Pub Date : 2023-10-09DOI: 10.1109/OJCSYS.2023.3322906
Erick Mejia Uzeda;Mireille E. Broucke
We consider the problem of mixed feedforward and feedback based disturbance rejection, where the feedforward measurement only provides a partial reconstruction of the disturbance. In doing so, we pose a new biologically relevant disturbance rejection problem which puts the role of feedforward measurements at the forefront. Based on the architecture of the human brain, we propose a design that utilizes an adaptive internal model operating on a fast timescale that, in turn, trains the correct feedforward gains on a slow timescale. As such, the training of reflexes in biological systems can be explained by leveraging the theory of adaptive feedforward control. It is proven that our design provides an arbitrary level of disturbance attenuation, and the benefits of using reflexes are illustrated via a multitude of simulations.
{"title":"Training Reflexes Using Adaptive Feedforward Control","authors":"Erick Mejia Uzeda;Mireille E. Broucke","doi":"10.1109/OJCSYS.2023.3322906","DOIUrl":"https://doi.org/10.1109/OJCSYS.2023.3322906","url":null,"abstract":"We consider the problem of mixed feedforward and feedback based disturbance rejection, where the feedforward measurement only provides a partial reconstruction of the disturbance. In doing so, we pose a new biologically relevant disturbance rejection problem which puts the role of feedforward measurements at the forefront. Based on the architecture of the human brain, we propose a design that utilizes an adaptive internal model operating on a fast timescale that, in turn, trains the correct feedforward gains on a slow timescale. As such, the training of reflexes in biological systems can be explained by leveraging the theory of adaptive feedforward control. It is proven that our design provides an arbitrary level of disturbance attenuation, and the benefits of using reflexes are illustrated via a multitude of simulations.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"2 ","pages":"396-409"},"PeriodicalIF":0.0,"publicationDate":"2023-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9552933/9973428/10274847.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50376167","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-06DOI: 10.1109/OJCSYS.2023.3322636
Kameron Eves;John Valasek
Singularly perturbed systems are a class of mathematical systems that are not well approximated by their limits and can be used to model plants with multiple fast and slow states. Multiple-timescale systems are very common in engineering applications, but adaptive control can be sensitive to timescale effects. Recently a method called [K]control of Adaptive Multiple-timescale Systems (KAMS) has shown improved performance and increased robustness for singularly perturbed systems, but it has only been studied on systems using adaptive control for the slow states. This article extends KAMS to the general case when adaptive control is used to stabilize both the slow and fast states simultaneously. This causes complex interactions between the fast state reference model and the manifold to which the fast states converge. It is proven that under certain conditions the system still converges to the reference model despite these complex interactions. This method is demonstrated on a nonlinear, nonstandard, numerical example.
{"title":"Adaptive Control for Singularly Perturbed Systems","authors":"Kameron Eves;John Valasek","doi":"10.1109/OJCSYS.2023.3322636","DOIUrl":"10.1109/OJCSYS.2023.3322636","url":null,"abstract":"Singularly perturbed systems are a class of mathematical systems that are not well approximated by their limits and can be used to model plants with multiple fast and slow states. Multiple-timescale systems are very common in engineering applications, but adaptive control can be sensitive to timescale effects. Recently a method called [K]control of Adaptive Multiple-timescale Systems (KAMS) has shown improved performance and increased robustness for singularly perturbed systems, but it has only been studied on systems using adaptive control for the slow states. This article extends KAMS to the general case when adaptive control is used to stabilize both the slow and fast states simultaneously. This causes complex interactions between the fast state reference model and the manifold to which the fast states converge. It is proven that under certain conditions the system still converges to the reference model despite these complex interactions. This method is demonstrated on a nonlinear, nonstandard, numerical example.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"3 ","pages":"1-13"},"PeriodicalIF":0.0,"publicationDate":"2023-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10273579","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136008098","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This article addresses the problem of data-driven model discrimination for unknown switched systems with unknown linear temporal logic (LTL) specifications, representing tasks, that govern their mode sequences, where only sampled data of the unknown dynamics and tasks are available. To tackle this problem, we propose data-driven methods to over-approximate the unknown dynamics and to infer the unknown specifications such that both set-membership models of the unknown dynamics and LTL formulas are guaranteed to include the ground truth model and specification/task. Moreover, we present an optimization-based algorithm for analyzing the distinguishability of a set of learned/inferred model-task pairs as well as a model discrimination algorithm for ruling out model-task pairs from this set that are inconsistent with new observations at run time. Further, we present an approach for reducing the size of inferred specifications to increase the computational efficiency of the model discrimination algorithms.
