Papers

We revisit the recently proposed “self-Wiener" (SW) filtering method for robust deconvolution, and generalize it to the classical denoising problem. The resulting estimator, termed generalized SW (GSW) filtering, retains the nonlinear shrinkage structure of SW but introduces a tunable threshold parameter. This tunability enables GSW to flexibly adapt to varying signal-to-noise ratio (SNR) regimes by balancing noise suppression and signal preservation. We derive closed-form expressions for its mean-square error (MSE) performance in both low- and high-SNR regimes, and demonstrate that GSW closely approximates the oracle MMSE at high SNR while maintaining strong robustness at low SNR. Simulation results validate the analytical findings, showing that GSW consistently achieves favorable denoising performance across a wide range of SNRs. Its analytical tractability, parameter flexibility, and close connection to the optimal Wiener filter structure make it a promising tool for practical applications including compressive sensing, sparse signal recovery, and domain-specific shrinkage in wavelet, Fourier, and potentially learned orthonormal representations.

We study joint compression and detection in distributed sensing systems motivated by emerging applications such as IoT-based localization. Two spatially separated sensors observe noisy signals and can exchange only a $k$-bit message over a reliable one-way low-rate link. One sensor compresses its observation into a $k$-bit description to help the other decide whether their observations share a common underlying signal or are statistically independent. We propose a simple extremum-based strategy, in which the encoder sends the index of its largest sample and the decoder performs a scalar threshold test. We derive exact nonasymptotic false-alarm and misdetection probabilities and validate the analysis with representative simulations.

To advance integrated sensing and communications (ISAC) in sixth-generation (6G) extremely large-scale multiple-input multiple-output (XL-MIMO) networks, a low-complexity compressed sensing (CS)-based dictionary design is proposed for wideband near-field (WB-NF) target localization. Currently, the massive signal dimensions in the WB-NF regime impose severe computational burdens and high spatial-frequency coherence on conventional grid-based algorithms. Furthermore, a unified framework exploiting both wideband (WB) and near-field (NF) effects is lacking, and the analytical conditions for simplifying this model into decoupled approximations remain uncharacterized. To address these challenges, the proposed algorithm mathematically decouples the mutual coherence function and introduces a novel angle-distance sampling grid with customized distance adjustments, drastically reducing dictionary dimensions while ensuring low coherence. To isolate the individual WB and NF impacts, two coherence-based metrics are formulated to establish the effective boundaries of the narrowband near-field (NB-NF) and wideband far-field (WB-FF) regions, where respective multiple signal classification (MUSIC) algorithms are utilized. Simulations demonstrate that the CS-based method achieves robust performance across the entire regime, and the established boundaries provide crucial theoretical guidelines for WB and NF effect decoupling.

This paper investigates the physical layer security of a non-orthogonal multiple access (NOMA) system assisted by a tertiary-mode simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS), which can perform transmission, reflection, and jamming simultaneously. The system comprises a base station (BS) serving two users located on opposite sides of the STAR-RIS, assuming perfect channel state information (CSI) at the transmitter. To enhance secrecy performance, a subset of STAR-RIS elements is adaptively configured for jamming. A penalty-based alternating optimization algorithm is developed to jointly optimize the BS's active beamforming and the STAR-RIS's passive beamforming and mode selection. Simulation results demonstrate that the proposed design substantially improves the achievable sum rate and secrecy performance compared to conventional RIS-assisted and no-RIS benchmarks, highlighting the potential of tertiary-mode STAR-RIS for secure and efficient next-generation wireless communications.

Microwave-frequency acoustic waves in solids have emerged as a versatile platform for both classical and quantum applications. While phononic integrated devices and circuits are being developed on various material platforms, an ideal phononic integrated circuit (PnIC) platform should simultaneously support low-loss waveguide structures, high-quality-factor resonators, high-performance modulators, and efficient electromechanical transducers. Here, we establish a low-loss gigahertz-frequency PnIC platform based on patterned thin-film silicon nitride (SiN) on lithium niobate (LN) substrate. We develop low-loss PnIC building blocks including waveguides, directional couplers, and high-quality-factor (high-Q) ring resonators. As an application, we demonstrate a 1-GHz phononic oscillator based on a ring resonator, reaching a low phase noise of -159.0 dBc/Hz at a 100-kHz offset frequency. Our low-loss PnICs could meet the requirements in microwave acoustics, quantum phononics, and integrated hybrid systems combining phonons, photons, superconducting qubits, and solid-state defects.

