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Electrical Engineering and Systems Science Systems and Control

eess.SY Watch Systems and Control

Thursday 3 September

Systems and Control

Taylor-Informed Indirect Adaptive Predictive Control Using Jacobian-Frozen Affine Predictors

Tam W. Nguyen

This paper develops a Taylor-informed indirect adaptive predictive control framework for nonlinear sampled-data systems using Jacobian-frozen affine predictors. A finite Taylor expansion approximates the sampled nonlinear dynamics, and recursive least squares (RLS) identifies its polynomial coefficients online. At each sampling instant, the Jacobian of the identified map is evaluated at the current operating point and frozen over the prediction horizon, yielding an affine predictor for model predictive control. In contrast to generic nonlinear feature dictionaries, the implemented polynomial dictionary is a forward-Euler/Taylor-structure-informed reduced dictionary. Exact joint-odd symmetry eliminates even-total-degree monomials, whereas additional forward-Euler-informed pruning constitutes a deliberate model reduction. Numerical simulations on an unstable nonlinear benchmark compare different Taylor degrees. The results show that higher-order models improve tracking accuracy as the operating point moves farther from the expansion point while maintaining comparable control effort. The complete MATLAB implementation is publicly available to facilitate reproducibility.
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Systems and Control

Traffic Congestion Control for ARZ Model with an Arbitrarily Large Input Delay

Yidan Cao, Xiang Xu and Lu Liu

This paper addresses the stabilization problem for Aw-Rascle-Zhang (ARZ) traffic model in the presence of an arbitrarily large input delay. The linearized ARZ model is a $2 \times 2$ hyperbolic partial differential equation (PDE) system with proximal reflection, which introduces significant analytical challenges when combined with input delays. To tackle this problem, we propose a backstepping-based boundary controller capable of stabilizing the linearized ARZ model under these conditions. The input delay is modeled as a transport PDE, which reformulates the entire system into a $3 \times 3$ hyperbolic PDE system. A backstepping transformation is designed to map the original system into a stable target system, enabling the design of a delay-compensated controller. A key technical contribution of this work is that for hyperbolic PDEs with delays, we develop a characteristic-region-wise construction for kernel functions subject to two boundary constraints and close the proof via successive approximation. Another contribution is that we utilize the small-gain theorem for input-to-state stability (ISS) of hyperbolic PDEs. Two simulations are provided to illustrate the effectiveness of the proposed delay-compensated controller: one compares it with a controller without compensation, and the other employs real traffic vehicle data to validate its effectiveness.
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Systems and Control

On the Degree of Safety: Beyond Safe or Unsafe with Control Barrier Functions

Ruoyu Lin, Fabio Pasqualetti and Magnus Egerstedt

A valid control barrier function (CBF) certifies if its represented safe set can be rendered forward invariant, and the sign of its value indicates whether a state is safe or not, but it does not quantify a degree of safety beyond the binary indication. In this paper, we show that among valid CBFs representing the same safe set, interior values and gradients can be changed arbitrarily, so neither quantity determines a degree of safety that is independent of how the set is represented. We also show that whether a candidate CBF-based inequality constraint is feasible does not by itself quantify a degree of safety. In particular, infeasibility can occur either because the safe set is not controlled invariant or because the candidate CBF representation fails. This motivates our distinction between intrinsic and representational infeasibility. Finally, we introduce the invariance authority demand (IAD), a representation-independent degree of safety that quantifies the control authority required for controlled invariance and can be used to guide set or actuator repair.
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Systems and Control · Cross-listed from cs.LG

Risk and Anomaly Identification for Distribution Network Optimal Operation Based on Reinforcement Learning and Uncertainty Quantification

Ziqi Zhang

Reliable operation of modern distribution networks requires timely identification of operational risks and anomalous events under pervasive uncertainty. In practice, operators must identify risks that are inherent in stochastic yet in-distribution conditions, and anomalies that correspond to out-of-distribution behaviors such as unusual load patterns, extreme weather or cyber-physical attacks. This paper addresses this joint risk and anomaly identification problem for optimal distribution network operation and proposes a deep reinforcement learning framework that is explicitly uncertainty aware. We integrate distributional and Bayesian deep reinforcement learning to realize a second- order uncertainty quantification scheme that decomposes total uncertainty into aleatoric and epistemic components, which are respectively used to characterize inherent risk and out-of- distribution anomalies. The resulting epistemic estimates drive both exploration during training and out-of-distribution detec- tion with fallback control during deployment, whereas aleatoric estimates are used to characterize intrinsic operational risk. Simulation results demonstrate the performance of our DRL agent and the effectiveness of the uncertainty quantification.
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Systems and Control

Coupling-Aware Aggregation of Multi-Zone HVAC Loads under Uncertainty: A Two-level Framework

Jingguan Liu, Han Jiang, Xiaomeng Ai et al. (+6 more)