{"title":"Data-Driven Model Discrimination of Switched Nonlinear Systems With Temporal Logic Inference","authors":"Zeyuan Jin;Nasim Baharisangari;Zhe Xu;Sze Zheng Yong","doi":"10.1109/OJCSYS.2023.3322069","DOIUrl":"10.1109/OJCSYS.2023.3322069","url":null,"abstract":"This article addresses the problem of data-driven model discrimination for unknown switched systems with unknown linear temporal logic (LTL) specifications, representing tasks, that govern their mode sequences, where only sampled data of the unknown dynamics and tasks are available. To tackle this problem, we propose data-driven methods to over-approximate the unknown dynamics and to infer the unknown specifications such that both set-membership models of the unknown dynamics and LTL formulas are guaranteed to include the ground truth model and specification/task. Moreover, we present an optimization-based algorithm for analyzing the distinguishability of a set of learned/inferred model-task pairs as well as a model discrimination algorithm for ruling out model-task pairs from this set that are inconsistent with new observations at run time. Further, we present an approach for reducing the size of inferred specifications to increase the computational efficiency of the model discrimination algorithms.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"2 ","pages":"410-424"},"PeriodicalIF":0.0,"publicationDate":"2023-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10271526","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135955069","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-28DOI: 10.1109/OJCSYS.2023.3320512
Babak Salamat;Abolfazl Yaghmaei;Gerhard Elsbacher;Andrea M. Tonello;Mohammad Javad Yazdanpanah
In this article, we propose a procedure to solve the controlled design for a class of under-actuated mechanical systems. Our proposed method can be viewed as a sub-method of the IDA-PBC or Controlled Lagrangian approaches, with a particular focus on shaping the potential energy. By emphasizing potential energy shaping, we can effectively tackle the bottleneck presented by the matching equation in these approaches. Moreover, our method leverages a suitable coordinate transformation that is inspired by the physics of the system, further enhancing its efficacy. Therefore, our design procedure is based on a coordinate transformation plus potential energy shaping in the new coordinates, and its existence and possibility of potential energy shaping can be verified via some algebraic calculations, making it constructive. To illustrate the results, we consider the cart-pole system and a recently introduced under-actuated mechanical system named swash mass pendulum (SMP) (Salamat and Tonello, 2021). The SMP consists of a pendulum made of a rigid shaft connected to a pair of cross-shafts where two swash masses can move under the action of servo-mechanisms.
{"title":"An Innovative Control Design Procedure for Under-Actuated Mechanical Systems: Emphasizing Potential Energy Shaping and Structural Preservation","authors":"Babak Salamat;Abolfazl Yaghmaei;Gerhard Elsbacher;Andrea M. Tonello;Mohammad Javad Yazdanpanah","doi":"10.1109/OJCSYS.2023.3320512","DOIUrl":"https://doi.org/10.1109/OJCSYS.2023.3320512","url":null,"abstract":"In this article, we propose a procedure to solve the controlled design for a class of under-actuated mechanical systems. Our proposed method can be viewed as a sub-method of the IDA-PBC or Controlled Lagrangian approaches, with a particular focus on shaping the potential energy. By emphasizing potential energy shaping, we can effectively tackle the bottleneck presented by the matching equation in these approaches. Moreover, our method leverages a suitable coordinate transformation that is inspired by the physics of the system, further enhancing its efficacy. Therefore, our design procedure is based on a coordinate transformation plus potential energy shaping in the new coordinates, and its existence and possibility of potential energy shaping can be verified via some algebraic calculations, making it constructive. To illustrate the results, we consider the cart-pole system and a recently introduced under-actuated mechanical system named swash mass pendulum (SMP) (Salamat and Tonello, 2021). The SMP consists of a pendulum made of a rigid shaft connected to a pair of cross-shafts where two swash masses can move under the action of servo-mechanisms.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"2 ","pages":"356-365"},"PeriodicalIF":0.0,"publicationDate":"2023-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9552933/9973428/10266689.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50376165","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-15DOI: 10.1109/OJCSYS.2023.3316071
