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63rd IEEE Conference on Decision and control, CDC 2024

作者： Mazumdar, Abhijit Wisniewski, Rafal Bujorianu, Manuela L. Aalborg University Section of Automation & Control Aalborg East9220 Denmark University College London Department of Computer Science United Kingdom

ISBN: (纸本)9798350316339

In this paper, we present an online reinforcement learning algorithm for constrained Markov decision processes with a safety constraint. Despite the necessary attention of the scientific community, considering stochastic stopping time, the problem of learning optimal policy without violating safety constraints during the learning phase is yet to be addressed. To this end, we propose an algorithm based on linear programming that does not require a process model. We show that the learned policy is safe with high confidence. We also propose a method to compute a safe baseline policy, which is central in developing algorithms that do not violate the safety constraints. Finally, we provide simulation results to show the efficacy of the proposed algorithm. Further, we demonstrate that efficient exploration can be achieved by defining a subset of the state-space called proxy set. © 2024 IEEE.

关键词： Contrastive Learning

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Special Session: D-MATE: A Design Methodology for Connecting Automatic Test Equipment in Industry 4.0 26

Special Session: D-MATE: A Design Methodology for Connecting...

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26th IEEE Latin American Test Symposium, LATS 2025

作者： Biondani, Francesco Tosoni, Francesco Dall’Ora, Nicola Fraccaroli, Enrico Vinco, Sara Cheng, Dong Seon Fummi, Franco Department of Engineering for Innovation Medicine Section of Engineering and Physics University of Verona Italy Department of Engineering Sciences Guglielmo Marconi University Italy Department of Control and Computer Engineering Politecnico di Torino Italy

ISBN: (纸本)9781665477635

With the growing presence of semiconductor devices in healthcare, automotive, and consumer electronics, Automatic Test Equipment (ATE) systems play an increasingly vital role in ensuring quality and reliability during validation. Despite their importance, ATE systems often operate in isolation from other manufacturing processes, limiting interoperability and integration potential. Consequently, fully incorporating ATE systems within the Industry 4.0 framework remains a largely unaddressed challenge. To bridge this gap, we propose adopting Open Platform Communications Unified Architecture (OPC UA), the industry de-facto standard communication protocol for machines, with an accompanying specification tailored to ATE systems. We developed and validated our information model on an advanced ATE system, demonstrating its practical application. The results showcase the successful integration of the ATE system into a fully-fledged Industrial computer Engineering (ICE) laboratory demonstrator. This study validates the effectiveness of our model in a real-world scenario and highlights the significance of our integration approach within the context of Industry 4.0. © 2025 IEEE.

关键词： Automatic testing

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Design and Stability Analysis of control System in Multiport Autonomous Reconfigurable Solar Power Plants (MARS) 24

Design and Stability Analysis of Control System in Multiport...

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24th IEEE Workshop on control and Modeling for Power Electronics, COMPEL 2023

作者： Xia, Qianxue Debnath, Suman Marthi, Phani R.V. Saeedifard, Maryam Energy Systems Integration Control Section Oak Ridge National Lab Knoxville United States School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta United States

ISBN: (纸本)9798350316186

The Multiport Autonomous Reconfigurable Solar Power Plant (MARS) is an integrated photovoltaic (PV) power generation and energy storage system (ESS), that is designed to connect to both alternating current (AC) transmission grids and high-voltage direct current (HVDC) links. It is a three-phase plant consisting of numerous components with a complex hardware and hierarchical control architecture. This paper presents an approach to decouple the multivariable system of MARS using a recursive reduced-order and boundary layer system methodology. This approach enables efficient computation of the control parameters for the Ll, L2, and L3 controllers. To validate the effectiveness of the proposed control strategy, cyclic tests in accordance with pre-defined performance criteria using controller Hardware-in-the-Loop (cHIL) experiments are conducted. The results demonstrate that the MARS system operates consistently under steady-state conditions. Furthermore, the dynamic response of the MARS system to various grid events is analyzed, underlining the resilience of MARS in presence of faults or loss of generation within the connected WECC system. © 2023 IEEE.

关键词： Energy storage

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LargeDeviations in Safety-CriticalHamiltonian Systems withProbabilistic InitialConditions

arXiv

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arXiv 2024年

作者： Gomez, Aitor R. Bujorianu, Manuela L. Wisniewski, Rafal Section of Automation & Control Aalborg University Denmark Department of Computer Science University College London United Kingdom

We address the problem of determining the least improbable deviations leading to an unsafe rare event in a weakly perturbed mechanical system with probabilistic initial conditions. These deviations are obtained as the solution to a variational problem formulated using rigorous approximation techniques grounded in the principles of large deviations theory. These types of results have been extended to accommodate stochastic uncertainty in the initial states, which is a common assumption in mechanical systems. Furthermore, we demonstrate the applicability of the method by solving the problem for a rare collision event between two space objects-a high-dimensional and non-linear problem-resulting in the most likely sample paths leading to the realization of the unsafe rare event. The solution is validated against the necessary conditions for optimality derived from the maximum principle. Access to these unsafe sample paths offers relevant information regarding the dangerous configurations of rare events and can be used to design control strategies to reduce the probability of realization. © 2024, CC BY-NC-SA.