Aggregating building heating, ventilation, and air-conditioning (HVAC) loads unlocks substantial demand-side flexibility for power systems. Yet multi-zone coupling creates intricate interdependencies and uncertainty propagation, complicating the quantification of aggregate flexibility. To address this issue, this paper proposes a coupling-aware two-level aggregation framework. At the building level, tailored Gaussian elimination and coordinate transformation techniques are employed to recast the high-dimensional thermal dynamics as an equivalent lower-dimensional analytical expression. This expression streamlines the subsequent aggregator-level stage by (i) clarifying the propagation of zone-level uncertainties to the building-level interface, (ii) decoupling intra-building multi-zone coupling from inter-building aggregation, and (iii) providing full-dimensional building-level flexibility sets that enable tractable reformulation. At the aggregator level, existing geometric aggregation approaches are generalized by a newly developed matrix-transformation technique. This technique effectively constructs inner approximations between polytopes of different dimensions, producing closed-form images of high-dimensional multi-zone HVAC flexibility in power subspace. The resulting inner approximation is then recast as a customized separatable linear program that efficiently determines the optimal aggregate parameters. Case studies validate the effectiveness of our framework, highlighting its accuracy, reliability, and scalability.
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All authors Jingguan Liu, Han Jiang, Xiaomeng Ai, Shengshi Wang, Xizhen Xue, Shichang Cui, Jinming Hou, Jiakun Fang, Jinyu Wen

Systems and Control

Preference-Oriented Aggregation of Heterogeneous Distributed Energy Resources for Reserve Dispatch

Jingguan Liu, Xiaomeng Ai, Shichang Cui et al. (+5 more)

Aggregating distributed energy resources (DERs) aims to encode their collective flexibility into a single set for efficient grid dispatch. However, existing aggregation methods are overly conservative for heterogeneous DERs due to two main challenges: 1) dimensional heterogeneity, which complicates the combination of flexibilities across different time dimensions, and 2) type heterogeneity, where diverse and irregular DER profiles hinder accurate approximations, resulting in significant flexibility loss. To resolve these challenges, this paper propose a novel preference-oriented aggregation method for reserve dispatch. For dimensional heterogeneity, we extend existing techniques by reformulating the Minkowski sum as a polytope projection problem using a matrix transformation technique. By unifying DERs in a higher-dimensional space and projecting them back into the aggregate feasible region, the proposed technique effectively aggregates dimensionally heterogeneous DERs. For type heterogeneity, we further develop a distributed aggregation-dispatch coordination framework that incorporates reserve dispatch preferences into aggregation. This framework effectively captures the critical, active aggregate flexibility prioritized in optimal reserve dispatch, thereby significantly reducing the flexibility loss when aggregating type-heterogeneous DERs. Numerical tests validate the effectiveness of our method in addressing both heterogeneities and highlight its promising potential for power systems with high reserve requirements.
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All authors Jingguan Liu, Xiaomeng Ai, Shichang Cui, Xizhen Xue, Shengshi Wang, Jiakun Fang, Wei Yao, Jinyu Wen

Systems and Control

Trajectory-Based Co-Optimization of Arrival Scheduling and Descent Path Design in the Terminal Maneuvering Area

Yutian Pang and John-Paul Clarke

Terminal arrival scheduling and descent procedure design are studied in two largely separate literatures. Scheduling models reduce each aircraft to a travel time and deliver target landing times, and fuel-efficient descent procedures are designed one aircraft at a time with the schedule taken as given, although both decide where an arriving aircraft absorbs delay before final approach. Existing formulations therefore cannot trade a slower, earlier-configuring descent against level track miles, a schedule that is efficient in time can be expensive in fuel, and autonomous or reduced-crew operations will need a single trajectory plan that ground automation and the flight management system both accept. To close this gap, we propose a four-dimensional terminal arrival scheduler that selects each aircraft's lateral path extension, glideslope-capture distance, and flap-deployment trigger speeds in one decision. We evaluate every candidate idle-thrust descent offline with a wind-aware backward plan and a six-degree-of-freedom forward simulation that returns descent time, fuel burn, minimum track length, and stabilized-approach feasibility, and a rolling-horizon scheduler commits one verified descent and one extension per aircraft under wake-separation constraints and observed entry winds. Two Atlanta terminal airspace case studies quantify the benefit. We show that co-optimized continuous descents save about 15% of fleet fuel below saturation in a free-descent environment and that delayed deceleration saves about 23%, while on the six published Runway 8L arrival flows the charted altitude floors remove 31% of the design lattice and reduce the savings to 9–10% and 20–21%, respectively. We also find that wind moves single-aircraft descent fuel by 34–81% yet explains at most 4% of fleet fuel variance, because aircraft-specific wind effects average out across a scenario.
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Systems and Control

Locating Power System Oscillation Sources by Extracting Interharmonics from Synchrophasor Data

Sophia Xu and Wilsun Xu

Recent studies have shown that oscillating phasors arise from beating waves caused by interharmonics. This finding has led to an interharmonic-based oscillation source-location method. But waveform data needed for the method are less available than PMU synchrophasor data. This paper investigates whether interharmonics can be extracted directly from phasor data for similar applications. The results show that, for certain oscillation phenomena, phasor data can support interharmonic extraction and oscillation source location. Sensitivity studies have identified the requirements, and a cloud-based software tool is developed for PMU-based source location.
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Wednesday 2 September

