Models that contain intersample behavior are important for control design of systems with slow-rate outputs. The aim of this paper is to develop a system identification technique for fast-rate models of systems where only slow-rate output measurements are available, e.g., vision-in-the-loop systems. In this paper, the intersample response is estimated by identifying fast-rate models through least-squares criteria, and the limitations of these models are determined. In addition, a method is developed that surpasses these limitations and is capable of estimating unique fast-rate models of arbitrary order by regularizing the least-squares estimate. The developed method utilizes fast-rate inputs and slow-rate output measurements and identifies fast-rate models accurately in a single identification experiment. Finally, both simulation and experimental validation on a prototype wafer stage demonstrate the effectiveness of the framework.

Grid-forming inverters control the power transfer between the AC and DC sides of an electrical grid while maintaining the frequency and voltage of the AC side. This paper focuses on ensuring large-signal stability of an electrical grid with inverter-interfaced renewable sources. We prove that the Hybrid-Angle Control (HAC) scheme for grid-forming inverters can exhibit incremental passivity properties between current and voltage at both the AC and DC ports. This incremental passivity can be certified through decentralized conditions. Inverters operating under HAC can, therefore, be connected to other passive elements (e.g. transmission lines) with an immediate guarantee of global transient stability regardless of the network topology or parameters. Passivity of Hybrid Angle Control is also preserved under small-signal (linearized) analyses, in contrast to conventional proportional droop laws that are passivity-short at low frequencies. Passivity and interconnected-stability properties are demonstrated through an example case study.

The transition to renewable energy is driving the rise of distributed multi-energy systems, in which individual energy hubs and prosumers (e.g., homes, industrial campuses) generate, store, and trade energy. Economic Model Predictive Control (MPC) schemes are widely used to optimize operation of energy hubs by efficiently dispatching resources and minimizing costs while ensuring operational constraints are met. Peer-to-peer (P2P) energy trading among hubs enhances network efficiency and reduces costs but also increases computational and privacy challenges, especially as the network scales. Additionally, current distributed control techniques require global recomputation whenever the network topology changes, limiting scalability. To address these challenges, we propose a clustering-based P2P trading framework that enables plug-and-play operation, allowing energy hubs to seamlessly join or leave without requiring network-wide controller updates. The impact is restricted to the hubs within the affected cluster. The energy trading problem is formulated as a bi-level bargaining game, where inter-cluster trading commitments are determined at the cluster level, while energy dispatch and cost-sharing among hubs within a cluster are refined at the hub level. Both levels are solved in a distributed manner using ADMM, ensuring computational feasibility and privacy preservation. Moreover, we develop plug-and-play procedures to handle dynamic topology changes at both the hub and cluster levels, minimizing disruptions across the network. Simulation results demonstrate that the proposed bi-level framework reduces operational costs, and enables scalable energy management under plug-and-play operation.

Ultraefficient, high-aspect-ratio wings offer a promising solution for reducing emissions in next-generation aircraft. However, these designs are sensitive to atmospheric disturbances and prone to instability. While active control strategies can mitigate structural loads and stabilize the system, their development is challenging due to the uncertain and time-varying nature of aeroelastic systems. This article addresses these challenges with a direct, adaptive, data-driven approach. The proposed data-enabled policy optimization algorithm leverages sample covariance to directly learn and adapt control strategies from a single batch of persistently exciting, closed-loop input-output data. A forgetting factor mechanism enhances adaptability to time-varying dynamics during operation. The algorithm is explicit and recursive, requiring only a single step of projected gradient descent per sample, improving computational efficiency and enabling real-time application. Numerical simulations demonstrate that the proposed algorithm effectively suppresses unstable flutter, alleviates structural loads, adapts to dynamic time variations, and minimizes control effort-all without requiring prior knowledge of system dynamics or disturbances.