Yuanhanqing Huang;Jianghai Hu
Non-cooperative games serve as a powerful framework for capturing the interactions among self-interested players and have broad applicability in modeling a wide range of practical scenarios, ranging from power management to path planning of self-driving vehicles. Although most existing solution algorithms assume the availability of first-order information or full knowledge of the objectives and others' action profiles, there are situations where the only accessible information at players' disposal is the realized objective function values. In this article, we devise a bandit online learning algorithm that integrates the optimistic mirror descent scheme and multi-point pseudo-gradient estimates. We further prove that the generated actual sequence of play converges a.s. to a critical point if the game under study is globally merely coherent, without resorting to extra Tikhonov regularization terms or additional norm conditions. We also discuss the convergence properties of the proposed bandit learning algorithm in locally merely coherent games. Finally, we illustrate the validity of the proposed algorithm via two two-player minimax problems and a cognitive radio bandwidth allocation game.
{"title":"Global and Local Convergence Analysis of a Bandit Learning Algorithm in Merely Coherent Games","authors":"Yuanhanqing Huang;Jianghai Hu","doi":"10.1109/OJCSYS.2023.3316071","DOIUrl":"https://doi.org/10.1109/OJCSYS.2023.3316071","url":null,"abstract":"Non-cooperative games serve as a powerful framework for capturing the interactions among self-interested players and have broad applicability in modeling a wide range of practical scenarios, ranging from power management to path planning of self-driving vehicles. Although most existing solution algorithms assume the availability of first-order information or full knowledge of the objectives and others' action profiles, there are situations where the only accessible information at players' disposal is the realized objective function values. In this article, we devise a bandit online learning algorithm that integrates the optimistic mirror descent scheme and multi-point pseudo-gradient estimates. We further prove that the generated actual sequence of play converges a.s. to a critical point if the game under study is globally merely coherent, without resorting to extra Tikhonov regularization terms or additional norm conditions. We also discuss the convergence properties of the proposed bandit learning algorithm in locally merely coherent games. Finally, we illustrate the validity of the proposed algorithm via two two-player minimax problems and a cognitive radio bandwidth allocation game.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"2 ","pages":"366-379"},"PeriodicalIF":0.0,"publicationDate":"2023-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9552933/9973428/10251984.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50226403","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-15DOI: 10.1109/OJCSYS.2023.3316090
Zhan Gao;Amanda Prorok
Traditional approaches for multi-agent navigation consider the environment as a fixed constraint, despite the obvious influence of spatial constraints on agents' performance. Yet hand-designing conducive environments is inefficient and potentially expensive. The goal of this article is to consider the obstacle layout of the environment as a decision variable in a system-level optimization problem. In other words, we aim to find an automated solution that optimizes the obstacle layout to improve the performance of multi-agent navigation, under a variety of realistic constraints. Towards this end, we propose novel problems of unprioritized and prioritized environment optimization , where the former considers agents unbiasedly and the latter incorporates agent priorities into optimization. We show, through formal proofs, under which conditions the environment can change to guarantee completeness (i.e., all agents reach goals), and analyze the role of agent priorities in the environment optimization. We proceed to impose constraints on the environment optimization that correspond to real-world restrictions on obstacle changes, and formulate it mathematically as a constrained stochastic optimization problem. Since the relationship between agents, environment and performance is challenging to model, we leverage reinforcement learning to develop a model-free solution and a primal-dual mechanism to handle constraints. Distinct information processing architectures are integrated for various implementation scenarios, including online/offline optimization and discrete/continuous environment. Numerical results corroborate the theory and demonstrate the validity and adaptability of our approach.
