Article
Automation & Control Systems
Pian Yu, Dimos Dimarogonas
Summary: Discrete abstractions are a standard approach for control synthesis, and this work introduces a control interface to relax the requirement of time-space discretization for continuous-time systems, proposing new stability and simulation concepts.
Article
Computer Science, Artificial Intelligence
Manfred Eppe, Christian Gumbsch, Matthias Kerzel, Phuong D. H. Nguyen, Martin Butz, Stefan Wermter
Summary: This article provides an overview of the cognitive foundations of hierarchical problem-solving and proposes steps to integrate biologically inspired hierarchical mechanisms into artificial agents. The authors highlight the promising approach of hierarchical reinforcement learning for developing problem-solving behavior in artificial agents and robots. However, the problem-solving abilities of many human and non-human animals still surpass those of artificial systems. The authors suggest integrating biologically inspired hierarchical mechanisms to improve the problem-solving skills of artificial agents.
NATURE MACHINE INTELLIGENCE
(2022)
Article
Biochemistry & Molecular Biology
Stephan Fritzsche, Andrey Surzhykov
Summary: This paper demonstrates how relativistic (many-electron) Green functions can be approximated and systematically improved for few- and many-electron atoms and ions by utilizing classes of virtual excitations and a multi-configuration Dirac-Hartree-Fock expansion. The implementation of these approximate Green functions in the Jena Atomic Calculator will facilitate the study of various multi-photon and multiple electron processes.
Article
Mathematics
Safoura Rezaei Aderyani, Reza Saadati, Donal O'Regan, Fehaid Salem Alshammari
Summary: We use Mittag-Leffler-type functions to introduce a class of matrix-valued fuzzy controllers, which enable us to propose the concept of multi-stability and obtain fuzzy approximate solutions of matrix-valued fractional differential equations in fuzzy spaces. The concept of multi-stability allows us to obtain different approximations depending on the initially chosen special functions. Additionally, we investigate the Ulam-Hyers stability of the models by utilizing various properties of a function of Mittag-Leffler type.
Article
Engineering, Industrial
T. A. Arno Kasper, Martin J. Land, Ruud H. Teunter
Summary: Reducing Work-In-Process (WIP) in manufacturing systems has advantages such as predictable throughput times and increased manageability. Various WIP control methods, including CONWIP and Kanban for repetitive manufacturing, and LUMS COR and POLCA for high-variety manufacturing, have been developed. By simultaneously considering release, authorization, and dispatching decisions, the non-hierarchical method DRACO outperforms traditional methods and improves overall manageability.
INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS
(2023)
Article
Automation & Control Systems
Adnane Saoud, Pushpak Jagtap, Majid Zamani, Antoine Girard
Summary: This article focuses on reducing computational complexity in abstraction-based controller synthesis for interconnected control systems. It provides a compositional framework for constructing abstractions for interconnected systems and a bottom-up controller synthesis scheme. By introducing the notion of approximate composition, it is possible to compute an abstraction of the global interconnected system from abstractions of its components, enabling a bottom-up approach for synthesizing controllers enforcing decomposable safety specifications.
IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS
(2021)
Article
Automation & Control Systems
Haizi Yu, Igor Mineyev, Lav R. Varshney
Summary: This paper presents a mathematical formulation for computationally emulating human-like abstractions, called computational abstraction, and explores the hierarchical development of abstraction processes from innate priors such as symmetries. The nature of abstraction is studied using a group-theoretic approach, formalizing and practically computing abstractions as symmetry-driven hierarchical clustering. It also generalizes existing works and formalizes knowledge discovery applications.
JOURNAL OF MACHINE LEARNING RESEARCH
(2023)
Article
Automation & Control Systems
Juliana Vilela, Richard Hill
Summary: This paper demonstrates how to improve the abstraction of hierarchy by relaxing the sequential dependence definition and using the concepts of cost equivalence and weak bisimulation to achieve compositional generation of supervisor abstractions, avoiding state space explosion. Through the compositional approach, models with zero-cost transitions can be created, enhancing the effectiveness of abstraction.
DISCRETE EVENT DYNAMIC SYSTEMS-THEORY AND APPLICATIONS
(2022)
Article
Quantum Science & Technology
Kilian Ender, Anette Messinger, Michael Fellner, Clemens Dlaska, Wolfgang Lechner
Summary: The parity transformation encodes spin models in the low-energy subspace of a larger Hilbert space with constraints on a planar lattice. By applying quantum approximate optimization algorithm (QAOA), constraints can be enforced explicitly or implicitly, with the implicit approach showing better QAOA performance. Furthermore, a modular parallelization method is introduced to partition the circuit into clusters of subcircuits with fixed maximal circuit depth, relevant for scaling up to large system sizes.
