Learning in games provides a powerful framework to design control policies for self-interested agents that may be coupled through their dynamics, costs, or constraints. We consider the case where the dynamics of the coupled system can be modeled as a Markov potential game. In this case, distributed learning by the agents ensures that their control policies converge to a Nash equilibrium of this game. However, typical learning algorithms such as natural policy gradient require knowledge of the entire global state and actions of all the other agents, and may not be scalable as the number of agents grows. We show that by limiting the information flow to a local neighborhood of agents in the natural policy gradient algorithm, we can converge to a neighborhood of optimal policies. If the game can be designed through decomposing a global cost function of interest to a designer into local costs for the agents such that their policies at equilibrium optimize the global cost, this approach can be of interest to team coordination problems as well. We illustrate our approach through a sensor coverage problem.
{"title":"A Scalable Game Theoretic Approach for Coordination of Multiple Dynamic Systems","authors":"Mostafa M. Shibl, Vijay Gupta","doi":"arxiv-2409.11358","DOIUrl":"https://doi.org/arxiv-2409.11358","url":null,"abstract":"Learning in games provides a powerful framework to design control policies\u0000for self-interested agents that may be coupled through their dynamics, costs,\u0000or constraints. We consider the case where the dynamics of the coupled system\u0000can be modeled as a Markov potential game. In this case, distributed learning\u0000by the agents ensures that their control policies converge to a Nash\u0000equilibrium of this game. However, typical learning algorithms such as natural\u0000policy gradient require knowledge of the entire global state and actions of all\u0000the other agents, and may not be scalable as the number of agents grows. We\u0000show that by limiting the information flow to a local neighborhood of agents in\u0000the natural policy gradient algorithm, we can converge to a neighborhood of\u0000optimal policies. If the game can be designed through decomposing a global cost\u0000function of interest to a designer into local costs for the agents such that\u0000their policies at equilibrium optimize the global cost, this approach can be of\u0000interest to team coordination problems as well. We illustrate our approach\u0000through a sensor coverage problem.","PeriodicalId":501175,"journal":{"name":"arXiv - EE - Systems and Control","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142264310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
David A. Robb, Donald Risbridger, Ben Mills, Ildar Rakhmatulin, Xianwen Kong, Mustafa Erden, M. J. Daniel Esser, Richard M. Carter, Mike J. Chantler
The alignment of optical systems is a critical step in their manufacture. Alignment normally requires considerable knowledge and expertise of skilled operators. The automation of such processes has several potential advantages, but requires additional resource and upfront costs. Through a case study of a simple two mirror system we identify and examine three different automation approaches. They are: artificial neural networks; practice-led, which mimics manual alignment practices; and design-led, modelling from first principles. We find that these approaches make use of three different types of knowledge 1) basic system knowledge (of controls, measurements and goals); 2) behavioural skills and expertise, and 3) fundamental system design knowledge. We demonstrate that the different automation approaches vary significantly in human resources, and measurement sampling budgets. This will have implications for practitioners and management considering the automation of such tasks.