Replay attacks comprise replaying previously recorded sensor measurements and injecting malicious signals into a physical plant, causing great damage to cyber-physical systems. Replay attack detection has been widely studied for linear systems, whereas limited research has been reported for nonlinear cases. In this paper, the replay attack is studied in the context of a nonlinear plant controlled by an observer-based output feedback controller. We first analyze replay attack detection using an innovation-based detector and reveal that this detector alone may fail to detect such attacks. Consequently, we turn to a watermark-based design framework to improve the detection. In the proposed framework, the effects of the watermark on attack detection and closed-loop system performance loss are quantified by two indices, which exploit the incremental gains of nonlinear systems. To balance the detection performance and control system performance loss, an explicit optimization problem is formulated. Moreover, to achieve a better balance, we generalize the proposed watermark design framework to co-design the watermark, controller and observer. Numerical simulations are presented to validate the proposed frameworks.

We present a framework for safety-constrained optimal control when the plant dynamics are unknown and only an approximate model is available. We apply Pontryagin's Minimum Principle to a model-based problem whose cost penalizes model-plant state mismatch and establish conditions under which the constrained model and plant Hamiltonians have the same minimizer. These conditions show that optimality can be recovered by preserving control-relevant Hamiltonian structure rather than identifying the complete plant dynamics. Motivated by this observation, we develop a fully online method that uses integral trajectory data and policy iteration to learn the dynamics-dependent factors of the plant Hamiltonian gradient directly. Experiments on robotic cruise control demonstrate constrained model-plant control equivalence and online Hamiltonian-gradient reconstruction.

This paper presents an adaptive causal discrete-time filter for derivative estimation, exemplified by its use in estimating relative velocity in a mechatronic application. The filter is based on a constrained least squares estimator with window adaptation. It demonstrates low sensitivity to low-amplitude measurement noise, while preserving a wide bandwidth for large-amplitude changes in the process signal. Favorable performance properties of the filter are discussed and demonstrated in a practical case study of PID feedback controller and compared experimentally to a standard linear low-pass filter-based differentiator and a robust sliding-mode based homogeneous differentiator.

Measuring sediment transport in riverbeds has long been a challenging research problem in geomorphology and river engineering. Traditional approaches rely on direct measurements using sediment samplers. Although such measurements are often considered ground truth, they are intrusive, labor-intensive, and prone to large variability. As an alternative, sediment flux can be inferred indirectly from the kinematics of migrating bedforms and temporal changes in bathymetry. While such approaches are helpful, bedform dynamics are nonlinear and multiscale, making it difficult to determine the contributions of different scales to the overall sediment flux. Fourier decomposition has been applied to examine bedform scaling, but it treats spatial and temporal variability separately. In this work, we introduce Dynamic Mode Decomposition (DMD) as a data-driven framework for analyzing riverbed evolution. By incorporating this representation into the Exner equation, we establish a link between modal dynamics and net sediment flux. This formulation provides a surrogate measure for scale-dependent sediment transport, enabling new insights into multiscale bedform-driven sediment flux in fluvial channels.

We consider the problem of learning-based man-in-the-middle (MITM) attacks in cyber-physical systems (CPS), and extend our previously proposed Bellman Deviation Detection (BDD) framework for model-free reinforcement learning (RL). We refine the standard MDP attack model by allowing the reward function to depend on both the current and subsequent states, thereby capturing reward variations induced by errors in the adversary's transition estimate. We also derive an optimal system-identification strategy for the adversary that minimizes detectable value deviations. Further, we prove that the agent's asymptotic learning time required to secure the system scales linearly with the adversary's learning time, and that this matches the optimal lower bound. Hence, the proposed detection scheme is order-optimal in detection efficiency. Finally, we extend the framework to asynchronous and intermittent attack scenarios, where reliable detection is preserved.