Peer-to-peer(P2P) energy trading may increase efficiency and reduce costs, but introduces significant challenges for network operators such as maintaining grid reliability, accounting for network losses, and redistributing costs equitably. We propose a novel loss-aware pricing strategy for P2P energy markets that addresses these challenges while incentivizing participation in the cooperative energy trading market. The problem is formulated as a hierarchical Stackelberg game, where a grid operator determines network tariffs while prosumers optimize their trades based on these tariffs while guaranteeing that network constraints are satisfied. The algorithm is designed to minimize and recover their cost from the trading parties, while also minimizing the total cost of the hubs. The mechanism dynamically adjusts tariffs based on location and network topology, discouraging loss-intensive trades. Finally, the complete framework includes the computation of fair trading prices, ensuring all market participants benefit equitably. An ADMM-based hyper-gradient descent method is proposed for solving this problem. Extensive numerical simulations using the benchmark IEEE 33-bus system demonstrate significant cost reductions and improved network efficiency through reduction in network losses compared to constant tariff schemes. Results highlight the adaptability and scalability of the proposed mechanism to varying network configurations and size, demand profiles, and seasonal conditions.

In this paper, a distributed dual-quaternion multiplicative extended Kalman filter for the estimation of poses and velocities of individual satellites in a fleet of spacecraft is analyzed. The proposed algorithm uses both absolute and relative pose measurements between neighboring satellites in a network, allowing each individual satellite to estimate its own pose and that of its neighbors. By utilizing the distributed Kalman consensus filter, a novel sensor and state-estimate fusion procedure is proposed that allows each satellite to improve its own state estimate by sharing data with its neighbors over a communication link. A leader–follower approach, whereby only a subset of the satellites has access to an absolute pose measurement, is also examined. In this case, followers rely solely on the information provided by their neighbors, as well as relative pose measurements to those neighbors. The algorithm is tested extensively via numerical simulations, and it is shown that the approach provides a substantial improvement in performance over the scenario in which the satellites do not cooperate. A case study of satellites swarming an asteroid is presented, and the performance in the leader–follower scenario is also analyzed.

A novel perspective on the design of robust model predictive control (MPC) methods is presented, whereby closed-loop constraint satisfaction is ensured using recursive feasibility of the MPC optimization. Necessary and sufficient conditions are derived for recursive feasibility, based on the effects of model perturbations and disturbances occurring at one time step. Using these conditions and Farkas' lemma, sufficient conditions suitable for design are formulated. The proposed method is called a closed-loop design, as only the existence of feasible inputs at the next time step is enforced by design. This is in contrast to most existing formulations, which compute control policies that are feasible under the worst-case realizations of all model perturbations and exogenous disturbances in the MPC prediction horizon. The proposed method has an online computational complexity similar to nominal MPC methods while preserving guarantees of constraint satisfaction, recursive feasibility and stability. Numerical simulations demonstrate the efficacy of our proposed approach.

In this paper, we present an impulse response identification scheme that incorporates the internal positivity side-information of the system. The realization theory of positive systems establishes specific criteria for the existence of a positive realization for a given transfer function. These transfer function criteria are translated to a set of suitable conditions on the shape and structure of the impulse responses of positive systems. Utilizing these conditions, the impulse response estimation problem is formulated as a constrained optimization in a reproducing kernel Hilbert space equipped with a stable kernel, and suitable constraints are imposed to encode the internal positivity side-information. The optimization problem is infinitedimensional with an infinite number of constraints. An equivalent finite-dimensional convex optimization in the form of a convex quadratic program is derived. The resulting equivalent reformulation makes the proposed approach suitable for numerical simulation and practical implementation. A Monte Carlo numerical experiment evaluates the impact of incorporating the internal positivity side-information in the proposed identification scheme. The effectiveness of the proposed method is demonstrated using data from a heating system experiment.

This paper proposes a unifying design framework for dynamic feedback controllers that track solution trajectories of time-varying generalized equations, such as local minimizers of nonlinear programs or competitive equilibria (e.g., Nash) of non-cooperative games. Inspired by the feedback optimization paradigm, the core idea of the proposed approach is to re-purpose classic iterative algorithms for solving generalized equations (e.g., Josephy--Newton, forward-backward splitting) as dynamic feedback controllers by integrating online measurements of the continuous-time nonlinear plant. Sufficient conditions for closed-loop stability and robustness of the algorithm-plant cyber-physical interconnection are derived in a sampled-data setting by combining and tailoring results from (monotone) operator, fixed-point, and nonlinear systems theory. Numerical simulations on smart building automation and competitive supply-chain management are presented to support the theoretical findings.