Systems and Control

Development of an EMT model of the Balearic power system

Yousef Pipelzadeh, Dharshana Muthumuni, Farid Mosallat et al. (+5 more)

The Balearic Islands are striving to achieve 100% renewable energy, which poses new challenges in operating the power system securely and reliably. With a higher integration of inverter-based resources (IBRs) and a reduced presence of conventional synchronous generators, the system strength, particularly short circuit levels, becomes weaker and more susceptible to disturbances; and the total inertia of the system becomes lower. Technology enablers are planned to achieve energy transition in the Balearic power system: a new 2x200 MW VSC-HVDC link (bipole with metallic return), Synchronous Compensators (SC) and Battery Energy Storage Systems (BESS), as fully-integrated network components. Detailed ElectroMagnetic Transient (EMT) simulation studies may be needed to analyse stability of the system, due to the high amounts of power-electronics devices in the system. This paper describes the implementation of an EMT model of the Balearic power system.
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All authors Yousef Pipelzadeh, Dharshana Muthumuni, Farid Mosallat, Javier Renedo, Silvia Sanz Verdugo, Antonio Cordón, Edgar Nuño, Macarena Martín

Systems and Control · Cross-listed from physics.ins-det

An extensible software platform for automated operational and characterization testing of scientific cameras

Baolong Chen, Hongfei Zhang, Wei Liu et al. (+5 more)

Multi-detector programs such as Earth 2.0 (ET) require detector characterization across different technologies, operating conditions, and production batches, together with consistent procedures for operation, characterization, and device selection. Traditional laboratory workflows often distribute camera control, illumination, image analysis, and report generation across detector-specific programs and manual steps, slowing iterative development and hindering standardized characterization. We address this gap with PixelXVision, a layered software platform that combines a common camera SDK, preset-based configuration, and a runtime plugin system. Detector-specific behavior is configured through declarative presets, allowing the same host interface to operate CCD, CMOS, and infrared detectors without model-specific core changes. Characterization procedures are implemented as plugins that use a shared object manager to access camera, illumination, storage, and database services. Each run links raw frames, measured operating conditions, configuration, analysis products, and reports into a traceable archive with a queryable index. The platform supports lightweight deployment for iterative single-detector development and structured workflows for multi-detector campaigns. The platform has been applied in characterization testing across CMOS, CCD, and HgCdTe cameras, and its configurable architecture can accommodate additional camera models for subsequent CCD testing for WFST without model-specific changes to the core host software. For operational troubleshooting, the platform integrates ChatPixel, a knowledge-base assistant that provides answers with cited sources when it has relevant information.
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All authors Baolong Chen, Hongfei Zhang, Wei Liu, Zihao Xia, Xin Luo, Qi Feng, Zhe Geng, Jian Wang

Systems and Control · Cross-listed from cs.RO

Real-Time Shape Control of Multi-Segment Soft Robotic Arms Using Koopman Operators with Global and Local Observables

Jiahe Wang, Eron Ristich, Sultan Haidar Ali et al. (+5 more)

Multi-segment soft robotic arms can continuously reconfigure their body shapes for safe interaction, but tip control alone is insufficient for constrained-space tasks. Therefore, shape control is a more important task for multi-segment soft arms than tip control, but remains challenging due to the high dimensionality and nonlinear dynamics of continuum deformation. In existing work, shape control accuracy is defined by the error in the global frame (global shape error). For multi-segment soft arms, using only global shape error as the control objective is insufficient, as segment coupling, gravity-induced loading, and inertial effects become more significant. This difficulty increases with the number of segments. In this paper, we present a Koopman-based model predictive control framework that combines global and local observables, enabling real-time shape control on multi-segment soft robotic arms. The framework is evaluated through numerical and physical experiments. Numerical experiments demonstrate the scalability of the proposed controller by achieving shape control on robots with up to 10 independently actuated segments. The physical experiments demonstrate that the controller is capable of (1) real-time shape control of 3- and 5-segment robotic arms with tip speeds up to 0.6 m/s, (2) robust tracking without retraining, including distal payloads up to 400~g and recovery from a 7~N lateral disturbance, and (3) the potential for future inspection applications through a confined-space demonstration. These results demonstrate that the proposed framework enables dynamic, scalable, and accurate real-time shape control on multi-segment soft robotic arms.
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All authors Jiahe Wang, Eron Ristich, Sultan Haidar Ali, Eric weissman, Lei Zhang, Wanxin Jin, Yi Ren, Jiefeng Sun

Systems and Control

A Reconfigurable Hybrid Convolutional-Fully Connected Neuromorphic Core for Biomedical Edge Inference

Sarah Johari, Suman Kumar, Abhishek Mishra et al. (+2 more)