Natural language (NL) navigation for low-altitude unmanned aerial vehicles (UAVs) offers an intelligent and convenient solution for low-altitude aerial services by enabling an intuitive interface for non-expert operators. However, deploying this capability in urban environments necessitates the precise grounding of underspecified instructions into safety-critical, dynamically feasible motion plans subject to spatiotemporal constraints. To address this challenge, we propose a unified framework that translates NL instructions into Signal Temporal Logic (STL) specifications and subsequently synthesizes trajectories via mixed-integer linear programming (MILP). Specifically, to generate executable STL formulas from free-form NL, we develop a reasoning-enhanced large language model (LLM) leveraging chain-of-thought (CoT) supervision and group-relative policy optimization (GRPO), which ensures high syntactic validity and semantic consistency. Furthermore, to resolve infeasibilities induced by stringent logical or spatial requirements, we introduce a specification repair mechanism. This module combines MILP-based diagnosis with LLM-guided semantic reasoning to selectively relax task constraints while strictly enforcing safety guarantees. Extensive simulations and real-world flight experiments demonstrate that the proposed closed-loop framework significantly improves NL-to-STL translation robustness, enabling safe, interpretable, and adaptable UAV navigation in complex scenarios.

This paper addresses the security allocation problem within networked control systems, which consist of multiple interconnected control systems under the influence of two opposing agents: a defender and a malicious adversary. The adversary aims to maximize the worst-case attack impact on system performance while remaining undetected by launching stealthy data injection attacks on one or several interconnected control systems. Conversely, the defender's objective is to allocate security resources to detect and mitigate these worst-case attacks. A novel centrality-based approach is proposed to guide the allocation of security resources to the most connected or influential subsystems within the network. The methodology involves comparing the worst-case attack impact for both the optimal and centrality-based security allocation solutions. The results demonstrate that the centrality measure approach enables significantly faster allocation of security resources with acceptable levels of performance loss compared to the optimal solution, making it suitable for large-scale networks. The proposed method is validated through numerical examples using Erdos-Renyi graphs.

Data-driven modeling is playing an increasing role in robotics and control, yet standard learning methods typically ignore the geometric structure of nonholonomic systems. As a consequence, the learned dynamics may violate the nonholonomic constraints and produce physically inconsistent motions. In this paper, we introduce a structure-preserving Gaussian process (GP) framework for learning nonholonomic dynamics. Our main ingredient is a nonholonomic matrix-valued kernel that incorporates the constraint distribution directly into the GP prior. This construction ensures that the learned vector field satisfies the nonholonomic constraints for all inputs. We show that the proposed kernel is positive semidefinite, characterize its associated reproducing kernel Hilbert space as a space of admissible vector fields, and prove that the resulting estimator admits a coordinate representation adapted to the constraint distribution. We also establish the consistency of the learned model. Numerical simulations on a vertical rolling disk illustrate the effectiveness of the proposed approach.

This paper proposes a model predictive trajectory tracking approach for quadrotor UAVs subject to input constraints. Our proposed approach relies on a hierarchical control strategy with an outer-loop feedback generating the required thrust and desired attitude, and an inner-loop feedback regulating the actual attitude to the desired one. For the outer-loop translational dynamics, the generation of the virtual control input is formulated as a constrained model predictive control problem with time-varying input constraints and a control strategy, endowed with uniform global asymptotic stability guarantees, is proposed. For the inner-loop rotational dynamics, a hybrid geometric controller is adopted, achieving semi-global exponential tracking of the desired attitude. Finally, we prove that the overall cascaded system is semi-globally asymptotically stable. Simulation results illustrate the effectiveness of the proposed approach.

