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eess.SY: explore 114 source-linked works published from 2024 to 2026, with original documents and citations.

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Sources: arxiv. Collection updated 2026-09-14. Counts describe this index, not the complete source archives.

arXiv2026

Linear Coding of LTI Sources Over Vector Gaussian Channels: A Majorization Approach

We study the design of linear time-invariant (LTI) encoder-decoder pairs for transmitting the state of a discrete-time LTI vector source over power-constrained parallel Gaussian channels with feedback. Two types of power constraints are considered. Under individual subchannel power constraints, a necessary and sufficient condition for designing an encoder-decoder pair that achieves bounded estimation error covariance (EEC) is established via two coupled majorization inequalities involving the subchannel signal-to-noise ratios and the antistable poles of the source. Under total channel power constraint, we derive the minimum total power required for a feasible encoder-decoder design by exploiting partial-order progamming under majorization order. An analytical optimal power allocation is obtained for the case of equal noise variances, which admits a water-filling interpretation; for general noise case, a sequential water-filling algorithm is developed. Our results reveal that the difficulty of transmitting a discrete-time LTI source via LTI coding is governed not only by its topological entropy, but also by the evenness of the log-magnitudes of its antistable poles. The design methods for feasible encoder-decoder pairs are also provided.

cs.IT↗
arXiv2026

Stimulated Oscillations in Renewable Energy Integrated Power Systems - Part II: Methodology of Oscillation Mitigation

As presented in Part I of this series, closely located poles can produce high-amplitude oscillations even under small perturba-tions, which are referred to as stimulated oscillations. As the second installment of this series, this paper develops a method-ology for stimulated oscillation mitigation. Firstly, the logical relationship between system stability and oscillation risk is in-vestigated, clarifying that stability is neither a necessary nor a sufficient condition for oscillation risk. Secondly, the dominant factors affecting the pole and zero position on the complex plane, as well as their effectiveness and limitations are analyzed. For feedback control systems in particular, the influence of feedback paths on the pole-zero distribution of closed-loop systems is ana-lyzed. On this basis, a methodology for stimulated oscillation mitigation is proposed. Combined with the reduced-order trans-fer function of systems with closely separated poles, the order relation and configuration of poles and zeros for the feedback path transfer function, together with the criteria for gain selec-tion, are elaborated. Finally, a parameter tuning scheme for the poles, zeros and gain of the feedback path transfer function is presented. The proposed methodology for stimulated oscillation mitigation based on feedback control is not restricted to specific devices and can serve as a methodological reference for the design of diverse oscillation suppression schemes.

eess.SY↗
arXiv2026

Adaptive Finite-Time Position-Force Control of Teleoperation Systems With Time-Varying Delays Using a Liquid State Machine Uncertainty Estimator

Teleoperation systems are increasingly used in medical, rehabilitation, and remote manipulation applications, where accurate position/force tracking and stable interaction are essential. In such applications, the remote environment may exhibit viscoelasticity, frictional memory, contact transitions, and other dynamic interaction effects, causing the system response to depend not only on the current state but also on its previous evolution. This history dependence, together with communication delays and uncertain nonlinear dynamics, makes accurate uncertainty compensation particularly challenging. Conventional feedforward neural approximators do not inherently retain temporal information, while fully recurrent architectures may introduce additional computational and online training complexity. To address this limitation, this article introduces the first application of a liquid state machine (LSM) to bilateral teleoperation control. A finite-time adaptive controller is developed using a hybrid position/force auxiliary error system with velocity and force filters, while the LSM is employed to estimate uncertain dynamics by exploiting its intrinsic temporal processing and fading-memory capabilities with a simple adaptation mechanism. Closed-loop stability and finite-time convergence are established through a Lyapunov--Krasovskii framework. Simulations in spring--damper and generalized Maxwell viscoelastic environments demonstrate improved position and force tracking and lower mean execution time compared with an RBFNN-based controller.

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arXiv2026

Conformal Prediction Regions for Continuous-Time Trajectories under Random Sampling

Uncertainty quantification for continuous-time trajectories is a prerequisite in many safety-critical engineering domains. However, a major challenge in data-driven uncertainty quantification is that calibration trajectories are sampled only at discrete, often sparse, and random intervals. Standard conformal prediction methods typically fail to provide guarantees in between sampling times. In this work, we introduce a new technique to obtain valid conformal prediction regions for continuous-time trajectories that are sampled at discrete and possibly random times. To accomplish this goal, we make three contributions: (1) we provide an algorithm that leverages regularity properties of the underlying trajectories to obtain valid prediction regions in between samples, (2) we provide methods that estimate valid bounds on the aforementioned regularity properties from an additional high-frequency calibration dataset, and (3) we introduce and compare several algorithms to deal with random sampling times. Finally, we present experiments where we validate that our methods achieve valid coverage across the entire continuous trajectory.