{"title":"Three Approaches to the Automation of Laser System Alignment and Their Resource Implications: A Case Study","authors":"David A. Robb, Donald Risbridger, Ben Mills, Ildar Rakhmatulin, Xianwen Kong, Mustafa Erden, M. J. Daniel Esser, Richard M. Carter, Mike J. Chantler","doi":"arxiv-2409.11090","DOIUrl":"https://doi.org/arxiv-2409.11090","url":null,"abstract":"The alignment of optical systems is a critical step in their manufacture.\u0000Alignment normally requires considerable knowledge and expertise of skilled\u0000operators. The automation of such processes has several potential advantages,\u0000but requires additional resource and upfront costs. Through a case study of a\u0000simple two mirror system we identify and examine three different automation\u0000approaches. They are: artificial neural networks; practice-led, which mimics\u0000manual alignment practices; and design-led, modelling from first principles. We\u0000find that these approaches make use of three different types of knowledge 1)\u0000basic system knowledge (of controls, measurements and goals); 2) behavioural\u0000skills and expertise, and 3) fundamental system design knowledge. We\u0000demonstrate that the different automation approaches vary significantly in\u0000human resources, and measurement sampling budgets. This will have implications\u0000for practitioners and management considering the automation of such tasks.","PeriodicalId":501175,"journal":{"name":"arXiv - EE - Systems and Control","volume":"34 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142264317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper presents an approach that employs log-linearization in Lie group theory and the Newton-Euler equations to derive exact linear error dynamics for a multi-rotor model, and applies this model with a novel log-linear dynamic inversion controller to simplify the nonlinear distortion and enhance the robustness of the log-linearized system. In addition, we utilize Linear Matrix Inequalities (LMIs) to bound the tracking error for the log-linearization in the presence of bounded disturbance input and use the exponential map to compute the invariant set of the nonlinear system in the Lie group. We demonstrate the effectiveness of our method via an illustrative example of a multi-rotor system with a reference trajectory, and the result validates the safety guarantees of the tracking error in the presence of bounded disturbance.
{"title":"Application of Log-Linear Dynamic Inversion Control to a Multi-rotor","authors":"Li-Yu Lin, James Goppert, Inseok Hwang","doi":"arxiv-2409.10866","DOIUrl":"https://doi.org/arxiv-2409.10866","url":null,"abstract":"This paper presents an approach that employs log-linearization in Lie group\u0000theory and the Newton-Euler equations to derive exact linear error dynamics for\u0000a multi-rotor model, and applies this model with a novel log-linear dynamic\u0000inversion controller to simplify the nonlinear distortion and enhance the\u0000robustness of the log-linearized system. In addition, we utilize Linear Matrix\u0000Inequalities (LMIs) to bound the tracking error for the log-linearization in\u0000the presence of bounded disturbance input and use the exponential map to\u0000compute the invariant set of the nonlinear system in the Lie group. We\u0000demonstrate the effectiveness of our method via an illustrative example of a\u0000multi-rotor system with a reference trajectory, and the result validates the\u0000safety guarantees of the tracking error in the presence of bounded disturbance.","PeriodicalId":501175,"journal":{"name":"arXiv - EE - Systems and Control","volume":"31 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142264319","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, we consider the problem of coordinating traffic light systems and connected automated vehicles (CAVs) in mixed-traffic intersections. We aim to develop an optimization-based control framework that leverages both the coordination capabilities of CAVs at higher penetration rates and intelligent traffic management using traffic lights at lower penetration rates. Since the resulting optimization problem is a multi-agent mixed-integer quadratic program, we propose a penalization-enhanced maximum block improvement algorithm to solve the problem in a distributed manner. The proposed algorithm, under certain mild conditions, yields a feasible and person-by-person optimal solution of the centralized problem. The performance of the control framework and the distributed algorithm is validated through simulations across various penetration rates and traffic volumes.