This paper studies the problem of decentralized learning of Coarse Correlated Equilibrium (CCE) in aggregative Markov games (AMGs), where each agent's instantaneous reward depends only on its own action and an aggregate quantity. Existing CCE learning algorithms for general Markov games are not designed to leverage the aggregative structure, and research on decentralized CCE learning for AMGs remains limited. We propose an adaptive stage-based V-learning algorithm that exploits the aggregative structure under a fully decentralized information setting. Based on the two-timescale idea, the algorithm partitions learning into stages and adjusts stage lengths based on the variability of aggregate signals, while using no-regret updates within each stage. We prove the algorithm achieves an epsilon-approximate CCE in O(S Amax T5 / epsilon2) episodes, avoiding the curse of multiagents which commonly arises in MARL. Numerical results verify the theoretical findings, and the decentralized, model-free design enables easy extension to large-scale multi-agent scenarios.

This paper presents a velocity-free position control strategy for quadrotor unmanned aerial vehicles based on nonlinear negative imaginary (NNI) systems theory. Unlike conventional position control schemes that require velocity measurements or estimation, the proposed approach achieves asymptotic stability using only position feedback. We establish that the quadrotor horizontal position subsystem, when augmented with proportional feedback, exhibits the NNI property with respect to appropriately defined horizontal thrust inputs. A strictly negative imaginary integral resonant controller is then designed for the outer loop, and robust asymptotic stability is guaranteed through satisfaction of explicit sector-bound conditions relating controller and plant parameters. The theoretical framework accommodates model uncertainties and external disturbances while eliminating the need for velocity sensors. Simulation results validate the theoretical predictions and demonstrate effective position tracking performance.

This paper extends the forward-backward consistency index, originally introduced in Koopman modeling of systems without input, to the setting of control systems, providing a closed-form computable measure of accuracy for data-driven models associated with the Koopman Control Family (KCF). Building on a forward-backward regression perspective, we introduce the control forward-backward consistency matrix and demonstrate that it possesses several favorable properties. Our main result establishes that the relative root-mean-square error of KCF function predictors is strictly bounded by the square root of the control consistency index, defined as the maximum eigenvalue of the consistency matrix. This provides a sharp, closed-form computable error bound for finite-dimensional KCF models. We further specialize this bound to the widely used lifted linear and bilinear models. We also discuss how the control consistency index can be incorporated into optimization-based modeling and illustrate the methodology via simulations.

Reconfigurable intelligent surfaces have emerged as a cutting-edge technology for next-generation wireless communications that are capable of reconfiguring the wireless environment using a large number of cost-effective reflecting elements. However, a significant body of prior studies has focused on single-layer surfaces that lack the capability of significantly mitigating inter-user interference. Moreover, previous studies mostly consider far-field operation and neglect working in the near-field region. In this paper, we propose a stacked intelligent metasurfaces (SIM)-assisted near-field multi-user multiple-input-single-output covert communication system. More specifically, we have a multi-antenna base station that is assisted with a SIM to serve multiple single-antenna users in the presence of multiple single-antenna wardens. We aim at optimizing the beamfocusing vectors at the BS and SIM phase shift matrices to maximize the sum covert rate under maximum transmit power budget constraint, quality-of-service (QoS) constraint for all users, and covertness constraint. Since the formulated problem is highly non-convex due to the coupling between the variables, we adopt alternating optimization to tackle it, where we divide the problem into beamfocusing sub-problem and SIM phase shift sub-problem, which are solved alternately until convergence. We leverage successive convex approximation (SCA) to solve the two sub-problems. Additionally, we formulate the SIM phase shift sub-problem using the widely adopted projected gradient ascent (PGA) method for comparison purposes. The conducted simulations reveal that the SCA-based algorithm outperforms the existing PGA-based algorithm as well as other benchmarks in terms of the achieved sum covert rate, demonstrating its consistent performance and robustness under various system parameter configurations.

