Automatica

Journal: Automatica

Publisher

Elsevier

ISSN

0005-1098

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Publications 1 - 10 of 109

Efficient spatio-temporal Gaussian regression via Kalman filtering
Todescato, Marco; Carron, Andrea; Carli, Ruggero; et al. (2020)
Automatica
Identification of stochastic nonlinear models using optimal estimating functions
Abdalmoaty, Mohamed; Hjalmarsson, Håkan (2020)
Automatica
The first part of the paper examines the asymptotic properties of linear prediction error method estimators, which were recently suggested for the identification of nonlinear stochastic dynamical models. It is shown that their accuracy depends not only on how the model is parameterized, but also on the shape of the unknown distribution of the data. Therefore, it is not obvious in general which linear prediction error method should be preferred. In the second part, the estimating functions approach is introduced and used to construct estimators that are asymptotically optimal with respect to a specific class of estimators. These estimators rely on partial probabilistic parametric models, and therefore neither require the computations of the likelihood function nor any marginalization integrals. The convergence and consistency of the proposed estimators are established under standard regularity and identifiability assumptions akin to those of prediction error methods. The paper is concluded by several numerical simulation examples.
Multi-robots Gaussian estimation and coverage control: From client–server to peer-to-peer architectures
Todescato, Marco; Carron, Andrea; Carli, Ruggero; et al. (2017)
Automatica
Signal temporal logic control synthesis among uncontrollable dynamic agents with conformal prediction
Yu , Xinyi; Zhao , Yiqi; Yin , Xiang; et al. (2026)
Automatica
The control of dynamical systems under temporal logic specifications among uncontrollable dynamic agents is challenging due to the agents’ a-priori unknown behavior. Existing works have considered the problem where either all agents are controllable, the agent models are deterministic and known, or no safety guarantees are provided. We propose a predictive control synthesis framework that guarantees, with high probability, the satisfaction of signal temporal logic (STL) tasks that are defined over a controllable system in the presence of uncontrollable stochastic agents. We use trajectory predictors and conformal prediction to construct probabilistic prediction regions for each uncontrollable agent that are valid over multiple future time steps. Specifically, we construct a normalized prediction region over all agents and time steps to reduce conservatism and increase data efficiency. We then formulate a worst-case bilevel mixed integer program (MIP) that accounts for all agent realizations within the prediction region to obtain an open-loop controller that provably guarantee task satisfaction with high probability. To efficiently solve this bilevel MIP, we propose an equivalent MIP program based on KKT conditions of the original bilevel formulation. Building upon this, we design a closed-loop controller, where both recursive feasibility and task satisfaction can be guaranteed with high probability. We illustrate our control synthesis framework on two case studies.
Linear quadratic control with risk constraints
Tsiamis, Anastasios; Kalogerias, Dionysios; Ribeiro, Alejandro; et al. (2025)
Automatica
We propose a new risk-constrained formulation of the Linear Quadratic (LQ) stochastic control problem for general partially-observed systems. Classical risk-neutral LQ controllers, although optimal in expectation, might be ineffective under infrequent, yet statistically significant extreme events. To effectively trade between average and extreme event performance, we introduce a new risk constraint, which restricts the cumulative expected predictive variance of the state penalty by a user-prescribed level. We show that, under certain conditions on the process noise, the optimal risk-aware controller can be evaluated explicitly and in closed form. In fact, it is affine relative to the minimum mean square error (mmse) state estimate. The affine term pushes the state away from directions where the noise exhibits heavy tails, by exploiting the third-order moment (skewness) of the noise. The linear term regulates the state more strictly in risky directions, where both the prediction error (conditional) covariance and the state penalty are simultaneously large; this is achieved by inflating the state penalty within a new filtered Riccati difference equation. We also prove that the new risk-aware controller is internally stable, regardless of parameter tuning, in the special cases of (i) fully-observed systems, and (ii) partially-observed systems with Gaussian noise. The properties of the proposed risk-aware LQ framework are lastly illustrated via indicative numerical examples.
Region of attraction analysis with Integral Quadratic Constraints
Iannelli, Andrea; Seiler, Peter; Marcos, Andrés (2019)
Automatica
A general framework is presented to estimate the Region of Attraction of attracting equilibrium points. The system is described by a feedback connection of a nonlinear (polynomial) system and a bounded operator. The input/output behavior of the operator is characterized using an Integral Quadratic Constraint. This allows to analyze generic problems including, for example, hard-nonlinearities and different classes of uncertainties, adding to the state of practice in the field which is typically limited to polynomial vector fields. The IQC description is also nonrestrictive, with the main result given for both hard and soft factorizations. Optimization algorithms based on Sum of Squares techniques are then proposed, with the aim to enlarge the inner estimates of the ROA. Numerical examples are provided to show the applicability of the approaches. These include a saturated plant where bounds on the states are exploited to refine the sector description, and a case study with parametric uncertainties for which the conservativeness of the results is reduced by using soft IQCs.
Fast generalized Nash equilibrium seeking under partial-decision information
Bianchi, Mattia; Belgioioso, Giuseppe; Grammatico, Sergio (2022)
Automatica
We address the generalized Nash equilibrium seeking problem in a partial-decision information scenario, where each agent can only exchange information with some neighbors, although its cost function possibly depends on the strategies of all agents. The few existing methods build on projected pseudo-gradient dynamics, and require either double-layer iterations or conservative conditions on the step sizes. To overcome both these flaws and improve efficiency, we design the first fully-distributed single-layer algorithms based on proximal best-response. Our schemes are fixed-step and allow for inexact updates, which is crucial for reducing the computational complexity. Under standard assumptions on the game primitives, we establish convergence to a variational equilibrium (with linear rate for games without coupling constraints) by recasting our algorithms as proximal-point methods, opportunely preconditioned to distribute the computation among the agents. Since our analysis hinges on a restricted monotonicity property, we also provide new general results that significantly extend the domain of applicability of proximal-point methods. Besides, our operator-theoretic approach favors the implementation of provably correct acceleration schemes that can further improve the convergence speed. Finally, the potential of our algorithms is demonstrated numerically, revealing much faster convergence with respect to projected pseudo-gradient methods and validating our theoretical findings.
Stability analysis of quasi-polynomial dynamical systems with applications to biological network models
Motee, Nader; Bamieh, Bassam; Khammash, Mustafa Hani (2012)
Automatica
Set-based value operators for non-stationary and uncertain Markov decision processes
Li, Sarah H.Q.; Adjé, Assalé; Garoche, Pierre-Loïc; et al. (2025)
Automatica
This paper analyzes finite-state Markov Decision Processes (MDPs) with nonstationary and uncertain parameters via set-based fixed point theory. Given compact parameter ambiguity sets, we demonstrate that a family of contraction operators, including the Bellman operator and the policy evaluation operator, can be extended to set-based contraction operators with a unique fixed point—a compact value function set. For non-stationary MDPs, we show that while the value function trajectory diverges, its Hausdorff distance from this fixed point converges to zero. In parameter uncertain MDPs, the fixed point's extremum value functions are equivalent to the min–max value function in robust dynamic programming under the rectangularity condition. Furthermore, we show that the rectangularity condition is a sufficient condition for the fixed point to contain its own extremum value functions. Finally, we derive novel guarantees for probabilistic path planning in capricious wind fields and stratospheric station-keeping.
Nonlinear offset-free model predictive control
Morari, M.; Maeder, U. (2012)
Automatica

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Journal: Automatica

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