We propose a computationally tractable method for the identification of stable canonical discrete-time rational transfer function models, using frequency domain data. The problem is formulated as a global non-convex optimization problem whose objective function is the sum of weighted squared residuals at each observed frequency datapoint. Stability is enforced using a polynomial matrix inequality constraint. The problem is solved globally by a moment-sum-of-squares hierarchy of semidefinite programs through a framework for sum-of-rational-functions optimization. Convergence of the moment-sum-of-squares program is guaranteed as the bound on the degree of the sum-of-squares polynomials approaches infinity. The performance of the proposed method is demonstrated using numerical simulation examples.

Sustainability and environmental requirements have driven the shift in electric grids towards renewable and distributed generation, characterized by inverter-based resources (IBRs) including grid-forming inverters (GFMIs). These inverters are critical components of the energy infrastructure, and their cyber-security is crucial with the growing standardization of grid support services. Analyzing the vulnerability of the converter to cyber-attacks is the first step towards developing strategies for a cyber-secure system. This paper presents a novel filter scheme for generating the smallest stealthy damaging false data injection (FDI) undetectable by model-based intrusion detection methods. Simulation results verify the undetectability and effectiveness of the designed FDI method in destabilizing currents and voltages at the Point of Common Coupling.

Data-driven predictive control methods based on the Willems' fundamental lemma have shown great success in recent years. These approaches use receding horizon predictive control with nonparametric data-driven predictors instead of model-based predictors. This study addresses three problems of applying such algorithms under unbounded stochastic uncertainties: 1) tuning-free regularizer design, 2) initial condition estimation, and 3) reliable constraint satisfaction, by using stochastic prediction error quantification. The regularizer is designed by leveraging the expected output cost. An initial condition estimator is proposed by filtering the measurements with the one-step-ahead stochastic data-driven prediction. A novel constraint-tightening method, using second-order cone constraints, is presented to ensure high-probability chance constraint satisfaction. Numerical results demonstrate that the proposed methods lead to satisfactory control performance in terms of both control cost and constraint satisfaction, with significantly improved initial condition estimation.

We study non-parametric frequency-domain system identification from a finite-sample perspective. We assume an open loop scenario where the excitation input is periodic and consider the Empirical Transfer Function Estimate (ETFE), where the goal is to estimate the frequency response at certain desired (evenly-spaced) frequencies, given input-output samples. We show that under sub-Gaussian colored noise (in time-domain) and stability assumptions, the ETFE estimates are concentrated around the true values. The error rate is of the order of $\mathcal{O}((d_{\mathrm{u}}+\sqrt{d_{\mathrm{u}}d_{\mathrm{y}}})\sqrt{M/N_{\mathrm{tot}}})$, where $N_{\mathrm{tot}}$ is the total number of samples, $M$ is the number of desired frequencies, and $d_{\mathrm{u}},\,d_{\mathrm{y}}$ are the dimensions of the input and output signals respectively. This rate remains valid for general irrational transfer functions and does not require a finite order state-space representation. By tuning $M$, we obtain a $N_{\mathrm{tot}}^{-1/3}$ finite-sample rate for learning the frequency response over all frequencies in the $ \mathcal{H}_{\infty}$ norm. Our result draws upon an extension of the Hanson-Wright inequality to semi-infinite matrices. We study the finite-sample behavior of ETFE in simulations.

This paper develops a method to upper-bound extreme-values of time-windowed risks for stochastic processes. Examples of such risks include the maximum average or 90% quantile of the current along a transmission line in any 5-minute window. This work casts the time-windowed risk analysis problem as an infinite-dimensional linear program in occupation measures. In particular, we employ the coherent risk measures of the mean and the expected shortfall (conditional value at risk) to define the maximal time-windowed risk along trajectories. The infinite-dimensional linear program must then be truncated into finite-dimensional optimization problems, such as by using the moment-sum of squares hierarchy of semidefinite programs. The infinite-dimensional linear program will have the same optimal value as the original nonconvex risk estimation task under compactness and regularity assumptions, and the sequence of semidefinite programs will converge to the true value under additional properties of algebraic characterization. The scheme is demonstrated for risk analysis of example stochastic processes.