Movable antennas (MAs) enable the reconfiguration of array geometry within a bounded region to exploit sub-wavelength spatial degrees of freedom in wireless communication and sensing systems. However, most prior research has predominantly focused on the communication and sensing performance, overlooking the mechanical power consumption inherent in antenna movement. To bridge this gap, this paper investigates a velocity profile optimization framework for MA-assisted direction-of-arrival (DoA) estimation, explicitly balancing sensing accuracy with mechanical energy consumption of MAs. We first establish a Newtonian-based mechanical energy model, and formulate a functional optimization problem for sensing energy efficiency (EE) maximization. By applying the calculus of variations, this formulation is transformed into an infinite-dimensional problem defined by the Euler-Lagrange equation. To solve it, we propose a spectral discretization framework based on the Galerkin method, which expands the velocity profile over a sinusoidal basis. In the regime where energy consumption is dominated by linear damping, we prove that the optimal velocity profile follows a closed-form sinusoidal shape. For more general scenarios involving strong nonlinear aerodynamic drag, we leverage the Markov-Lukács theorem to transform the kinematic constraints into strictly convex sum-of-squares (SOS) conditions. Consequently, the infinite-dimensional problem is reformulated as a tractable finite-dimensional nonlinear algebraic system, which is solved by a two-layer algorithm combining Dinkelbach's method with successive convex approximation (SCA). Numerical results demonstrate that our optimized velocity profile significantly outperforms baselines in terms of EE across various system configurations. Insights into the optimized velocity profiles and practical design guidelines are also provided.

Analytical modeling of field-assisted molecular communication under dynamic electric fields is fundamentally challenging due to the coupling between stochastic transport and complex boundary geometries, which renders conventional partial differential equation (PDE) approaches intractable. In this work, we introduce an effective stochastic modeling approach to address this challenge. By leveraging trajectory-reweighting techniques, we derive analytically tractable channel impulse response (CIR) expressions for both fully-absorbing and passive spherical receivers, where the latter serves as an exact theoretical baseline to validate our modeling accuracy. Building upon these models, we establish a dynamic waveform design framework for system optimization. Under a maximum a posteriori decision-feedback equalizer (MAP-DFE) framework, we show that the first-slot received probability serves as the primary determinant of the bit error probability (BEP), while inter-symbol interference manifests as higher-order corrections. Exploiting the monotonic response of the fully-absorbing architecture and using the limitations of the passive model to justify this strategic focus, we reformulate BEP minimization into a distance-based optimization problem. We propose a unified, low-complexity Maximize Received Probability (MRP) algorithm, encompassing the Maximize Hitting Probability (MHP) and Maximize Sensing Probability (MSP) methods, to dynamically enhance desired signals and suppress inter-symbol interference. Numerical results validate the accuracy of the proposed modeling approach and demonstrate near-optimal detection performance.

In this work, we propose a zero constellation for binary modulation on conjugate-reciprocal zeros (BMOCZ), called jutted BMOCZ (J-BMOCZ), and study its application to non-coherent orthogonal frequency division multiplexing (OFDM). With J-BMOCZ, we introduce asymmetry to the zero constellation for Huffman BMOCZ, which removes ambiguity at the receiver under a uniform rotation of the zeros. The asymmetry is controlled by the magnitude of "jutted" zeros and enables the receiver to estimate zero rotation using a simple cross-correlation. The proposed method, however, leads to a natural trade-off between asymmetry and zero stability. Accordingly, we introduce a reliability metric to measure the stability of a polynomial's zeros under an additive perturbation of the coefficients, and we apply the metric to optimize the J-BMOCZ zero constellation parameters. We then combine the advantages of J-BMOCZ and Huffman BMOCZ to design a hybrid waveform for OFDM with BMOCZ (OFDM-BMOCZ). The pilot-free waveform enables blind synchronization/detection and has a fixed peak-to-average power ratio that is independent of the message. Finally, we assess the proposed scheme through simulation and demonstrate non-coherent OFDM-BMOCZ using low-cost software-defined radios.

Effective co-optimization of energy management strategy (EMS) and thermal management (TM) is crucial for optimizing fuel efficiency in hybrid electric vehicles (HEVs). Driving conditions significantly influence the performance of both EMS and TM in HEVs. This study presents a novel driving condition-aware integrated thermal and energy management (ITEM) framework. In this context, after analyzing and segmenting driving data into micro-trips, two primary features (average speed and maximum acceleration) are measured. Using the K-means approach, the micro-trips are clustered into three main groups. Finally, a deep neural network is employed to develop a real-time driving recognition model. An ITEM is then developed based on multi-agent deep reinforcement learning (DRL), leveraging the proposed real-time driving recognition model. The primary objectives are to improve the fuel economy and reduce TM power consumption while maintaining a pleasant cabin temperature for passengers. Our simulation results illustrate the effectiveness of the suggested framework and the positive impact of recognizing driving conditions on ITEM, improving fuel economy by 16.14% and reducing TM power consumption by 8.22% compared to the benchmark strategy.