{"title":"Distributed Optimization for Traffic Light Control and Connected Automated Vehicle Coordination in Mixed-Traffic Intersections","authors":"Viet-Anh Le, Andreas A. Malikopoulos","doi":"arxiv-2409.10864","DOIUrl":"https://doi.org/arxiv-2409.10864","url":null,"abstract":"In this paper, we consider the problem of coordinating traffic light systems\u0000and connected automated vehicles (CAVs) in mixed-traffic intersections. We aim\u0000to develop an optimization-based control framework that leverages both the\u0000coordination capabilities of CAVs at higher penetration rates and intelligent\u0000traffic management using traffic lights at lower penetration rates. Since the\u0000resulting optimization problem is a multi-agent mixed-integer quadratic\u0000program, we propose a penalization-enhanced maximum block improvement algorithm\u0000to solve the problem in a distributed manner. The proposed algorithm, under\u0000certain mild conditions, yields a feasible and person-by-person optimal\u0000solution of the centralized problem. The performance of the control framework\u0000and the distributed algorithm is validated through simulations across various\u0000penetration rates and traffic volumes.","PeriodicalId":501175,"journal":{"name":"arXiv - EE - Systems and Control","volume":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142264320","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This work introduces a novel control-aware distributed process monitoring methodology based on modules comprised of clusters of interacting measurements. The methodology relies on the process flow diagram (PFD) and control system structure without requiring cross-correlation data to create monitoring modules. The methodology is validated on the Tennessee Eastman Process benchmark using full Principal Component Analysis (f-PCA) in the monitoring modules. The results are comparable to nonlinear techniques implemented in a centralized manner such as Kernel PCA (KPCA), Autoencoders (AE), and Recurrent Neural Networks (RNN), or distributed techniques like the Distributed Canonical Correlation Analysis (DCCA). Temporal plots of fault detection by different modules show clearly the magnitude and propagation of the fault through each module, pinpointing the module where the fault originates, and separating controllable faults from other faults. This information, combined with PCA contribution plots, helps detection and identification as effectively as more complex nonlinear centralized or distributed methods.
{"title":"Fault Detection and Identification via Monitoring Modules Based on Clusters of Interacting Measurements","authors":"Enrique Luna Villagomez, Vladimir Mahalec","doi":"arxiv-2409.11444","DOIUrl":"https://doi.org/arxiv-2409.11444","url":null,"abstract":"This work introduces a novel control-aware distributed process monitoring\u0000methodology based on modules comprised of clusters of interacting measurements.\u0000The methodology relies on the process flow diagram (PFD) and control system\u0000structure without requiring cross-correlation data to create monitoring\u0000modules. The methodology is validated on the Tennessee Eastman Process\u0000benchmark using full Principal Component Analysis (f-PCA) in the monitoring\u0000modules. The results are comparable to nonlinear techniques implemented in a\u0000centralized manner such as Kernel PCA (KPCA), Autoencoders (AE), and Recurrent\u0000Neural Networks (RNN), or distributed techniques like the Distributed Canonical\u0000Correlation Analysis (DCCA). Temporal plots of fault detection by different\u0000modules show clearly the magnitude and propagation of the fault through each\u0000module, pinpointing the module where the fault originates, and separating\u0000controllable faults from other faults. This information, combined with PCA\u0000contribution plots, helps detection and identification as effectively as more\u0000complex nonlinear centralized or distributed methods.","PeriodicalId":501175,"journal":{"name":"arXiv - EE - Systems and Control","volume":"19 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142264254","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Caio Fabio Oliveira da Silva, Azita Dabiri, Bart De Schutter
This work proposes an approach that integrates reinforcement learning and model predictive control (MPC) to efficiently solve finite-horizon optimal control problems in mixed-logical dynamical systems. Optimization-based control of such systems with discrete and continuous decision variables entails the online solution of mixed-integer quadratic or linear programs, which suffer from the curse of dimensionality. Our approach aims at mitigating this issue by effectively decoupling the decision on the discrete variables and the decision on the continuous variables. Moreover, to mitigate the combinatorial growth in the number of possible actions due to the prediction horizon, we conceive the definition of decoupled Q-functions to make the learning problem more tractable. The use of reinforcement learning reduces the online optimization problem of the MPC controller from a mixed-integer linear (quadratic) program to a linear (quadratic) program, greatly reducing the computational time. Simulation experiments for a microgrid, based on real-world data, demonstrate that the proposed method significantly reduces the online computation time of the MPC approach and that it generates policies with small optimality gaps and high feasibility rates.