Article
Psychology, Experimental
Rie Asano, Cedric Boeckx, Uwe Seifert
Summary: This paper explores the relationship between language and music as neurocognitive systems, focusing on hierarchical control as a mechanism. The Coordinated Hierarchical Control (CHC) hypothesis suggests that linguistic and musical syntax rely on hierarchical control, but engage this shared mechanism differently. Evidence from neuroimaging and neuropsychological studies supports this hypothesis and provides novel testable predictions for future research on the language-music relationship.
Article
Computer Science, Artificial Intelligence
Dechao Li, Qingxue Zeng
Summary: In this paper, three approximate reasoning methods with aggregation functions are developed to strengthen the effectiveness of approximate reasoning in fuzzy modus ponens (FMP) and fuzzy modus tollens (FMT) problems. The validity of these methods is investigated, and their effectiveness is analyzed using GMP rules.
ARTIFICIAL INTELLIGENCE REVIEW
(2022)
Article
Engineering, Multidisciplinary
Pang Xiaobing, Xingfa Yang, Mohammad Hadi Noori Skandari, Emran Tohidi, Stanford Shateyi
Summary: In this research work, an applicable Legendre spectral collocation method, based on a space of fractional basis functions, is proposed for numerically computing the solution of fractional optimal control problems. The method approximates the unknown control and state using fractional Lagrange interpolation, and employs shifted Legendre-Gauss collocation points and exact fractional differentiation matrix for discretization.
ALEXANDRIA ENGINEERING JOURNAL
(2022)
Article
Physics, Multidisciplinary
Petar Mitric, Veljko Jankovic, Nenad Vukmirovic, Darko Tanaskovic
Summary: The dynamical mean field theory is an excellent, numerically cheap, approximate solution for the spectral function of the Holstein model even in one dimension, as revealed by detailed comparisons with other methods and literature results.
PHYSICAL REVIEW LETTERS
(2022)
Article
Computer Science, Information Systems
Denis Vasko, Jan Kardos, Eva Miklovicova
Summary: This paper considers second order nonlinear systems in control canonical form, where the functional relation between the control variable and the system acceleration is nonlinear, uncertain and discontinuous. The nonlinear, uncertain and discontinuous function is approximated with an invertible one, and variable structure control theory is used to suppress the error of the approximate inverse. The finite time reaching law or the terminal reaching law is rewritten into inequalities, and these inequalities are used to design a control law that drives the system state to the sliding surface in finite time, and ensures the convergence of the system speed and position errors to a narrow, adjustable width band around zero. The stability of the control system is also analyzed and proven. The sufficiency of the available control effort is demonstrated through proper design of control parameters. The method is then validated on a numerical model of a nonlinear system with nonlinear, uncertain and discontinuous input function.
Article
Energy & Fuels
Zhaodi Shi, Weisheng Wang, Yuehui Huang, Pai Li, Ling Dong
Summary: This study proposes a hierarchical optimization algorithm to optimize the capacity of renewable energy generation and energy storage systems simultaneously. By adopting time sequence simulation technology and optimization models, the regional resource characteristics are fully considered, providing guidance for the development of power generation and energy storage capacity in high renewable energy penetration systems.
CSEE JOURNAL OF POWER AND ENERGY SYSTEMS
(2022)
Article
Automation & Control Systems
Mahyar Fazlyab, Manfred Morari, George J. Pappas
Summary: Certifying the safety and robustness of neural networks against input uncertainties and adversarial attacks is a new challenge. This article proposes a semidefinite programming framework to bound the output of neural networks when their inputs change, and evaluates its performance through numerical problem instances.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2022)
Article
Automation & Control Systems
Vasileios Tzoumas, Ali Jadbabaie, George J. Pappas
Summary: In this article, the authors propose a robust and adaptive maximization algorithm for solving discrete optimization problems in adversarial environments. The algorithm, called RAM, runs in an online fashion and adapts to the history of failures in each step. It guarantees near-optimal performance and has both provable per-instance a priori bounds and tight and/or optimal a posteriori bounds.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2022)
Article
Robotics
Yiannis Kantaros, Samarth Kalluraya, Qi Jin, George J. Pappas
Summary: This article addresses a multi-robot planning problem in environments with partially unknown semantics. It introduces a perception-based LTL planning algorithm that generates open-loop control policies updated online to adapt to a continuously learned semantic map, enabling robots to accomplish collaborative tasks.