We present a flow tube reactor design for gas-phase kinetics studies near ambient temperature and pressure. Built entirely from standard tubing, the setup simplifies conventional flow tube configurations based on injector translation while allowing tighter adjustment of reaction time. The reactor spans residence times from sub-second to several minutes through two operating modes: (i) a variable-length mode, in which reaction time is controlled by the tube length, and (ii) a variable-flow mode, in which the second arm acts as an exhaust branch that decouples reactor pressure from inlet flows while allowing the reactor flow rate to be adjusted over a setup-dependent range. Key advantages include a narrow and well-characterized residence time distribution, rapid radial mixing in millimeter-scale tubing, low wall reactivity through the use of perfluoroalkoxy alkane (PFA), and operation of the reaction section at nearly uniform pressure independently of detector constraints. We characterize the residence time distribution and demonstrate reactor performance with the ozonolysis of 2,3-dimethyl-2-butene. Overall, the method provides a compact, low-cost, and versatile alternative to conventional movable-injector flow tubes, with potential applications in atmospheric chemistry, fundamental kinetics, and gas–liquid or gas–solid uptake studies.

Digital Video Broadcasting Satellite, Second Generation and its extension DVBS2X are widely used in modern satellite communications, where synchronization relies on physical layer headers, pilot symbols, and optional superframe structures but lacks defined implementation methods. This work explores the use of external synchronization to enhance DVBS2 performance by using GPS disciplined oscillators, and a hardware software in the loop satellite channel model emulating Low Earth Orbit propagation. We evaluate scenarios with and without Doppler shifts and radio frequency interference, comparing synchronized and unsynchronized cases. Results show that external synchronization significantly improves bit error rate, frame error rate, and signal-to-noise ratio, subsequently reducing the frames required for reliable synchronization and enabling higher throughput in future satellite communication systems.

This paper investigates the nonlinear dynamics and phase transitions in power packet network connected with routers, conceptualized as macroscopic information-ratchets. In the emerging paradigm of cyber-physical energy systems, the interplay between stochastic energy fluctuations and the thermodynamic cost of control information defines fundamental operational limits. We first formulate the dynamics of a single router using a Langevin framework, incorporating an exponential cost function for information acquisition. Our analysis reveals a discontinuous (first-order) phase transition, where the system adopts a strategic abandon of regulation as noise intensity exceeds a critical threshold $D_c$. This transition represents a fundamental information-barrier inherent to autonomous energy management. Here, we extend this model to network configurations, where multiple routers are linked through diffusive coupling, sharing energy between them. We demonstrate that the network topology and coupling strength significantly extend the bifurcation points, with collective resilient behaviors against local fluctuations. These results provide a rigorous mathematical basis for the design of future complex communication-energy network, suggesting that the stability of proposed systems is governed by the synergistic balance between physical energy flow and the thermodynamics of information exchange. It will serve to design future complex communication-energy networks, including internal energy management for autonomous robots.

When the system is linear, why should learning be nonlinear? Linear dynamical systems, the analytical backbone of control theory, signal processing and circuit analysis, have exact closed-form solutions via the state transition matrix. Yet when system parameters must be inferred from data, recent neural approaches offer flexibility at the cost of physical guarantees: Neural ODEs provide flexible trajectory approximation but may violate physical invariants, while energy preserving architectures do not natively represent dissipation essential to real-world systems. We introduce Lie Generator Networks (LGN), which learn a structured generator A and compute trajectories directly via matrix exponentiation. This shift from integration to exponentiation preserves structure by construction. By parameterizing A = S - D (skew-symmetric minus positive diagonal), stability and dissipation emerge from the underlying architecture and are not introduced during training via the loss function. LGN provides a unified framework for linear conservative, dissipative, and time-varying systems. On a 100-dimensional stable RLC ladder, standard derivative-based least-squares system identification can yield unstable eigenvalues. The unconstrained LGN yields stable but physically incorrect spectra, whereas LGN-SD recovers all 100 eigenvalues with over two orders of magnitude lower mean eigenvalue error than unconstrained alternatives. Critically, these eigenvalues reveal poles, natural frequencies, and damping ratios which are interpretable physics that black-box networks do not provide.