eess.SY↗
arXiv2026

Asynchronous Cooperative Online Learning for Multi-Robot Control under Computational Delays

Ensuring the safe operation of multi-agent systems (MASs) under uncertain environments is crucial for cooperative robotic, where external disturbances and inaccurate dynamic models can significantly compromise performance and reliability. To address this challenge, calibrated machine learning models, particularly Gaussian process (GP) regression, are extensively employed due to their interpretable performance quantification. As the interconnected communication of MASs facilitates cooperative learning, agents are able to enhance learning performance by exchanging local GP inferences with their neighbors and aggregating the received information via distributed GP strategies. However, variations in computational power and prediction tasks among agents inevitably lead to heterogeneous computational delays and differences in query points, which are often overlooked in existing aggregation methods. To overcome these limitations, this work proposes an asynchronous cooperative learning strategy that explicitly accounts for prediction accuracy, query point variations and delay effects. Additionally, a distributed control law based on an adjoint MAS is developed to ensure the desired control performance. Simulations on unmanned surface vehicles validate the effectiveness of the proposed approach, demonstrating substantial improvements in both learning and control performance compared to the state-of-the-art approaches.

cs.LG↗
arXiv2026

A Small-Gain-Like Framework for Large-Signal Stability Evaluation of Multi-Converter Systems

The increasing penetration of grid-connected converters has greatly altered the large-signal behavior of power systems. Their angle dynamics, shaped by diverse control algorithms and coupled through complex circuit interactions, pose substantial challenges to large-signal stability evaluation of multi-converter systems. To resolve this issue, the small-gain theorem, which characterizes the dissipation capability of interconnected systems (the small-gain-like property) via the individual dissipation capabilities of subsystems, is introduced to investigate transient angle motions in multi-converter systems. A large-signal model involving a set of interconnected relative angle motions is first developed, and the small-gain-like property is then established for multi-converter systems within certain angle limits, which further enables the construction of Lyapunov functions. Based on this, an ellipsoidal forward-invariant region is identified inside the angle-limit region, which serves as an effective estimate of the large-signal stability region for multi-converter systems. The method is further applied to a paralleled system and a four-converter system, where the large-signal stability boundaries are explicitly computed and subsequently validated through experiments. The proposed small-gain-like based large-signal stability evaluation method enables quantitative stability assessment in multi-converter systems with diverse control algorithms, which may provide a scalable framework for large-signal stability evaluation and parameter design in modern power systems.

eess.SY↗
arXiv2026

Event-triggered Control and Online Learning for Networked Systems under Computational Delays

Online learning-based control is a promising approach to control uncertain systems, where unknown components are identified during operation to improve control performance. However, resource-intensive online learning algorithms introduce non-negligible computational delays, especially when executed on systems with limited local computational resources. To mitigate this, an in-network online learning-based control structure is employed by deploying the learning-based controller on a remote computation node and connecting it via a communication channel. In this paper, control performance guarantee is first established by deriving tracking error bound for the in-network control architecture, while accounting for computational delays. The derived tracking error bound allows for diverse communication and computation strategies under a specific condition, including time-/event-triggered mechanisms. Additionally, the trade-off between communication and computation performances is shown for a given desired control performance. Furthermore, to enhance the efficiency in both communication and computation, an efficient control framework with an asynchronous event-triggered mechanism in both control and online learning is devised under the existence of computational delay. The proposed event-triggered strategy is proven to achieve the same control performance as time-triggered scenario while excluding Zeno behavior. Finally, we derive an explicit expression of the proposed event-trigger condition for exponentially stabilizable systems, and demonstrate its effectiveness through simulations.

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arXiv2026

Reciprocal-Manifold Annealed KKT Flows for Constrained Optimization: Application to the Nonconvex AC Optimal Power Flow