{"title":"Constrained Environment Optimization for Prioritized Multi-Agent Navigation","authors":"Zhan Gao;Amanda Prorok","doi":"10.1109/OJCSYS.2023.3316090","DOIUrl":"https://doi.org/10.1109/OJCSYS.2023.3316090","url":null,"abstract":"Traditional approaches for multi-agent navigation consider the environment as a fixed constraint, despite the obvious influence of spatial constraints on agents' performance. Yet hand-designing conducive environments is inefficient and potentially expensive. The goal of this article is to consider the obstacle layout of the environment as a decision variable in a system-level optimization problem. In other words, we aim to find an automated solution that optimizes the obstacle layout to improve the performance of multi-agent navigation, under a variety of realistic constraints. Towards this end, we propose novel problems of \u0000<italic>unprioritized</i>\u0000 and \u0000<italic>prioritized environment optimization</i>\u0000, where the former considers agents unbiasedly and the latter incorporates agent priorities into optimization. We show, through formal proofs, under which conditions the environment can change to guarantee completeness (i.e., all agents reach goals), and analyze the role of agent priorities in the environment optimization. We proceed to impose constraints on the environment optimization that correspond to real-world restrictions on obstacle changes, and formulate it mathematically as a constrained stochastic optimization problem. Since the relationship between agents, environment and performance is challenging to model, we leverage reinforcement learning to develop a model-free solution and a primal-dual mechanism to handle constraints. Distinct information processing architectures are integrated for various implementation scenarios, including online/offline optimization and discrete/continuous environment. Numerical results corroborate the theory and demonstrate the validity and adaptability of our approach.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"2 ","pages":"337-355"},"PeriodicalIF":0.0,"publicationDate":"2023-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9552933/9973428/10251921.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50376164","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-13DOI: 10.1109/OJCSYS.2023.3315088
Romain Cosson;Ali Jadbabaie;Anuran Makur;Amirhossein Reisizadeh;Devavrat Shah
Several recent empirical studies demonstrate that important machine learning tasks such as training deep neural networks, exhibit a low-rank structure, where most of the variation in the loss function occurs only in a few directions of the input space. In this article, we leverage such low-rank structure to reduce the high computational cost of canonical gradient-based methods such as gradient descent ( GD ). Our proposed Low-Rank Gradient Descent ( LRGD ) algorithm finds an $epsilon$ -approximate stationary point of a $p$ -dimensional function by first identifying $r leq p$ significant directions, and then estimating the true $p$ -dimensional gradient at every iteration by computing directional derivatives only along those $r$ directions. We establish that the “directional oracle complexities” of LRGD for strongly convex and non-convex objective functions are ${mathcal {O}}(r log (1/epsilon) + rp)$ and ${mathcal {O}}(r/epsilon ^{2} + rp)$ , respectively. Therefore, when $r ll p$ , LRGD provides significant improvement over the known complexities of ${mathcal {O}}(p log (1/epsilon))$ and ${mathcal {O}}(p/epsilon ^{2})$ of GD in the strongly convex and non-convex settings, respectively. Furthermore, we formally characterize the classes of exactly and approximately low-rank functions. Empirically, using real and synthetic data, LRGD provides significant gains over GD when the data has low-rank structure, and in the absence of such structure, LRGD does not degrade performance compared to GD . This suggests that LRGD could be used in practice in any setting in place of GD .