This paper presents a dissipativity-based distributed droop-free control and communication topology co-design framework for voltage regulation and current sharing in DC microgrids (MGs), where constant-power loads (CPLs) and voltage-source converter (VSC) input saturation introduce significant nonlinearities. In particular, CPLs introduce an inherently destabilizing nonlinearity, while VSC input saturation imposes hard amplitude constraints on applicable control input at each distributed generator (DG), collectively making the DC MG control system design extremely challenging. To this end, the DC MG is modeled as a networked system of DGs, transmission lines, and loads coupled through a static interconnection matrix. Each DG is equipped with a local PI-based controller with an anti-windup compensator and a distributed consensus-based global controller, from which a nonlinear networked error dynamics model is derived. The CPL nonlinearity is characterized via sector-boundedness with the S-procedure applied directly to yield tight LMI conditions, while the VSC input saturation is handled via a dead-zone decomposition and sector-boundedness, with both nonlinearities simultaneously absorbed into the dissipativity analysis. Both nonlinearities are simultaneously absorbed into the dissipativity analysis using the S-procedure. Subsequently, local controller gains and passivity indices, and distributed controller gains and the communication topology are co-designed by solving a sequence of local and global Linear Matrix Inequality (LMI) problems, enabling a one-shot co-design process that avoids iterative procedures. The effectiveness of the proposed framework is validated through simulation of an islanded DC MG under multiple operating scenarios, demonstrating robust performance superior to conventional control approaches.

We consider the constrained stabilization problem of second-order systems evolving on the n-sphere. We propose a control strategy with a constraint proximity-based dynamic damping mechanism that ensures safe and almost global asymptotic stabilization of the target point in the presence of star-shaped constraints on the n-sphere. It is also shown that the proposed approach can be used to deal with constrained rigid-body attitude stabilization. The effectiveness of our approach is demonstrated through simulation results on the 2-sphere and the 3-sphere in the presence of star-shaped constraint sets.

In adaptive-sampling control, the control frequency can be adjusted during task execution. Ensuring that these changes do not jeopardize the safety of the system being controlled requires attention. We introduce robust M-step hold model predictive control (MPC) to address this. Our formulation provides robust constraint satisfaction for an uncertain discrete-time system model with a fixed sampling time subject to an adaptable multi-step input hold (referred to as M-step hold). We show how to ensure recursive feasibility of the MPC utilizing M-step hold extensions of robust invariant sets, and demonstrate how to enable safe adaptive-sampling control via the online selection of M. We evaluate the utility of the robust M-step hold MPC formulation in a cruise control example.

Polarization imaging captures the polarization state of light, revealing information invisible to the human eye yet valuable in domains such as biomedical diagnostics, autonomous driving, and remote sensing. However, conventional polarization cameras are often expensive, bulky, or both, limiting their practical use. Lensless imaging offers a compact, low-cost alternative by replacing the lens with a simple optical element like a diffuser and performing computational reconstruction, but existing lensless polarization systems suffer from limited reconstruction quality. To overcome these limitations, we introduce a RGB-guided lensless polarization imaging system that combines a compact polarization-RGB sensor with an auxiliary, widely available conventional RGB camera providing structural guidance. We reconstruct multi-angle polarization images for each RGB color channel through a two-stage pipeline: a physics-based inversion recovers an initial polarization image, followed by a Transformer-based fusion network that refines this reconstruction using the RGB guidance image from the conventional RGB camera. Our two-stage method significantly improves reconstruction quality and fidelity over lensless-only baselines, generalizes across datasets and imaging conditions, and achieves high-quality real-world results on our physical prototype lensless camera without any fine-tuning.