IEEE TRANSACTIONS ON ROBOTICS
(2022)
Article
Robotics
Brent Schlotfeldt, Vasileios Tzoumas, George J. Pappas
Summary: This article introduces a robust and adaptive multirobot planning algorithm RAIN, which can plan information acquisition tasks in adversarial environments and exhibits superior performance in multiple information acquisition scenarios.
IEEE TRANSACTIONS ON ROBOTICS
(2022)
Article
Automation & Control Systems
Andreea B. Alexandru, George J. Pappas
Summary: This article addresses the problem of private weighted sum aggregation and proposes different secure multiparty computation schemes to achieve the private aggregation of weights.
IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS
(2022)
Article
Automation & Control Systems
Lars Lindemann, George J. Pappas, Dimos Dimarogonas
Summary: In this paper, we propose reactive risk signal interval temporal logic (ReRiSITL) for formulating complex spatiotemporal specifications and provide an algorithm to check its satisfiability. We also propose a reactive planning and control framework for dynamical control systems under ReRiSITL specifications.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2022)
Article
Computer Science, Information Systems
Yukun Yuan, Desheng Zhang, Fei Miao, John A. A. Stankovic, Tian He, George J. J. Pappas, Shan Lin
Summary: With the rapid development of cities, heterogeneous urban cyber-physical systems are designed to improve citizens' experience, but the integration of services is not designed for disruptive events, resulting in a ripple effect on service quality. To address this issue, we present a service called eRoute that automatically selects and integrates parts of subway, bus, and taxi systems using their mobility patterns. Evaluation results show that eRoute significantly improves the served passengers per time interval and reduces the average traveling time compared to existing solutions.
IEEE TRANSACTIONS ON MOBILE COMPUTING
(2023)
Article
Automation & Control Systems
Alena Rodionova, Lars Lindemann, Manfred Morari, George J. Pappas
Summary: This paper proposes a combined notion of temporal robustness that considers both left and right time shifts. Control laws for linear systems are designed using mixed-integer linear programming to maximize temporal robustness. Two case studies illustrate the effectiveness of the proposed temporal robustness in accounting for timing uncertainties.
IEEE CONTROL SYSTEMS LETTERS
(2023)
Article
Automation & Control Systems
C. de Souza, S. Tarbouriech, A. Girard
Summary: This letter addresses the problem of event-triggered control of discrete-time linear time-invariant systems stabilized by neural network controllers. The event-triggering mechanism is proposed to update only a portion of the layers required to maintain stability and satisfactory performance of the feedback system, reducing the computational cost associated with the evaluation of the neural network. Sufficient convex conditions in the form of linear matrix inequalities are provided to compute the triggering parameters and characterize an estimate of the domain of attraction for the feedback system. Optimization procedures are also formulated to effectively reduce the amount of computation in the neural network. An example borrowed from the literature is used to illustrate the effectiveness of the proposal.
IEEE CONTROL SYSTEMS LETTERS
(2023)
Article
Automation & Control Systems
Anastasios Tsiamis, George J. Pappas
Summary: In this article, the problem of predicting observations generated by an unknown, partially observable linear system driven by Gaussian noise is considered. The optimal predictor is the Kalman filter, but when the system model is unknown, learning to predict online based on finite data is necessary, leading to a nonzero regret compared to the Kalman filter's prediction. An online least-squares algorithm is proposed to achieve a regret of the order of $\text{poly}\log (N)$ with high probability, utilizing the linear relation between future and past observations. The analysis is based on stability properties, statistical tools, and classical results, and is applicable to other predictors and different scenarios.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2023)
Article
Robotics
Xiaoyi Cai, Brent Schlotfeldt, Kasra Khosoussi, Nikolay Atanasov, George J. Pappas, Jonathan P. How
Summary: This article addresses the problem of coordinating sensor-equipped robots in a safe manner to reduce uncertainty in a dynamical process, considering the tradeoff between information gain and energy cost. Existing multirobot planners based on coordinate descent fail to provide performance guarantees due to the nonmonotone objective function in robot trajectories. Methods that handle nonmonotonicity also lose their performance guarantees when collision avoidance constraints are imposed. To achieve both performance and safety guarantees, this work proposes a hierarchical approach that combines a distributed planner with worst-case performance guarantees and a decentralized controller based on control barrier functions. Extensive simulations, hardware-in-the-loop tests, and hardware experiments demonstrate that the proposed approach achieves a better tradeoff between sensing and energy cost compared to coordinate-descent-based algorithms.