This work presents a programmable FPGA-based architecture for spiking convolutional neural network (SCNN) inference, with real-time hypoxia classification serving as a biomedical edge application. The architecture implements a hybrid spiking convolutional-fully connected (CNN-FC) topology on a programmable, quantized, layer-based neuromorphic hardware core. Early layers perform spiking convolution using receptive-field connectivity with support for multi-channel kernels and stride, while deeper layers use fully connected spiking layers for classification. A PyTorch-based hardware-software co-design flow enables deployment of trained parameters with quantization and configurability support. The design is first validated on MNIST and Fashion-MNIST, achieving hardware accuracies of up to 98% and 86%, respectively, at 16-bit precision. It is then applied to hypoxia classification using red and infrared photoplethysmography (PPG) signals acquired from a shoulder-mounted sensor, with skin tone included as an additional input channel. The resulting classifier achieves an average hardware accuracy of 88.26% across five folds at 16-bit precision while consuming 1.455 W of dynamic power, demonstrating the feasibility of low-power neuromorphic biomedical classification at the edge.
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All authors Sarah Johari, Suman Kumar, Abhishek Mishra, Anush Lingamoorthy, Nagarajan Kandasamy

Systems and Control · Cross-listed from math.OC

Structural Compatibility and Uniform Stability of Temporally Degenerate Parabolic Systems

Amadou Cissé

Modern feedback design for distributed parameter systems presupposes that the closed-loop dynamics define a well-posed evolution problem. This presupposition becomes nontrivial for temporally degenerate parabolic systems, where temporal degeneracy affects not only the analytical properties of the evolution equation but also the mathematical formulation of the feedback interconnection itself. It is shown that admissible feedback interconnections for temporally degenerate parabolic systems are completely characterized by an operator compatibility condition linking the singular reaction operator with the actuator and observation operators. This characterization removes the singular component of the closed-loop dynamics and reduces the degenerate evolution equation to a regular evolution equation. Building upon this regularized formulation, a critical–residual decomposition yields a uniform exponential stability certificate, which is subsequently extended to the original infinite-dimensional evolution through a finite-to-infinite lifting theorem. A constructive static output feedback synthesis is finally obtained as a consequence of these results. Numerical experiments illustrate the regularization mechanism, validate the stability certificate, and confirm the finite-to-infinite lifting.
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Systems and Control

On a Gradation for Asymptotic Stability

Yigit Narter and Inigo Incer

Classical asymptotic stability guarantees convergence but does not quantify the rate at which convergence occurs. This paper introduces a gradation of asymptotic stability where degree zero corresponds to exponential stability and degree $m>0$ corresponds to algebraic decay of order $t^{-1/m}$. We provide direct and converse Lyapunov characterizations for admissible degrees and conditions for certifying the exact stability degree. Hopf, Bautin, fractional-degree, and time-varying examples demonstrate how the degree identifies the leading stabilizing mechanism.
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Systems and Control

Finite-Sample Limits of Entropy-Based Structure Identification in Discretized Nonlinear Systems

Pratishtha Shukla and James Nutaro

Discretization fundamentally limits structure identification in stochastic systems. When system stochasticity exceeds the discretization resolution, entropy-based methods lose their ability to distinguish which input drives the output. We study this in Fuzzy Inductive Reasoning (FIR), a nonparametric framework for learning dynamical systems from discretized measurements, where the choice of input variables determines both predictive accuracy and the interpretability of the learned input–output relationships. Entropy-based selection targets explainability, i.e., identifying which variables causally drive the output, while mean-squared-error-based selection targets prediction. We introduce a resolution-stochasticity ratio that governs when entropy-based selection is reliable. Three results follow. First, entropy-based selection is consistent below this threshold but loses discriminative power above it, regardless of sample size. Second, using the entropy-selected variables for prediction instead of the MSE-selected ones incurs a closed-form excess prediction risk that grows with input complexity and shrinks with sample size. Third, reliable identification of the causally relevant inputs requires data that scales with the number of input combinations and inversely with the strength of the entropy signal. The theory is validated on a two-state Markov model and demonstrated on a distribution grid reliability dataset analyzing the impact of infrastructure investment, where the goal is to explain which investments drive reliability improvements rather than merely predict outcomes.
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Systems and Control · Cross-listed from cs.RO

GPU-Accelerated Astrodynamics World Models for Spacecraft Rendezvous and Proximity Operations

Duncan Eddy, Isaac R. Ward, Grace Ra Kim et al. (+1 more)

World models are an emerging paradigm in representation learning in which an agent jointly learns state-action dynamics and observation models from offline trajectory data, enabling multi-step planning and trajectory prediction with uncertainty estimates. They have shown strong results in robotics and game environments, but, to the best of our knowledge, have not previously been applied to the space domain. This paper introduces a world model-based approach to cooperative and non-cooperative spacecraft rendezvous and proximity operations. First, we introduce an open-source, JAX-based International Space Station (ISS) docking environment supporting parallel GPU simulation of spacecraft orbit and attitude dynamics, generating the thousands of state-action transitions that world model training requires. Second, we introduce Out-of-this-World-Model, a transformer-based world model that encodes relative kinematic states and body-fixed camera imagery into a latent state and predicts its evolution under commanded thrusts and torques using one-step flow matching. It produces a distribution over future observations, capturing stochastic dynamics and per-timestep uncertainty, and outperforms DreamerV3-style posterior-correction baselines with fewer trainable parameters and hyperparameters. Third, we apply the approach to a capsule autonomously docking with the ISS under keep-out-zone constraints, demonstrating improved sample efficiency and task performance over reinforcement learning baselines (53% versus 29% docking success across ports), better out-of-distribution generalization (on held-out ports the world model more than doubles baseline success, 40% versus 17%), and detection of anomalous objects encountered during approach with 98% classification accuracy. We open-source the simulation environment and model architecture to enable further study of this paradigm.
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All authors Duncan Eddy, Isaac R. Ward, Grace Ra Kim, Mykel J. Kochenderfer