{"title":"Integrating Reinforcement Learning and Model Predictive Control with Applications to Microgrids","authors":"Caio Fabio Oliveira da Silva, Azita Dabiri, Bart De Schutter","doi":"arxiv-2409.11267","DOIUrl":"https://doi.org/arxiv-2409.11267","url":null,"abstract":"This work proposes an approach that integrates reinforcement learning and\u0000model predictive control (MPC) to efficiently solve finite-horizon optimal\u0000control problems in mixed-logical dynamical systems. Optimization-based control\u0000of such systems with discrete and continuous decision variables entails the\u0000online solution of mixed-integer quadratic or linear programs, which suffer\u0000from the curse of dimensionality. Our approach aims at mitigating this issue by\u0000effectively decoupling the decision on the discrete variables and the decision\u0000on the continuous variables. Moreover, to mitigate the combinatorial growth in\u0000the number of possible actions due to the prediction horizon, we conceive the\u0000definition of decoupled Q-functions to make the learning problem more\u0000tractable. The use of reinforcement learning reduces the online optimization\u0000problem of the MPC controller from a mixed-integer linear (quadratic) program\u0000to a linear (quadratic) program, greatly reducing the computational time.\u0000Simulation experiments for a microgrid, based on real-world data, demonstrate\u0000that the proposed method significantly reduces the online computation time of\u0000the MPC approach and that it generates policies with small optimality gaps and\u0000high feasibility rates.","PeriodicalId":501175,"journal":{"name":"arXiv - EE - Systems and Control","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142264313","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Data-driven and adaptive control approaches face the problem of introducing sudden distributional shifts beyond the distribution of data encountered during learning. Therefore, they are prone to invalidating the very assumptions used in their own construction. This is due to the linearity of the underlying system, inherently assumed and formulated in most data-driven control approaches, which may falsely generalize the behavior of the system beyond the behavior experienced in the data. This paper seeks to mitigate these problems by enforcing consistency of the newly designed closed-loop systems with data and slow down any distributional shifts in the joint state-input space. This is achieved through incorporating affine regularization terms and linear matrix inequality constraints to data-driven approaches, resulting in convex semi-definite programs that can be efficiently solved by standard software packages. We discuss the optimality conditions of these programs and then conclude the paper with a numerical example that further highlights the problem of premature generalization beyond data and shows the effectiveness of our proposed approaches in enhancing the safety of data-driven control methods.
{"title":"Data-conforming data-driven control: avoiding premature generalizations beyond data","authors":"Mohammad Ramadan, Evan Toler, Mihai Anitescu","doi":"arxiv-2409.11549","DOIUrl":"https://doi.org/arxiv-2409.11549","url":null,"abstract":"Data-driven and adaptive control approaches face the problem of introducing\u0000sudden distributional shifts beyond the distribution of data encountered during\u0000learning. Therefore, they are prone to invalidating the very assumptions used\u0000in their own construction. This is due to the linearity of the underlying\u0000system, inherently assumed and formulated in most data-driven control\u0000approaches, which may falsely generalize the behavior of the system beyond the\u0000behavior experienced in the data. This paper seeks to mitigate these problems\u0000by enforcing consistency of the newly designed closed-loop systems with data\u0000and slow down any distributional shifts in the joint state-input space. This is\u0000achieved through incorporating affine regularization terms and linear matrix\u0000inequality constraints to data-driven approaches, resulting in convex\u0000semi-definite programs that can be efficiently solved by standard software\u0000packages. We discuss the optimality conditions of these programs and then\u0000conclude the paper with a numerical example that further highlights the problem\u0000of premature generalization beyond data and shows the effectiveness of our\u0000proposed approaches in enhancing the safety of data-driven control methods.","PeriodicalId":501175,"journal":{"name":"arXiv - EE - Systems and Control","volume":"92 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142264250","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ashwini Pondeycherry Ganesh, Anthony Perre, Alphan Sahin, Ismail Guvenc, Brian A. Floyd
Advanced millimeter-wave software-defined array (SDA) platforms, or testbeds at affordable costs and high performance are essential for the wireless community. In this paper, we present a low-cost, portable, and programmable SDA that allows for accessible research and experimentation in real time. The proposed platform is based on a 16-element phased-array transceiver operating across 24-29.5 GHz, integrated with a radio-frequency system-on-chip board that provides data conversion and baseband signal-processing capabilities. All radio-communication parameters and phased-array beam configurations are controlled through a high-level application program interface. We present measurements evaluating the beamforming and communication link performance. Our experimental results validate that the SDA has a beam scan range of -45 to +45 degrees (azimuth), a 3 dB beamwidth of 20 degrees, and support up to a throughput of 1.613 Gb/s using 64-QAM. The signal-to-noise ratio is as high as 30 dB at short-range distances when the transmit and receive beams are aligned.