{"title":"Low-Rank Gradient Descent","authors":"Romain Cosson;Ali Jadbabaie;Anuran Makur;Amirhossein Reisizadeh;Devavrat Shah","doi":"10.1109/OJCSYS.2023.3315088","DOIUrl":"https://doi.org/10.1109/OJCSYS.2023.3315088","url":null,"abstract":"Several recent empirical studies demonstrate that important machine learning tasks such as training deep neural networks, exhibit a low-rank structure, where most of the variation in the loss function occurs only in a few directions of the input space. In this article, we leverage such low-rank structure to reduce the high computational cost of canonical gradient-based methods such as gradient descent (\u0000<monospace>GD</monospace>\u0000). Our proposed \u0000<italic>Low-Rank Gradient Descent</i>\u0000 (\u0000<monospace>LRGD</monospace>\u0000) algorithm finds an \u0000<inline-formula><tex-math>$epsilon$</tex-math></inline-formula>\u0000-approximate stationary point of a \u0000<inline-formula><tex-math>$p$</tex-math></inline-formula>\u0000-dimensional function by first identifying \u0000<inline-formula><tex-math>$r leq p$</tex-math></inline-formula>\u0000 significant directions, and then estimating the true \u0000<inline-formula><tex-math>$p$</tex-math></inline-formula>\u0000-dimensional gradient at every iteration by computing directional derivatives only along those \u0000<inline-formula><tex-math>$r$</tex-math></inline-formula>\u0000 directions. We establish that the “directional oracle complexities” of \u0000<monospace>LRGD</monospace>\u0000 for strongly convex and non-convex objective functions are \u0000<inline-formula><tex-math>${mathcal {O}}(r log (1/epsilon) + rp)$</tex-math></inline-formula>\u0000 and \u0000<inline-formula><tex-math>${mathcal {O}}(r/epsilon ^{2} + rp)$</tex-math></inline-formula>\u0000, respectively. Therefore, when \u0000<inline-formula><tex-math>$r ll p$</tex-math></inline-formula>\u0000, \u0000<monospace>LRGD</monospace>\u0000 provides significant improvement over the known complexities of \u0000<inline-formula><tex-math>${mathcal {O}}(p log (1/epsilon))$</tex-math></inline-formula>\u0000 and \u0000<inline-formula><tex-math>${mathcal {O}}(p/epsilon ^{2})$</tex-math></inline-formula>\u0000 of \u0000<monospace>GD</monospace>\u0000 in the strongly convex and non-convex settings, respectively. Furthermore, we formally characterize the classes of exactly and approximately low-rank functions. Empirically, using real and synthetic data, \u0000<monospace>LRGD</monospace>\u0000 provides significant gains over \u0000<monospace>GD</monospace>\u0000 when the data has low-rank structure, and in the absence of such structure, \u0000<monospace>LRGD</monospace>\u0000 does not degrade performance compared to \u0000<monospace>GD</monospace>\u0000. This suggests that \u0000<monospace>LRGD</monospace>\u0000 could be used in practice in any setting in place of \u0000<monospace>GD</monospace>\u0000.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"2 ","pages":"380-395"},"PeriodicalIF":0.0,"publicationDate":"2023-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9552933/9973428/10250907.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50226404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-24DOI: 10.1109/OJCSYS.2023.3308009
Jing Shuang Li;Carmen Amo Alonso
Recent advances in model predictive control (MPC) leverage local communication constraints to produce localized MPC algorithms whose complexities scale independently of total network size. However, no characterization is available regarding global performance, i.e. whether localized MPC (with communication constraints) performs just as well as global MPC (no communication constraints). In this paper, we provide analysis and guarantees on global performance of localized MPC — in particular, we derive sufficient conditions for optimal global performance in the presence of local communication constraints. We also present an algorithm to determine the communication structure for a given system that will preserve performance while minimizing computational complexity. The effectiveness of the algorithm is verified in simulations, and additional relationships between network properties and performance-preserving communication constraints are characterized. A striking finding is that in a network of 121 coupled pendula, each node only needs to communicate with its immediate neighbors to preserve optimal global performance. Overall, this work offers theoretical understanding on the effect of local communication on global performance, and provides practitioners with the tools necessary to deploy localized model predictive control by establishing a rigorous method of selecting local communication constraints. This work also demonstrates — surprisingly — that the inclusion of severe communication constraints need not compromise global performance.