Safety-critical optimization applications, such as real-time power system operation, maintain feasibility at every intermediate step, not merely at convergence. Existing approaches either violate constraints mid-solve (interior-point methods) or enforce feasibility through per-instant quadratic programming subproblems with cubic computational cost and unbounded worst-case execution time. We propose a continuous-time optimization framework for smooth constrained nonlinear problems that preserves feasibility throughout the optimization process without requiring projection operators, quadratic programming subproblems, or other per-iteration optimization routines. The method is built around a reciprocal multiplier manifold, which establishes an explicit relationship between inequality constraints and their associated Lagrange multipliers. By designing a continuous multiplier update law, the manifold is shown to remain forward invariant, while the resulting dynamics are equivalent to continuous-time logarithmic barrier gradient descent. The proposed framework naturally extends to multiple inequality constraints, equality constraints, nonconvex feasible sets, and infeasible initial conditions. The method is further enhanced through an augmented Uzawa flow that eliminates oscillatory transients commonly observed in classical primal-dual saddle-point dynamics. The effectiveness of the proposed approach is applied to the AC Optimal Power Flow problem of IEEE 9-bus and IEEE 57-bus systems. Numerical results show convergence to solutions within 0.4\% of the benchmark optimum while maintaining strict feasibility of all constraints. A computational complexity analysis shows that the proposed dynamics reduce the per-step computational cost from cubic to linear complexity. Finally, dynamic tracking studies under time-varying operating conditions demonstrate reliable feasibility preservation.

math.OC↗
arXiv2026

On the Number of Observation Nodes in Recurrent Neural Networks with Linear Threshold and ReLU Functions

This paper investigates how node update rules and admissible state domains affect the minimum number of observation nodes required for global finite-horizon observability in recurrent neural networks with linear-threshold and ReLU update functions. Over a common binary state domain, we construct a class of $K$-linear-threshold ($K$-LT) networks whose initial states can be uniquely reconstructed from the finite output trajectory of a single observation node. We further establish a dynamical equivalence between binary-valued $K$-ReLU networks and $K$-AND Boolean networks, which transfers existing class-level observation-node bounds to binary-valued $K$-ReLU networks. For nonnegative-valued ReLU networks, the observability problem reduces to the classical linear-system setting whenever the relevant pre-activations remain nonnegative. For general real-valued ReLU networks, we prove that global finite-horizon observability requires at least $n/2$ observation nodes when no restriction is imposed on the number of state variables involved in each node update. This lower bound is tight when $n=2K$, for which we construct a $K$-ReLU network observable from exactly $K$ nodes. These results show that both update rules and state domains fundamentally affect extremal observation requirements: temporal evolution can concentrate finite-state information into a single measured trajectory, whereas activation-induced rank loss creates an intrinsic sensor lower bound in continuous-state ReLU networks.

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arXiv2026

Successive design of backstepping observers for parabolic PDE-ODE systems and its duality to state feedback stabilization

The paper introduces a successive backstepping observer design for strictly feedforward parabolic PDE-ODE systems, in which the coupling structure determines the order of error stabilization and the corresponding transformations. First, a transformation based on a virtual measurement stabilizes the ODE observer error subsystem, which is most distal from the measurement, while decoupling it from the PDE error state. Second, a Volterra integral transformation is employed to stabilize the PDE error subsystem and to map the overall error dynamics into a cascade of exponentially stable ODE and PDE subsystems. The design is shown to be dual to a recently proposed multi-step state feedback design for parabolic PDE-ODE systems in strict feedback form, thus explaining the structure of the presented observer design.

math.OC↗
arXiv2026

Identification of $dq$-Asymmetric Impedances as Complex Transfer Functions Using a Single Arbitrary Excitation

Cross-coupling between the $dq$ coordinates makes the identification of asymmetric grid impedances a challenging problem, particularly near the fundamental frequency where the asymmetric coupling is strongest. Existing schemes usually handle it either by perturbing the two coordinates sequentially, which lengthens the measurement, or by using a time-domain method with a global parametric model whose order must be tuned. This paper develops a single-shot active non-parametric frequency-domain method that avoids both. The equivalent impedance is parameterized by a pair of single-input single-output complex transfer functions. Each spectral line is fitted with a local rational model; the leakage and transient contributions are estimated, so that neither periodic steady-state excitation nor repeated excitation cycles are required. We give the exact finite-time discrete Fourier transform relation for the conjugate-coupled complex-signal model, and analyse the distortion that a stationary-frame filter placed ahead of the Park transform imposes on the identified pair. The method is validated on a controller hardware-in-the-loop platform against an analytically derived small-signal model, for a symmetric grid and for the same grid with an added grid-following converter that renders it asymmetric. Both complex transfer functions and all four real transfer functions of the $dq$ impedance are recovered over a wide band from a single one-second record of a random excitation, at 1 Hz resolution.