{"title":"A mmWave Software-Defined Array Platform for Wireless Experimentation at 24-29.5 GHz","authors":"Ashwini Pondeycherry Ganesh, Anthony Perre, Alphan Sahin, Ismail Guvenc, Brian A. Floyd","doi":"arxiv-2409.11480","DOIUrl":"https://doi.org/arxiv-2409.11480","url":null,"abstract":"Advanced millimeter-wave software-defined array (SDA) platforms, or testbeds\u0000at affordable costs and high performance are essential for the wireless\u0000community. In this paper, we present a low-cost, portable, and programmable SDA\u0000that allows for accessible research and experimentation in real time. The\u0000proposed platform is based on a 16-element phased-array transceiver operating\u0000across 24-29.5 GHz, integrated with a radio-frequency system-on-chip board that\u0000provides data conversion and baseband signal-processing capabilities. All\u0000radio-communication parameters and phased-array beam configurations are\u0000controlled through a high-level application program interface. We present\u0000measurements evaluating the beamforming and communication link performance. Our\u0000experimental results validate that the SDA has a beam scan range of -45 to +45\u0000degrees (azimuth), a 3 dB beamwidth of 20 degrees, and support up to a\u0000throughput of 1.613 Gb/s using 64-QAM. The signal-to-noise ratio is as high as\u000030 dB at short-range distances when the transmit and receive beams are aligned.","PeriodicalId":501175,"journal":{"name":"arXiv - EE - Systems and Control","volume":"25 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142264253","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Control barrier function (CBF)-based safety filters are used to certify and modify potentially unsafe control inputs to a system such as those provided by a reinforcement learning agent or a non-expert user. In this context, safety is defined as the satisfaction of state constraints. Originally designed for continuous-time systems, CBF safety filters typically assume that the system's relative degree is well-defined and is constant across the domain; however, this assumption is restrictive and rarely verified -- even linear system dynamics with a quadratic CBF candidate may not satisfy this assumption. In real-world applications, continuous-time CBF safety filters are implemented in discrete time, exacerbating issues related to violating the condition on the relative degree. These violations can lead to the safety filter being unconstrained (any control input may be certified) for a finite time interval and result in chattering issues and constraint violations. We propose an alternative formulation to address these challenges. Specifically, we present a theoretically sound method that employs multiple CBFs to generate bounded control inputs at each state within the safe set, thereby preventing incorrect certification of arbitrary control inputs. Using this approach, we derive conditions on the maximum sampling time to ensure safety in discrete-time implementations. We demonstrate the effectiveness of our proposed method through simulations and real-world quadrotor experiments, successfully preventing chattering and constraint violations. Finally, we discuss the implications of violating the relative degree condition on CBF synthesis and learning-based CBF methods.