Systems and Control

Dynamic Operational Reserve Margin Assessment from Risk-Constrained Unit Commitment States

Pratishtha Shukla, Shaked Regev, Evan J. R. Brody et al. (+2 more)

We propose Dynamic Reserve Margin (DRM) as a time-varying operational adequacy metric derived from risk-constrained unit commitment (RCUC) states. DRM quantifies reserve adequacy using the additional generation capacity that committed generators can provide within a 5-minute response window relative to uncertainty and contingency reserve requirements. We introduce a complementary Reserve Risk Envelope (RRE) metric to quantify operational reserve headroom in directly interpretable MW terms. A low-margin duration metric is further developed to quantify the persistence of reserve stress over an operating horizon. We use an IEEE 14-bus example to illustrate these concepts, followed by large-scale RCUC case studies under multiple operating scenarios. Results demonstrate that reserve requirements and ramp-accessible reserve capability can vary substantially across operating conditions, and that commitment decisions adapt to maintain reserve adequacy under changing system conditions. The proposed DRM and RRE metrics provide an interpretable operational characterization of reserve adequacy, reveal reserve accessibility and stress persistence that are not captured by conventional reserve margin metrics.
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All authors Pratishtha Shukla, Shaked Regev, Evan J. R. Brody, Charles Foltz, Teja Kuruganti

Systems and Control

Hosting Capacity Assessment of Data Centers with Voltage Ride-Through Capability in Power Systems

Pengyu Ren, Wei Sun and Fei Teng

Large data centers are emerging as concentrated, power-electronic grid loads whose abrupt disconnection or transfer to on-site backup supply during voltage disturbances can remove large demand from the power system, and may create a system-level stability problem. Their interconnection feasibility therefore depends not only on steady-state thermal and voltage limits, but also on whether internal power-conditioning systems can maintain IT service while limiting customer-initiated load reduction. This paper presents a voltage ride-through (VRT)-aware data center and grid co-planning framework that couples transmission-level fault simulation with an internal data center ride-through model. Python-based dynamic simulations generate point-of-interconnection (POI) voltage trajectories under selected network faults, and the resulting waveforms drive an internal model incorporating IT and cooling-load dynamics, DC-link, Uninterruptible Power Supply (UPS) response, and converter apparent power limits. The IEEE 118-bus case study shows that internal VRT capability can become a binding interconnection constraint: steady-state planning alone can overestimate feasible data center capacity, whereas increased UPS converter headroom progressively restores hosting capacity. Under the reduced-order response models studied, the grid-forming mode provides greater ride-through margin than the current-limited grid-following mode under the same network fault conditions. The results further show that VRT constraints can materially change both the total hosting capacity of data centers and its spatial allocation across candidate interconnection buses.
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Systems and Control

Tests on the POD-P controller of INELFE Spain-France VSC-HVDC interconnector

Javier Renedo, Agustín Diaz-García, Gilles Torresan et al. (+11 more)

INELFE interconnector consists of a 2x1000 MW high voltage direct current system based on voltage source converters (VSC-HVDC) interconnecting France and Spain. INELFE VSC-HVDC link is embedded into the high voltage alternating current (HVAC) system. Electromechanical oscillations, also known as power oscillations, are a major concern worldwide. INELFE VSC-HVDC link has specific controllers to damp power oscillations by modulating active (P)- and reactive (Q)-power injections of the VSC converters (POD-P and POD-Q controllers, respectively). The Spanish and French Transmission System Operators (TSOs) carried out a join task force to - Increase the gain of POD-P controller of INELFE VSC-HVDC link. - Make it possible to use the POD-P controller together with angle difference control (ADC) of INELFE VSC-HVDC interconnector. The objective of these modifications is to improve the effectiveness of POD-P controller and, therefore, to increase its contribution to the damping of inter-area oscillations in the Continental Europe (CE) power system. Such changes require (a) extensive simulation studies and (b) extensive tests in different operation modes, in order to ensure the correct behavior of the system. This paper presents simulation studies and field tests on the POD-P controller of INELFE VSC-HVDC interconnector in different modes of operation.
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All authors Javier Renedo, Agustín Diaz-García, Gilles Torresan, Eduardo Lorenzo Cabrera, Antonio Cordón, Silvia Sanz Verdugo, Juan Peiró, Javier Pérez Castro, Gorka Calvo, Aitor Hernández Sautua, Ambroise Petit, Hamza Oukhayi, Samy Akkari, Hani Saad

Systems and Control

Guidelines for the implementation of power oscillation damping controllers in power converters