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arXiv2026

A Unified Perspective on Conformal Prediction and Wasserstein Distributionally Robust Optimization for Uncertainty Quantification

Uncertainty quantification from finite data is central to machine learning, optimization, and automation systems, where decisions must remain reliable under limited samples and test-time distribution shift. Conformal prediction (CP) and distributionally robust optimization (DRO) offer two complementary approaches: CP constructs data-dependent prediction sets with distribution-free finite-sample validity under exchangeability, while DRO optimizes worst-case performance over an ambiguity set around an empirical distribution. We develop a unified probabilistic perspective on CP and DRO by viewing both as ways to turn finite calibration data into a data-dependent quantile estimator that a test score falls below with high probability. From this perspective, CP and DRO correct the empirical quantile along two coordinates of the same family of estimators: CP inflates the quantile level, whereas DRO shifts the quantile value through an ambiguity radius. Both methods provide the same calibration-conditional guarantee for the true distribution, requiring the target coverage to hold with high probability over the calibration sample. Their constructions differ, however: CP uses a closed-form, distribution-free level correction, while DRO uses a value-space correction whose certified radius depends on properties of the unknown distribution and additionally guarantees coverage uniformly over the ambiguity set. This distinction emerges in the tails of the score distribution. Because CP relies on sparse upper-tail order statistics of the calibration samples, its level inflation barely moves the estimator when those samples are dense near the target quantile but overshoots when they are sparse, whereas a well-chosen DRO radius corrects in value space and may avoid this overshoot.

math.OC↗
arXiv2026

SymVD: Symmetric Vision Language Action Distillation for Robot Manipulation

While pretrained Vision-Language-Action (VLA) models offer broad generalization capabilities in robotic manipulation tasks, adapting them to real-world environments or handling task shifts often requires substantial additional data and retraining. To address this, we propose Symmetric VLA Distillation (SymVD), a distillation framework that transfers knowledge from a large VLA teacher to a compact student policy by explicitly exploiting geometric symmetries in manipulation tasks, such as rotational and reflectional invariance. SymVD employs an equivariant actor-critic architecture and trains the student using a symmetry-aware objective that aligns with teacher actions under group-invariant properties. We demonstrate that by enforcing the policy to respect equivariance, SymVD reduces redundant exploration across configurations related by group transformations and improves sample efficiency during distillation. To further stabilize and improve distillation, SymVD introduces an adaptive weighting scheme that dynamically balances the distillation objective and reinforcement learning updates based on training progress, enabling robust transfer even when the teacher signal is imperfect or misaligned. Experimental results on robotic manipulation tasks demonstrate that SymVD consistently improves over standard distillation and also outperforms SAC in terms of sample efficiency and generalization to previously unseen symmetric transformations of the environment.

eess.SY↗
arXiv2026

Quiver Semistability and Structured Kalman Decompositions for Networked Linear Dynamical Systems

We introduce new notions of controllability and observability for networked linear time-invariant (LTI) systems based on $σ$-semistability of quiver representations. Utilizing King's criterion for $σ$-semistability, we define a network generalization of the Kalman decomposition for networked LTI systems, which systematically decomposes the local and interconnection dynamics while respecting the underlying network structure. Furthermore, we present efficient algorithms for deciding the proposed controllability and observability of a given networked LTI system and for finding the Kalman-type decomposition. We also show efficient algorithms for deciding the $σ$-semistability of representations of acyclic quivers with self-loops if the weight $σ$ has the same sign for all vertices with self-loops. Such quiver representations and weights arise from networked LTI systems.

math.OC↗
arXiv2026

Finite Sample Identification of Analytic Nonlinear Systems

This paper studies the identification of linearly parameterized nonlinear (LPN) systems. Although LPN systems share the same linear parameterization structure as linear systems, they are more challenging to identify. In particular, previous work has shown, through a counterexample based on a piecewise-affine system, that non-active exploration is generally insufficient for LPN system identification. In this paper, we consider LPN systems with real-analytic feature functions. We show that non-active exploration is sufficient for the identification of this class of systems by establishing non-asymptotic convergence rates of least-squares estimation and set-membership estimation. In addition, we provide counterexamples to show that non-active exploration may not be sufficient for system identification for non-real-analytic systems, even if those systems are infinitely differentiable. We present numerical experiments to further support and validate our theoretical results.

eess.SY↗
arXiv2026

Integrated Transmission and Distribution Expansion Planning Considering Customer Actions and Distributed Energy Resources