IEEE TRANSACTIONS ON ROBOTICS
(2023)
Article
Robotics
Lars Lindemann, Matthew Cleaveland, Gihyun Shim, George J. Pappas
Summary: We propose a framework that uses conformal prediction to plan in unknown dynamic environments with probabilistic safety guarantees. Our model predictive controller (MPC) utilizes trajectory predictions and prediction regions to quantify uncertainty. By using conformal prediction, valid prediction regions can be obtained, ensuring provable safety for the MPC. Our approach is compatible with state of the art trajectory predictors, making no assumptions on the underlying trajectory-generating distribution, and provides the first results with valid safety guarantees in such a setting.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2023)
Article
Automation & Control Systems
Nicolo Dal Fabbro, Aritra Mitra, George J. Pappas
Summary: In this study, we propose a quantized federated temporal difference learning algorithm, QFedTD, for a federated policy evaluation problem with finite capacity up-link channels. We provide a finite-sample analysis of QFedTD, highlighting the effects of quantization and erasures on the convergence rate and establishing a linear speedup with respect to the number of agents under Markovian sampling. This is the first work to provide a non-asymptotic analysis of the effects of quantization and packet drop models in multi-agent and federated reinforcement learning.
IEEE CONTROL SYSTEMS LETTERS
(2023)
Article
Engineering, Biomedical
Erfan Nozari, Maxwell A. Bertolero, Jennifer Stiso, Lorenzo Caciagli, Eli J. Cornblath, Xiaosong He, Arun S. Mahadevan, George J. Pappas, Dani S. Bassett
Summary: The study challenges the assumption that large networks of neurons exhibit a large repertoire of nonlinear behaviors. By using mathematical models derived from measurements of brain activity, the researchers found that linear models provide the best fit for describing resting-state neural dynamics. They argue that this is due to microscopic nonlinear dynamics being counteracted or masked by macroscopic dynamics and technological limitations.
NATURE BIOMEDICAL ENGINEERING
(2023)
Article
Automation & Control Systems
Thomas Beckers, Leonardo J. Colombo, Sandra Hirche, George J. Pappas
Summary: Trajectory tracking control of underactuated vehicles is crucial for a wide range of applications, but the unknown external disturbances and internal dynamics pose challenges. To tackle this issue, this study proposes a control law for underactuated rigid-body dynamics using an online learning-based predictor, with Gaussian process models as the predictor. The approach guarantees a bounded tracking error with a given bound, as demonstrated with a numerical example.
IEEE CONTROL SYSTEMS LETTERS
(2022)
Article
Automation & Control Systems
Xiaoyu Luo, Chengcheng Zhao, Chongrong Fang, Jianping He
Summary: This paper investigates the problem of false data injection attacks in multi-agent dynamical systems and proposes FDI attack set selection algorithms to maximize the convergence error by finding the optimal subset of compromised agents.
Article
Automation & Control Systems
Nitin K. Singh, Abhisek K. Behera
Summary: In this paper, a twisting observer is proposed for robustly estimating the states of a second-order uncertain system. The observer approximates the unknown sign term for the non-measurable state with a delayed output-based switching function, and achieves the desired steady-state accuracy by controlling the delay parameter. The application of the observer to output feedback stabilization is also discussed.
Article
Automation & Control Systems
Alexander Aleksandrov
Summary: This paper investigates the absolute stability problem for positive Persidskii systems with delay, proposes a special construction method for diagonal Lyapunov-Krasovskii functionals, and derives a criterion for the existence of such functionals guaranteeing the absolute stability, as well as obtaining sufficient conditions for a family of time-delay Persidskii systems to construct a common diagonal Lyapunov-Krasovskii functional. The efficiency of the developed approaches is demonstrated through four examples.
Article
Automation & Control Systems
Noureddine Toumi, Roland Malhame, Jerome Le Ny
Summary: This paper addresses large multi-agent dynamic discrete choice problems using a linear quadratic mean field games framework. The model incorporates the features where agents have to reach a predefined set of possible destinations within a fixed time frame and running costs can become negative to simulate crowd avoidance. An upper bound on the time horizon is derived to prevent agents from escaping to infinity in finite time. The existence of a Nash equilibrium for infinite population and its epsilon-Nash property for a large but finite population are established. Simulations are conducted to explore the model behavior in various scenarios.
Article
Automation & Control Systems
Philippe Schuchert, Vaibhav Gupta, Alireza Karimi
Summary: This paper presents the design of fixed-structure controllers for the As2 and Asw synthesis problem using frequency response data. The minimization of the norm of the transfer function between the exogenous inputs and performance outputs is approximated through a convex optimization problem involving Linear Matrix Inequalities (LMIs). A general controller parametrization is used for continuous and discrete-time controllers with matrix transfer function or state-space representation. Numerical results show that the proposed data-driven method achieves performance equivalent to model-based approaches when a parametric model is available.