Javier Renedo, Macarena Martín Almenta, Sergio Martínez Villanueva et al. (+3 more)

One of the most effective ways to damp electromechanical oscillations in power systems is by means of supplementary controllers attached to the different devices in the power system is by means of power system stabilizers (PSS) in synchronous machines or by means of power oscillation damping (POD) controllers in facilities with power converters. In the recent years, the use of POD controllers in voltage source converters (VSCs) with grid-following (GFL) control has been investigated. Although the potential of POD controllers to help to damp inter-area oscillations in power systems is enormous, their correct implementation is not trivial, because their effectiveness is strongly linked to their settings. This paper provides guidelines for the implementation of POD controllers in power converters for application in real-world power systems. The paper proposes compliance criteria for POD controllers using a synthetic test system and a systematic methodology used in Spanish technical standard for monitoring compliance (NTS), considering practical considerations. The paper also includes numerical examples to illustrate compliance criteria for POD controllers in a synthetic test system. A generic power converter with grid-following (GFL) control is used for the analysis by simulation and POD controllers using modulation of active-power injection (POD-P), reactive-power injection (POD-Q) or both simultaneously (POD-PQ) will be analysed. Results were validated in a large-scale power system. The paper concludes that by using appropriate synthetic systems, methodologies and compliance criteria, POD controllers in power converters could be effective to damp electromechanical oscillation in large-scale power systems.
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All authors Javier Renedo, Macarena Martín Almenta, Sergio Martínez Villanueva, Agustín Díaz-García, Antonio Cordón, Davide Gotti

Systems and Control · Cross-listed from eess.SP

A Dry-Contact Ear-EEG System With Continuous Electrode-Skin Impedance Mismatch Monitoring for Motion Artifact Cancellation Using DRL Stimulus

Lohan Atapattu, Sajitha Madugalle, Imasha Nethmal et al. (+6 more)

Dry-contact ear-electroencephalography (Ear-EEG) enables wearable neural monitoring. However, motion induced electrode-skin impedance (ESI) mismatches between electrodes can severely degrade signal quality. To the best of our knowledge, this paper presents the first proof-of-concept dry-contact EarEEG system that uses a driven-right-leg (DRL) stimulus for continuous ESI mismatch monitoring, enabling online adaptive motion artifact cancellation. A 1 kHz sinusoidal stimulus is injected through the DRL electrode. The resulting response to the injected carrier is separated from the EEG using bandpass filtering and demodulation, and then used to extract the ESI mismatch information as the reference input for a normalized least-mean-square adaptive filter followed by a Hampel filtering stage. To evaluate artifact suppression and preservation of neural activity, alpha-band EEG activity was analyzed involving four healthy participants performing head nodding, electrode tapping, and jaw clenching. The system achieved artifact power reductions of 6.5, 12.6, and 9.0 dB (77.6%, 92.8%, and 86.4%, respectively) while alpha-band modulation remained clearly observable after processing. This demonstrates the feasibility of DRL-stimulusbased ESI mismatch monitoring for motion artifact cancellation in wearable dry-contact Ear-EEG.
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All authors Lohan Atapattu, Sajitha Madugalle, Imasha Nethmal, Erandee Jayathilaka, Avishka Herath, Kithmin Wickremasinghe, Simon L. Kappel, Nilan Udayanga, Chamira U. S. Edussooriya

Systems and Control

Economic Model Predictive Control with Policy-Guided Terminal Ingredients

Salim Msaad and Robert D. McAllister

Conventional designs for model predictive control typically rely on terminal costs and constraints derived from a steady state to guarantee closed-loop stability and performance. However, this dependence on a steady-state assumption limits the applicability of this control method to systems in which such a fixed operating point is either not available or not desirable. This work introduces a novel framework, termed policy-guided MPC, to address this limitation. Our approach constructs terminal costs and constraints using a known sub-optimal control policy. Specifically, the terminal region is defined around a center determined by a rollout of the policy, and a penalty on deviation from this center is used to define the terminal cost. This method obviates the need for a steady state or reference trajectory. Closed-loop performance guarantees are established relative to the guiding policy, for both finite and infinite horizon problems. The effectiveness of the proposed framework is demonstrated through numerical simulations on an energy management example.
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Systems and Control

Zonotope-Based Active Exposure of Stealthy Deception Attacks in Sensor-Fusion Systems

Meiqi Tian, Shuo Li and Bingzhuo Zhong

This paper investigates the stealthy attack detection for sensor-fusion cyber-physical systems with unknown-but-bounded noises through the control channel. The detection framework is particularly applicable to sensor-fusion scenarios in which multiple suspicious sensors contributing to the fused estimate may be compromised simultaneously. First, we construct an admissible output set using secure sensors and an attack output set for each attack hypothesis. Then, we introduce a receding-horizon optimization framework to design exposure inputs, namely bounded auxiliary control perturbations injected through the control channel, so as to enlarge the separation between the admissible output set and the attack output sets according to the separation tendency. A sufficient detection condition is further derived, showing that set separation guarantees detectability of the compromised sensors. Moreover, an offline exposure budget guidance is developed to support budget selection before online exposure starts. Simulations on a UAV navigation system under stealthy GNSS and LiDAR attacks validate the proposed method.
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Systems and Control · Cross-listed from cs.RO