The continued integration of distributed energy resources (DERs) motivates research into integrated system planning. While existing literature shows that DERs can reduce system costs, it often overlooks customer behaviour, particularly DER adoption driven by cost savings and incentives. System cost allocation can influence these decisions, affecting DER uptake, grid injections, and overall system efficiency. Therefore, integrated system planning should consider both system- and customer-level benefits of DERs. This paper proposes a multi-step framework that combines an integrated planning model with cost allocation and retailer business models. The integrated planning model minimizes total system costs subject to network constraints, while the cost allocation and retailer models capture customer responses to costs and DER opportunities. By coordinating these models, the framework represents how customer behaviour influences planning outcomes. The proposed approach is demonstrated on a 36-bus system, with results showing that incorporating customer decision-making reduces overall system costs, lowers reliance on transmission-connected generation, and supports more realistic system planning.

eess.SY↗
arXiv2026

On the suboptimality of stochastic MPC with varying constraint horizon

Enforcing stochastic state constraints over the full prediction horizon in Model Predictive Control (MPC) can be computationally demanding. Here we study stochastic MPC without terminal ingredients in which chance constraints are enforced only over a shorter constraint horizon. Using stochastic relaxed dynamic programming, we derive an explicit upper bound on the average expected closed-loop cost that depends on both prediction and constraint horizons. For linear quadratic problems with affine chance constraints and bounded uniform disturbances, we provide a deterministic reformulation via coordinate transformation and constraint tightening. Simulations illustrate the trade-off between computational effort and performance.

math.OC↗
arXiv2026

MiBOT: A head-worn robot that modulates cardiovascular responses through human-like soft massage

Massage therapy is helpful for the rehabilitation of various diseases, such as headaches caused by migraines and stress. Existing robotic systems have focused on massage therapy on the torso and limbs, but performing massage motions through suitable actuation on a person's head has been a challenge. In this paper, we present MiBOT, a head-worn massage robot that actuates two soft tactors to produce touch motions mimicking human massage. A key design principle behind MiBOT is its silent actuation, which we achieve through pneumatic artificial muscles in conjunction with a controller loop to respond to contact pressure. We evaluated the effectiveness of MiBOT in a controlled study and assessed subjects' blood pressure and heart rate levels while applying MiBOT. We found that our mechanical system generated positive and conclusive quantitative outcomes that are similar to the human-administered massage, decreasing participants' mean systolic and diastolic blood pressure by 2.8 mmHg and 1.7 mmHg, respectively, as well as calming their heart rate by 8-10% on average.

cs.RO↗
Compare source metadata on this page
WorkPublishedSource identifierSource
Linear Coding of LTI Sources Over Vector Gaussian Channels: A Majorization Approach2026-08-302608.29511arxiv
Stimulated Oscillations in Renewable Energy Integrated Power Systems - Part II: Methodology of Oscillation Mitigation2026-08-302608.29518arxiv
Adaptive Finite-Time Position-Force Control of Teleoperation Systems With Time-Varying Delays Using a Liquid State Machine Uncertainty Estimator2026-08-302608.29544arxiv
Conformal Prediction Regions for Continuous-Time Trajectories under Random Sampling2026-08-302608.29559arxiv
Asynchronous Cooperative Online Learning for Multi-Robot Control under Computational Delays2026-08-302608.29562arxiv
A Small-Gain-Like Framework for Large-Signal Stability Evaluation of Multi-Converter Systems2026-08-302608.29570arxiv
Event-triggered Control and Online Learning for Networked Systems under Computational Delays2026-08-302608.29576arxiv
Reciprocal-Manifold Annealed KKT Flows for Constrained Optimization: Application to the Nonconvex AC Optimal Power Flow2026-08-302608.29628arxiv
On the Number of Observation Nodes in Recurrent Neural Networks with Linear Threshold and ReLU Functions2026-08-302608.29650arxiv
Successive design of backstepping observers for parabolic PDE-ODE systems and its duality to state feedback stabilization2026-08-302608.29693arxiv
Identification of $dq$-Asymmetric Impedances as Complex Transfer Functions Using a Single Arbitrary Excitation2026-08-302608.29740arxiv
A Unified Perspective on Conformal Prediction and Wasserstein Distributionally Robust Optimization for Uncertainty Quantification2026-08-302608.29789arxiv
SymVD: Symmetric Vision Language Action Distillation for Robot Manipulation2026-08-302608.29828arxiv
Quiver Semistability and Structured Kalman Decompositions for Networked Linear Dynamical Systems2026-08-302608.29871arxiv
Finite Sample Identification of Analytic Nonlinear Systems2026-08-302608.29908arxiv
Integrated Transmission and Distribution Expansion Planning Considering Customer Actions and Distributed Energy Resources2026-08-302608.29949arxiv
On the suboptimality of stochastic MPC with varying constraint horizon2026-08-302608.30017arxiv
MiBOT: A head-worn robot that modulates cardiovascular responses through human-like soft massage2026-08-302608.30055arxiv

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