关键词： Stochastic systems

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Online Model-Free Safety Verification for Markov Decision Processes Without Safety Violation

Online Model-Free Safety Verification for Markov Decision Pr...

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European control Conference (ECC)

作者： Abhijit Mazumdar Rafal Wisniewski Manuela L. Bujorianu Section of Automation & Control Aalborg University Aalborg East Denmark Department of Computer Science University College London UK

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

In this paper, we consider the problem of safety assessment for Markov decision processes without explicit knowledge of the model. We aim to learn probabilistic safety specifications associated with a given policy without compromising the safety of the process. To accomplish our goal, we characterize a subset of the state-space namely proxy set, which contains the states that are near in a probabilistic sense to the forbidden set consisting of all unsafe states. We compute the safety function using the single-step temporal difference method. To thi s end, we relate the safety function computation to that of the value function estimation using temporal difference learning. Since the given control policy could be unsafe, we use a safe baseline sub-policy to generate data for learning. We then use an off-policy temporal difference learning method with importance sampling to learn the safety function corresponding to the given policy. Finally, we demonstrate our results using a numerical example.

关键词： Monte Carlo methods Markov decision processes Temporal difference learning Process control Europe Estimation Probabilistic logic

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Safe Reinforcement Learning for Constrained Markov Decision Processes with Stochastic Stopping Time

Safe Reinforcement Learning for Constrained Markov Decision ...

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IEEE Conference on Decision and control

作者： Abhijit Mazumdar Rafal Wisniewski Manuela L. Bujorianu Section of Automation & Control Aalborg University Aalborg East Denmark Department of Computer Science University College London UK

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

关键词： Markov decision processes Simulation Computational modeling Stochastic processes Process control Reinforcement learning Linear programming Safety

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Safe Reinforcement Learning for Constrained Markov Decision Processes with Stochastic Stopping Time

arXiv

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arXiv 2024年

作者： Mazumdar, Abhijit Wisniewski, Rafal Bujorianu, Manuela L. Section of Automation & Control Aalborg University Aalborg East9220 Denmark The Department of Computer Science University College London United Kingdom

关键词： Reinforcement learning

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Online Learning of Safety function for Markov Decision Processes

Online Learning of Safety function for Markov Decision Proce...

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European control Conference (ECC)

作者： Abhijit Mazumdar Rafal Wisniewski Manuela L. Bujorianu Section of Automation & Control Aalborg University Aalborg East Denmark Department of Computer Science University College London UK

In this paper, we aim to study safety specifications for a Markov decision process with stochastic stopping time in an almost model-free setting. Our approach involves characterizing a proxy set of the states that are near in a probabilistic sense to the set of unsafe states - forbidden set. We also provide results that relate safety function with reinforcement learning. Consequently, we develop an online algorithm based on the temporal difference method to compute the safety function. Finally, we provide simulation results that demonstrate our work in a simple example.

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From MDP to POMDP and Back: Safety and Compositionality

From MDP to POMDP and Back: Safety and Compositionality

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European control Conference (ECC)

作者： Manuela L. Bujorianu Tristan Caulfield David Pym Rafael Wisniewski Department of Computer Science University College London UK Section of Automation & Control Aalborg University Aalborg East Denmark

We propose a compositional framework for the stochastic safety of distributed Markov Decision Processes (MDPs) and Partially Observable Markov Decision Processes (POMDPs). We use MDP and POMDPs and their distributed versions as an appropriate modelling paradigm for computational ecosystems, understood in the context of distributed systems. We extend our work on stochastic safety from MDPs to POMDPs, and then to networked MDP/POMDPs. We propose a unifying mathematical framework for stochastic safety for MDPs, their partially observable version and their composition.

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Online Model-free Safety Verification for Markov Decision Processes Without Safety Violation

arXiv

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arXiv 2023年

作者： Mazumdar, Abhijit Wisniewski, Rafal Bujorianu, Manuela L. The Section of Automation & Control Aalborg University Aalborg9220 Denmark The Department of Computer Science University College London United Kingdom

In this paper, we consider the problem of safety assessment for Markov decision processes without explicit knowledge of the model. We aim to learn probabilistic safety specifications associated with a given policy without compromising the safety of the process. To accomplish our goal, we characterize a subset of the state-space called proxy set, which contains the states that are near in a probabilistic sense to the forbidden set consisting of all unsafe states. We compute the safety function using the single-step temporal difference method. To this end, we relate the safety function computation to that of the value function estimation using temporal difference learning. Since the given control policy could be unsafe, we use a safe baseline sub-policy to generate data for learning. We then use an off-policy temporal difference learning method with importance sampling to learn the safety function corresponding to the given policy. Finally, we demonstrate our results using a numerical example. © 2023, CC BY-NC-ND.

关键词： Markov processes

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