{"title":"Preventing Unconstrained CBF Safety Filters Caused by Invalid Relative Degree Assumptions","authors":"Lukas Brunke, Siqi Zhou, Angela P. Schoellig","doi":"arxiv-2409.11171","DOIUrl":"https://doi.org/arxiv-2409.11171","url":null,"abstract":"Control barrier function (CBF)-based safety filters are used to certify and\u0000modify potentially unsafe control inputs to a system such as those provided by\u0000a reinforcement learning agent or a non-expert user. In this context, safety is\u0000defined as the satisfaction of state constraints. Originally designed for\u0000continuous-time systems, CBF safety filters typically assume that the system's\u0000relative degree is well-defined and is constant across the domain; however,\u0000this assumption is restrictive and rarely verified -- even linear system\u0000dynamics with a quadratic CBF candidate may not satisfy this assumption. In\u0000real-world applications, continuous-time CBF safety filters are implemented in\u0000discrete time, exacerbating issues related to violating the condition on the\u0000relative degree. These violations can lead to the safety filter being\u0000unconstrained (any control input may be certified) for a finite time interval\u0000and result in chattering issues and constraint violations. We propose an\u0000alternative formulation to address these challenges. Specifically, we present a\u0000theoretically sound method that employs multiple CBFs to generate bounded\u0000control inputs at each state within the safe set, thereby preventing incorrect\u0000certification of arbitrary control inputs. Using this approach, we derive\u0000conditions on the maximum sampling time to ensure safety in discrete-time\u0000implementations. We demonstrate the effectiveness of our proposed method\u0000through simulations and real-world quadrotor experiments, successfully\u0000preventing chattering and constraint violations. Finally, we discuss the\u0000implications of violating the relative degree condition on CBF synthesis and\u0000learning-based CBF methods.","PeriodicalId":501175,"journal":{"name":"arXiv - EE - Systems and Control","volume":"27 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142264315","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Modern power systems increasingly demand converter-driven generation systems that integrate seamlessly with grid infrastructure. Grid-based converters are particularly advantageous, as they operate in harmony with conventional synchronous machines. However, most existing research focuses on managing grid-forming converters (GFM) under normal conditions, often neglecting the converters' behavior during faults and their short-circuit capabilities. This paper addresses these gaps by introducing a power matching-based current limitation scheme, which ensures GFM converter synchronization while preventing over currents. It also highlights the limitations of grid-following techniques, which need to maintain robust grid-forming properties during fault conditions. Unlike conventional methods, no assumptions are made regarding outer power loops or droop mechanisms, and current references are immediately restricted to prevent wind-ups. A dynamic virtual damping algorithm is proposed to improve fault isolation further. This technique enhances fault-ride-through capability and maintains grid-forming properties even in weak grid conditions. The dynamic virtual damping controller and fault mode for GFMs are modeled and validated using detailed simulations in MATLAB. These results demonstrate that altering outer power sources, rather than internal structures, improves converter performance during faults, ensuring grid stability and reliability.
{"title":"Improved Dynamic Response in Grid-Forming Converters with Current Limiting Control during Fault Conditions","authors":"Somayeh Mehri Boroojeni, Ehsan Sharafoddin","doi":"arxiv-2409.11548","DOIUrl":"https://doi.org/arxiv-2409.11548","url":null,"abstract":"Modern power systems increasingly demand converter-driven generation systems\u0000that integrate seamlessly with grid infrastructure. Grid-based converters are\u0000particularly advantageous, as they operate in harmony with conventional\u0000synchronous machines. However, most existing research focuses on managing\u0000grid-forming converters (GFM) under normal conditions, often neglecting the\u0000converters' behavior during faults and their short-circuit capabilities. This\u0000paper addresses these gaps by introducing a power matching-based current\u0000limitation scheme, which ensures GFM converter synchronization while preventing\u0000over currents. It also highlights the limitations of grid-following techniques,\u0000which need to maintain robust grid-forming properties during fault conditions.\u0000Unlike conventional methods, no assumptions are made regarding outer power\u0000loops or droop mechanisms, and current references are immediately restricted to\u0000prevent wind-ups. A dynamic virtual damping algorithm is proposed to improve\u0000fault isolation further. This technique enhances fault-ride-through capability\u0000and maintains grid-forming properties even in weak grid conditions. The dynamic\u0000virtual damping controller and fault mode for GFMs are modeled and validated\u0000using detailed simulations in MATLAB. These results demonstrate that altering\u0000outer power sources, rather than internal structures, improves converter\u0000performance during faults, ensuring grid stability and reliability.","PeriodicalId":501175,"journal":{"name":"arXiv - EE - Systems and Control","volume":"189 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142264252","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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