Correction
Automation & Control Systems
Zhijun Guo, Gang Chen
Article
Automation & Control Systems
Matteo Della Rossa, Thiago Alves Lima, Marc Jungers, Raphael M. Jungers
Summary: This paper presents new stabilizability conditions for switched linear systems with arbitrary and uncontrollable underlying switching signals. The study focuses on two specific settings: the robust case with completely unknown and unobservable active mode, and the mode-dependent case with controller depending on the current active switching mode. The technical developments are based on graph-theory tools and path-complete Lyapunov functions framework, enabling the design of robust and mode-dependent piecewise linear state-feedback controllers using directed and labeled graphs.
Article
Automation & Control Systems
Elena Petri, Romain Postoyan, Daniele Astolfi, Dragan Nesic, W. P. M. H. (Maurice) Heemels
Summary: This study investigates a scenario where a perturbed nonlinear system transmits its output measurements to a remote observer via a packet-based communication network. By designing both the observer and the local transmission policies, accurate state estimates can be obtained while only sporadically using the communication network.
Article
Automation & Control Systems
Jonas Krook, Robi Malik, Sahar Mohajerani, Martin Fabian
Summary: This paper proposes a method to synthesise controllers for cyber-physical systems subjected to disturbances, such that the controlled system satisfies specifications given as linear temporal logic formulas. The approach constructs a finite-state abstraction of the original system and synthesises a controller for the abstraction. It introduces the robust stutter bisimulation relation to account for disturbances and uncertainty, ensuring that related states have similar effects under the same controller. The paper demonstrates that the existence of a controller for the abstracted system implies the existence of a controller for the original system enforcing the linear temporal logic formula.
Article
Automation & Control Systems
Clement Chahbazian, Karim Dahia, Nicolas Merlinge, Benedicte Winter-Bonnet, Aurelien Blanc, Christian Musso
Summary: The paper derives a recursive formula of the Fisher information matrix on Lie groups and applies it to nonlinear Gaussian systems on Lie groups for testing. The proposed recursive CRLB is consistent with state-of-the-art filters and exhibits representative behavior in estimation errors. This paper provides a simple method to recursively compute the minimal variance of an estimator on matrix Lie groups, which is fundamental for implementing robust algorithms.
Article
Automation & Control Systems
Yiheng Fu, Pouria Ramazi
Summary: This study investigates the characteristics of decision fluctuations in heterogeneous populations and explores the uncertainties in imitation behavior. The findings are important for understanding the bounded rationality nature of imitation behaviors.
Article
Automation & Control Systems
Lars A. L. Janssen, Bart Besselink, Rob H. B. Fey, Nathan van de Wouw
Summary: This paper introduces a mathematical relationship between the accuracy of reduced-order linear-time invariant subsystem models and the stability and accuracy of the resulting reduced-order interconnected linear time-invariant model. This result can be used to directly translate the accuracy characteristics of the reduced-order subsystem models to the accuracy properties of the interconnected reduced-order model, or to translate accuracy requirements on the interconnected system model to accuracy requirements on subsystem models.
Article
Automation & Control Systems
Piyush Gupta, Vaibhav Srivastava
Summary: We study the optimal fidelity selection for a human operator servicing tasks in a queue, considering the trade-off between high-quality service and penalty due to increased queue length. By modeling the operator's cognitive dynamics and task fidelity, we determine the optimal policy and value function numerically, and analyze the structural properties of the optimal fidelity policy.
Article
Automation & Control Systems
Lukas Schwenkel, Alexander Hadorn, Matthias A. Mueller, Frank Allgoewer
Summary: In this work, the authors study economic model predictive control (MPC) in periodic operating conditions. They propose a method to achieve optimality by multiplying the stage cost by a linear discount factor, which is easy to implement and robust against online changes. Under certain assumptions, they prove that the resulting linearly discounted economic MPC achieves optimal asymptotic average performance and guarantees practical asymptotic stability of the optimal periodic orbit.
Article
Automation & Control Systems
Taher Ebrahim, Sankaranarayanan Subramanian, Sebastian Engell
Summary: We propose a robust nonlinear model predictive control algorithm for dynamic systems with mixed degrees of freedom. This algorithm optimizes both continuous and discrete manipulated variables, enhancing closed-loop performance. Our approach relies on a computationally efficient relaxation and integrality restoration strategy and provides sufficient conditions to establish recursive feasibility and guarantee robust closed-loop stability. The effectiveness of the approach is demonstrated through two nonlinear simulation examples.