Pre-Lane-change Signal in Transitional Autonomous Vehicles: Results from Controlled Experiments

Zeyu Mu, Danjue Chen, Abhinav Sharma et al. (+1 more)

This paper investigates how a production transitional autonomous vehicle (tAV) develops and executes mandatory lane-change decisions. Using 150 controlled mandatory lane changes from the NC-tALC experiments, the study examines whether the eventual target gap is observable before lateral movement begins and how the tAV progresses longitudinally from that pre-lane-change state to lane-change start. Signal time (SigT) is defined as an operational pre-lane-change-start reference point. A Firth logistic regression predicts whether the tAV eventually merges in front of or behind its nearest target-lane vehicle using relative position and relative speed at SigT. Longitudinal progression from SigT to lane-change start is then examined separately for in-position and repositioning cases. The traffic state at SigT contains substantial information about eventual target-gap choice and provides meaningful lead time before lateral movement begins. The proposed formulation predicts whether the tAV remains with its current gap or repositions to a neighboring gap by moving forward or dropping back, including cases with longitudinal overlap and ambiguous current-gap geometry. The model achieves an average five-fold cross-validated accuracy of 0.89. Results also provide preliminary evidence that in-position and repositioning cases follow different longitudinal pathways from SigT to lane-change start. These findings support a two-stage conjecture of the observable lane-change process: longitudinal preparation from SigT to lane-change start, followed by lateral maneuver execution. The formulation applies to in-position, repositioning, and longitudinally overlapping cases, and can support lane-change models that distinguish target-gap choice from lateral-onset timing while representing longitudinal preparation before lateral movement begins.
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All authors Zeyu Mu, Danjue Chen, Abhinav Sharma, George F. List

Systems and Control

Physics-based Online Adaptive Koopman Model Predictive Attitude Control for Combined Spacecraft with Dynamic Uncertainties

Yicheng Sun, Yueyong Lyu, Yuhan Liu et al. (+2 more)

This paper proposes a physics-based adaptive Koopman Model Predictive Control (MPC) strategy for combined spacecraft attitude stabilization under inertia uncertainties and active target maneuverability. A novel, quaternion-based Koopman model is constructed from a set of analytical lifting functions derived from the quaternion kinematics, which provides a more compact and physically interpretable linear representation of the nonlinear dynamics compared with the conventional black-box EDMD and higher-dimensional DCM-based model. Leveraging the linear structure of this nominal model, a gradient descent-based update law is employed to efficiently identify time-varying inertial uncertainties from real-time input/output data. By integrating this adaptive linear model into the MPC framework, the optimal control problem reduces to a computationally efficient Quadratic Program (QP), thereby significantly lowering the online computational burden compared to nonlinear adaptive MPC. Recursive feasibility and regional input-to-state stability are formally established through the design of terminal ingredients for the MPC. The effectiveness and superiority of the proposed strategy are validated through comparative simulations of an attitude stabilization task for combined spacecraft in a high-fidelity 3D simulator.
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All authors Yicheng Sun, Yueyong Lyu, Yuhan Liu, Yanning Guo, Wei Pan

Systems and Control

Effective Range and Optimal Frequency of Through-the-Earth Magnetic Induction Communication

Honglei Ma, Erwu Liu, Wei Ni et al. (+4 more)

Magnetic induction communication (MIC) is a promising technology for through-the-earth (TTE) communication. Previous studies on the MIC range have often overlooked the impact of eddy losses caused by underground materials. For TTE MIC, significant eddy losses complicate the analysis of the effective MIC range, which is vital for optimizing performance but has never been addressed in the literature. Accounting for the conductivity and permittivity of the underground medium, this paper derives the effective MIC range in TTE MIC, along with a closed-from expression that predicts the optimal carrier frequency to maximize this range. Finite element simulations validate the analysis, demonstrating that the optimal carrier frequency can significantly enhance the MIC range. It is also revealed that optimizing the antenna radius is effective in extending the MIC range for TTE and vehicle MIC applications.
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All authors Honglei Ma, Erwu Liu, Wei Ni, Jun Zhu, Zhijun Fang, Rui Wang, Xinyu Qu

Systems and Control

Multivariable Geometric Laplace Transform and Fault Detection in Distributed-Converter Lines

Francisco Manuel Arrabal-Campos, Francisco G. Montoya, Santiago Sánchez-Acevedo et al. (+2 more)