We revisit the problem of non-parametric closedloop identification in frequency domain; we give a brief survey of the literature and provide a small noise analysis of the direct, indirect, and joint input-output methods when two independent experiments with identical excitation are used. The analysis is asymptotic in the noise variance (i.e., as the standard deviation of the innovations sigma -> 0), for a finite data record of length N. We highlight the relationship between the estimators accuracy and the loop shape via asymptotic variance expressions given in terms of the sensitivity function. The results are illustrated using a numerical simulation example.

Data-driven methods for predictive control rely on input-output data to give a Hankel matrix representation of the space of trajectories. They are poorly suited to situations where both process noise and measurement noise dominate the behaviour whereas Kalman filters optimally estimate system states in this scenario. We derive a data-driven Kalman filter formulation based on the dynamic evolution of Hankel matrix output predictions. This leads to an extended state space model that describes the evolution of both the future inputs and outputs. By applying measurement feedback one arrives at a Kalman filter for the system. The Kalman filter design is performed purely on the basis of the input and output signals and without the need for a specific state-space representation. A benchmark simulation illustrates that the resulting prediction-based control significantly out-performs predictive controllers based on current data-driven methods.

Data-driven control uses a past signal trajectory to characterise the input-output behaviour of a system. Willems' lemma provides a data-based prediction model allowing a control designer to bypass the step of identifying a state-space or transfer function model. This paper provides a more parsimonious formulation of Willems' lemma that separates the model into initial condition matching and predictive control design parts. This avoids the need for regularisers in the predictive control problem that are found in other data-driven predictive control methods. It also gives a closed form expression for the optimal (minimum variance) unbiased predictor of the future output trajectory and applies it for predictive control. Simulation comparisons illustrate very good control performance.

Demand side management is perceived as a tool to support a secure and reliable energy system operation amid growing integration of renewable energy resources. However, the lack of scalable modeling and control procedures hinders the practical implementation. To address this challenge, this paper proposes a novel signal matrix model predictive control algorithm. Compared to existing data-driven methods, this approach explicitly provides stochastic predictions considering both disturbance and measurement errors with few tuning parameters, ensuring reliability by high-probability constraint satisfaction. The performance is extensively compared with three state-of-the-art algorithms in a space heating case study using a high-fidelity simulator. The results are further validated with physical experiments using the same system that the simulator is based on. To assess transferability, the algorithm is further implemented on diverse controlled systems, including a domestic hot water heating system and a stationary electric battery. The simulation results show that, compared to existing data-driven methods, the proposed approach improves constraint satisfaction and energy savings by up to 90 % and 8 %, respectively. The experimental results further confirm that the algorithm is applicable to multiple tasks of demand side management, with reasonable control performance observed in all case studies.

This paper presents an algorithmic approach towards bounding the peak time-windowed average value attained by a state function along trajectories of a dynamical system. An example includes the maximum average current flowing across a power line in any 5 -minute window. The peak time-windowed mean estimation task may be posed as a finite-dimensional but nonconvex optimization problem in terms of an initial condition and stopping time. This problem can be lifted into an infinite-dimensional linear program in occupation measures, where no conservatism is introduced under compactness and dynamical regularity assumptions. The peak time-windowed mean estimation linear program is in turn truncated into a convergent sequence of semidefinite programs using the moment-Sum-of-Squares hierarchy. Bounds of the time-windowed mean are computed for example systems.

It is well known that ignoring the presence of stochastic disturbances in the identification of stochastic Wiener models leads to asymptotically biased estimators. On the other hand, optimal statistical identification, via likelihood-based methods, is sensitive to the assumptions on the data distribution and is usually based on relatively complex sequential Monte Carlo algorithms. We develop a simple recursive online estimation algorithm based on an output-error predictor, for the identification of continuous-time stochastic parametric Wiener models through stochastic approximation. The method is applicable to generic model parameterizations and, as demonstrated in the numerical simulation examples, it is robust with respect to the assumptions on the spectrum of the disturbance process.