{"title":"Global Performance Guarantees for Localized Model Predictive Control","authors":"Jing Shuang Li;Carmen Amo Alonso","doi":"10.1109/OJCSYS.2023.3308009","DOIUrl":"https://doi.org/10.1109/OJCSYS.2023.3308009","url":null,"abstract":"Recent advances in model predictive control (MPC) leverage local communication constraints to produce localized MPC algorithms whose complexities scale independently of total network size. However, no characterization is available regarding global performance, i.e. whether localized MPC (with communication constraints) performs just as well as global MPC (no communication constraints). In this paper, we provide analysis and guarantees on global performance of localized MPC — in particular, we derive sufficient conditions for optimal global performance in the presence of local communication constraints. We also present an algorithm to determine the communication structure for a given system that will preserve performance while minimizing computational complexity. The effectiveness of the algorithm is verified in simulations, and additional relationships between network properties and performance-preserving communication constraints are characterized. A striking finding is that in a network of 121 coupled pendula, each node only needs to communicate with its immediate neighbors to preserve optimal global performance. Overall, this work offers theoretical understanding on the effect of local communication on global performance, and provides practitioners with the tools necessary to deploy localized model predictive control by establishing a rigorous method of selecting local communication constraints. This work also demonstrates — surprisingly — that the inclusion of severe communication constraints need not compromise global performance.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"2 ","pages":"325-336"},"PeriodicalIF":0.0,"publicationDate":"2023-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9552933/9973428/10229197.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50226364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-16DOI: 10.1109/OJCSYS.2023.3305831
Sribalaji C. Anand;André M. H. Teixeira
This article considers the problem of risk-optimal allocation of security measures when the actuators of an uncertain control system are under attack. We consider an adversary injecting false data into the actuator channels. The attack impact is characterized by the maximum performance loss caused by a stealthy adversary with bounded energy. Since the impact is a random variable, due to system uncertainty, we use Conditional Value-at-Risk (CVaR) to characterize the risk associated with the attack. We then consider the problem of allocating security measures to the set of actuators to minimize the risk. We assume that there are only a limited number of security measures available. Under this constraint, we observe that the allocation problem is a mixed-integer optimization problem. Thus we use relaxation techniques to approximate the security allocation problem into a Semi-Definite Program (SDP). We also compare our allocation method $(i)$ across different risk measures: the worst-case measure, the average (nominal) measure, and $(ii)$ across different search algorithms: the exhaustive and the greedy search algorithms. We depict the efficacy of our approach through numerical examples.
{"title":"Risk-Based Security Measure Allocation Against Actuator Attacks","authors":"Sribalaji C. Anand;André M. H. Teixeira","doi":"10.1109/OJCSYS.2023.3305831","DOIUrl":"https://doi.org/10.1109/OJCSYS.2023.3305831","url":null,"abstract":"This article considers the problem of risk-optimal allocation of security measures when the actuators of an uncertain control system are under attack. We consider an adversary injecting false data into the actuator channels. The attack impact is characterized by the maximum performance loss caused by a stealthy adversary with bounded energy. Since the impact is a random variable, due to system uncertainty, we use Conditional Value-at-Risk (CVaR) to characterize the risk associated with the attack. We then consider the problem of allocating security measures to the set of actuators to minimize the risk. We assume that there are only a limited number of security measures available. Under this constraint, we observe that the allocation problem is a mixed-integer optimization problem. Thus we use relaxation techniques to approximate the security allocation problem into a Semi-Definite Program (SDP). We also compare our allocation method \u0000<inline-formula><tex-math>$(i)$</tex-math></inline-formula>\u0000 across different risk measures: the worst-case measure, the average (nominal) measure, and \u0000<inline-formula><tex-math>$(ii)$</tex-math></inline-formula>\u0000 across different search algorithms: the exhaustive and the greedy search algorithms. We depict the efficacy of our approach through numerical examples.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"2 ","pages":"297-309"},"PeriodicalIF":0.0,"publicationDate":"2023-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9552933/9973428/10221684.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50226363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-16DOI: 10.1109/OJCSYS.2023.3305835
Satya Prakash Nayak;Lucas N. Egidio;Matteo Della Rossa;Anne-Kathrin Schmuck;Raphael M. Jungers
We consider the problem of automatically synthesizing a hybrid controller for non-linear dynamical systems which ensures that the closed-loop fulfills an arbitrary Linear Temporal Logic specification. Moreover, the specification may take into account logical context switches induced by an external environment or the system itself. Finally, we want to avoid classical brute-force time- and space-discretization for scalability. We achieve these goals by a novel two-layer strategy synthesis approach, where the controller generated in the lower layer provides invariant sets and basins of attraction, which are exploited at the upper logical layer in an abstract way. In order to achieve this, we provide new techniques for both the upper- and lower-level synthesis. Our new methodology allows to leverage both the computing power of state space control techniques and the intelligence of finite game solving for complex specifications, in a scalable way.
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