Monitoring a DC line with many distributed power converters is a genuinely spatio-temporal problem: the information about a localized fault travels along the whole conductor and reaches a few measurement points mixed with the dynamics of the line itself. This paper develops a two-dimensional geometric Laplace transform (t,x) -> (s_t,s_x) over a commutative subalgebra of the geometric algebra Cl(4,0), isomorphic to Segre's bicomplex numbers, in which two bivectors B_t and B_x act as independent imaginary units for the temporal and the spatial phase. Because the two phases live in algebraically distinguishable planes, a fault at position x_f leaves a transformed residual that factorizes as F_f(s_t) e^{-s_x x_f}: its temporal nature stays in the first factor and its location can be read as a geometric argument of the second. On this representation we build a transmission-line model of the converter line and its space-time dispersion relation, a distributed control by admittance shaping, including an exact treatment of discrete converter sites (spatial sampling, aliasing, and a per-converter droop realization that is exact on the sub-Nyquist band), and a fault diagnosis chain that detects, localizes and classifies injection-loss, shunt, sensor and local-controller faults, extends to multiple simultaneous faults with automatic order selection, and distinguishes the outage of a plant from a cable defect. As an integral object the transform is known in bicomplex analysis, and with a single independent variable it reduces to the complex Laplace transform; the contribution lies in its geometric embedding and in its operational use for fault diagnosis in distributed-converter networks. All results are reproduced by an accompanying open implementation.
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All authors Francisco Manuel Arrabal-Campos, Francisco G. Montoya, Santiago Sánchez-Acevedo, Raymundo E. Torres-Olguin, Alfredo Alcayde

Systems and Control

Leveraging Time-Causal State Variable Aggregation for Real-Time Schedule of Massive Air Conditioners

Jingguan Liu, Xiaomeng Ai, Shichang Cui et al. (+5 more)

Air conditioner (AC) loads offer promising flexibility for active distribution networks to manage uncertainties, such as those in renewable energy generation, electricity prices, and load demand. However, real-time scheduling of ACs is challenging due to their massive temporal coupling constraints and time-causal uncertainties. To address this, a novel time-causal aggregation-based approximate dynamic programming (TCA-ADP) algorithm is proposed for efficient scheduling. The time-causality requirements for aggregating state variables are first analyzed to align with the real-time sequential decision-making process. Subsequently, an enhanced aggregation model is developed to ensure both high accuracy and adherence to time causality. The aggregation process is further reformulated as a linear program to optimize aggregation parameters and enable tractable computation. Accordingly, the TCA-ADP leverages aggregated state variables to approximate the value function as a new way, balancing computational efficiency and economy against the large value function space of massive ACs. By training the value function offline using historical data, the TCA-ADP efficiently achieves near-optimal real-time scheduling of massive ACs through parallel and closed-form disaggregation. Case studies demonstrate the effectiveness and scalability of the TCA-ADP, highlighting its aggregation accuracy, uncertainty handling, and the trade-off between economy and tractability.
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All authors Jingguan Liu, Xiaomeng Ai, Shichang Cui, Xizhen Xue, Shengshi Wang, Jiakun Fang, Jinyu Wen, Yang Shi

Systems and Control

Continuous-Time Aggregation of Massive Flexible HVAC Loads Considering Uncertainty for Reserve Provision in Power System Dispatch

Jingguan Liu, Xiaomeng Ai, Jiakun Fang et al. (+4 more)

Heating, ventilation, and air conditioning (HVAC) loads, with their rapid response capabilities, can provide considerable intra-hour flexibility on the demand side for reserve provision in order to follow the fast variations of renewables. However, scheduling massive HVACs is challenging due to computation complexity and the uncertainty of outdoor temperature. In this paper, we first introduce a novel continuous-time (CT) aggregation model to reveal the potential intra-hour flexibility of HVACs. For accurate aggregation, a new affine transformation is designed to handle the heterogeneity in high-dimensional feasible region. Further, for reliable aggregation in practical environment, the outdoor temperature uncertainty is constructed by distributionally robust chance constrains and integrated into the aggregation model. Secondly, for the tractable calculation of the proposed CT aggregation model, a cascade of tailored reformulation techniques is proposed, including the Bernstein polynomial spline, polytope projection, and linearization transformation. Thirdly, a customized hierarchical dispatch framework is proposed via incorporating the proposed CT aggregation model into reserve provision in power system dispatch, so as to efficiently schedule massive HVACs to cope with the renewable uncertainty. Case studies verify the effectiveness and scalability of the proposed CT aggregation model in aggregation accuracy, intra-hour flexibility utilization, and uncertainty handling.
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All authors Jingguan Liu, Xiaomeng Ai, Jiakun Fang, Shichang Cui, Shengshi Wang, Wei Yao, Jinyu Wen

Systems and Control · Cross-listed from math.OC

On the invariance of risk-sensitive LQR gain under input randomization

Yeongjun Jang

This paper shows that the optimal gain of the risk-sensitive linear quadratic regulator (LQR) problem is invariant under input randomization, i.e., when the controller deliberately injects noise into the nominal control input. This appears counterintuitive at first glance because certainty equivalence does not hold for risk-sensitive LQR and input randomization inflates the effective process noise. Nonetheless, the gain is preserved because the input noise enters not only the system dynamics but also the cost functional, and its total effect on the gain eventually vanishes. Consequently, the optimal gain and its associated Riccati recursion need not be recomputed, and the increment in the optimal cost can be readily evaluated in closed form. This result facilitates the use of risk-sensitive LQR in applications that employ input randomization for privacy or exploration, such as watermarking for replay attack detection, differential privacy, and path integral control.
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