposes the estimation method combining Gaussian process regression with the unscented Kalman filter. This study uses Gaussian process regression for a non-parametric observation model, enhancing robustness against unknown surface parameters. Numerical examples consider a box-wing object in a geosynchronous orbit and demonstrate that the proposed method has better estimation accuracy than a conventional unscented Kalman filter. The numerical simulation results also represent the attitude estimation robust against uncertainties in surface properties, contributing to practical scenarios in space situational awareness and space domain awareness where the object parameters are unknown.

Relative orbit estimation using optical observations is a key technology for on-orbit servicing missions. In the far-range phase, the target appears as an unresolved point source, providing only bearing angles (azimuth and elevation) from the servicing satellite. Angles-only navigation is inherently challenging due to the weak observability of the relative range. To address this limitation, this study investigates the effectiveness of an estimation scheme that fuses photometric light curve data with bearing measurements. Since the light intensity depends on the relative distance, fusing light curves enhances the observability of the relative state. The Ashikhmin-Shirley model is used as the optical reflectance model, and observability analysis is conducted with the Fisher information matrix. Numerical simulations involving different target geometries, a flat plate and a box-wing satellite, demonstrate that integrating light curve measurements significantly enhances observability and enables faster convergence compared to conventional state estimation methods.

Electric vehicles (EVs) represent a critical intelligent terminal within the Internet of Things (IoT). Despite the year-on-year growth in EV penetration, the highway driving experience still requires improvement. Accurate prediction of EV highway charging behavior is crucial to addressing this issue. This paper introduces a novel bounded rationality framework to analyze highway charging decisions. Specifically, we utilize prospect theory to capture the tendency of drivers to reserve more electricity than theoretically necessary. We then propose a Bayesian game in which EV drivers, unaware of others' decisions, aim to minimize costs, including range anxiety, charging fees, and queuing time. To gain insights into the game, we prove the existence and uniqueness of the Bayesian Nash Equilibrium in two practical scenarios. Our numerical experiments, based on real-life data, demonstrate that drivers' risk aversion tendency significantly influence EV charging decisions, charging demand, queuing lengths at charging stations, and the departure rate on the highway network. Furthermore, our strategy reduces cumulative EV cost and CSs' charging costs compared to other benchmarks.