IEEE Control Systems Letters最新文献

{"title":"Guaranteed Feasibility in Differentially Private Linearly Constrained Convex Optimization","authors":"Alexander Benvenuti;Brendan Bialy;Miriam Dennis;Matthew Hale","doi":"10.1109/LCSYS.2024.3513232","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3513232","url":null,"abstract":"Convex programming with linear constraints plays an important role in the operation of a number of everyday systems. However, absent any additional protections, revealing or acting on the solutions to such problems may reveal information about their constraints, which can be sensitive. Therefore, in this letter, we introduce a method to keep linear constraints private when solving a convex program. First, we prove that this method is differentially private and always generates a feasible optimization problem (i.e., one whose solution exists). Then we show that the solution to the privatized problem also satisfies the original, non-private constraints. Next, we bound the expected loss in performance from privacy, which is measured by comparing the cost with privacy to that without privacy. Simulation results apply this framework to constrained policy synthesis in a Markov decision process, and they show that a typical privacy implementation induces only an approximately 9% loss in solution quality.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"2745-2750"},"PeriodicalIF":2.4,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142859188","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}

{"title":"Stealthy Optimal Range-Sensor Placement for Target Localization","authors":"Mohammad Hussein Yoosefian Nooshabadi;Rifat Sipahi;Laurent Lessard","doi":"10.1109/LCSYS.2024.3513814","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3513814","url":null,"abstract":"We study a stealthy range-sensor placement problem where a set of range sensors are to be placed with respect to targets to effectively localize them while maintaining a degree of stealthiness from the targets. This is an open and challenging problem since two competing objectives must be balanced: (a) optimally placing the sensors to maximize their ability to localize the targets and (b) minimizing the information the targets gather regarding the sensors. We provide analytical solutions in 2D for the case of any number of sensors that localize two targets.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"2763-2768"},"PeriodicalIF":2.4,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142858884","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}

{"title":"Resilience to Non-Compliance in Coupled Cooperating Systems","authors":"Brooks A. Butler;Philip E. Paré","doi":"10.1109/LCSYS.2024.3513813","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3513813","url":null,"abstract":"This letter explores the implementation of a safe control law for systems of dynamically coupled cooperating agents. Under a CBF-based collaborative safety framework, we examine how the maximum safety capability for a given agent, which is computed using a collaborative safety condition, influences safety requests made to neighbors. We provide conditions under which neighbors may be resilient to non-compliance of neighbors to safety requests, and compute an upper bound for the total amount of non-compliance an agent is resilient to, given its 1-hop neighborhood state and knowledge of the network dynamics. We then illustrate our results via simulations of a networked susceptible-infected-susceptible (SIS) epidemic model.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"2715-2720"},"PeriodicalIF":2.4,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142821239","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}

{"title":"Disturbance-Robust Backup Control Barrier Functions: Safety Under Uncertain Dynamics","authors":"David E. J. van Wijk;Samuel Coogan;Tamas G. Molnar;Manoranjan Majji;Kerianne L. Hobbs","doi":"10.1109/LCSYS.2024.3514998","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3514998","url":null,"abstract":"Obtaining a controlled invariant set is crucial for safety-critical control with control barrier functions (CBFs) but is non-trivial for complex nonlinear systems and constraints. Backup control barrier functions allow such sets to be constructed online in a computationally tractable manner by examining the evolution (or flow) of the system under a known backup control law. However, for systems with unmodeled disturbances, this flow cannot be directly computed, making the current methods inadequate for assuring safety in these scenarios. To address this gap, we leverage bounds on the nominal and disturbed flow to compute a forward invariant set online by ensuring safety of an expanding norm ball tube centered around the nominal system evolution. We prove that this set results in robust control constraints which guarantee safety of the disturbed system via our Disturbance-Robust Backup Control Barrier Function (DR-bCBF) solution. The efficacy of the proposed framework is demonstrated in simulation, applied to a double integrator problem and a rigid body spacecraft rotation problem with rate constraints.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"2817-2822"},"PeriodicalIF":2.4,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142858889","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}

{"title":"Local Linear Convergence of Infeasible Optimization With Orthogonal Constraints","authors":"Youbang Sun;Shixiang Chen;Alfredo Garcia;Shahin Shahrampour","doi":"10.1109/LCSYS.2024.3513817","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3513817","url":null,"abstract":"Many classical and modern machine learning algorithms require solving optimization tasks under orthogonality constraints. Solving these tasks with feasible methods requires a gradient descent update followed by a retraction operation on the Stiefel manifold, which can be computationally expensive. Recently, an infeasible retraction-free approach, termed the landing algorithm, was proposed as an efficient alternative. Motivated by the common occurrence of orthogonality constraints in tasks such as principle component analysis and training of deep neural networks, this letter studies the landing algorithm and establishes a novel linear convergence rate for smooth non-convex functions using only a local Riemannian PŁ condition. Numerical experiments demonstrate that the landing algorithm performs on par with the state-of-the-art retraction-based methods with substantially reduced computational overhead.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"2727-2732"},"PeriodicalIF":2.4,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142821293","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}

{"title":"Continuous Venous Oxygen Saturation Estimation via Population-Informed Personalized Gaussian Sum Extended Kalman Filtering","authors":"Parham Rezaei;Joseph S. Friedberg;Hosam K. Fathy;Jin-Oh Hahn","doi":"10.1109/LCSYS.2024.3514780","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3514780","url":null,"abstract":"Mixed venous oxygen saturation (SvO2) can play a pivotal role for patient monitoring and treatment in critical care and cardiopulmonary medicine. Unfortunately, its continuous measurement requires the use of invasive pulmonary artery catheters. This letter presents a novel population-informed personalized Gaussian sum extended Kalman filtering (PI-P-GSEKF) approach to continuous \u0000<inline-formula> <tex-math>${mathrm { SvO}}_{2}$ </tex-math></inline-formula>\u0000 estimation from arterial oxygen saturation (SpO2) measurement. The main challenge in \u0000<inline-formula> <tex-math>${mathrm { SvO}}_{2}$ </tex-math></inline-formula>\u0000 estimation is large inter-individual variability in the cardiopulmonary dynamics, which seriously deteriorates the efficacy of standard EKF. To cope with this challenge, we employ the GSEKF in which individual EKFs are designed using a mathematical model of cardiopulmonary dynamics whose operating points are selected from (i) population-level generative sampling (thus “population-informed”) and (ii) Markov chain Monte Carlo (MCMC) sampling based on a one-time SpO2-SvO2 measurement (thus “personalized”). Using the experimental data collected from 8 hypoxia trials in 4 large animals, we showed the ability of the PI-P-GSEKF to estimate \u0000<inline-formula> <tex-math>${mathrm { SvO}}_{2}$ </tex-math></inline-formula>\u0000 from \u0000<inline-formula> <tex-math>${mathrm { SpO}}_{2}$ </tex-math></inline-formula>\u0000 in comparison with its PI-EKF (EKF with population-level generative sampling as the source of process noise) and PI-GSEKF (GSEKF with population-level generative sampling alone) counterparts (average \u0000<inline-formula> <tex-math>${mathrm { SvO}}_{2}$ </tex-math></inline-formula>\u0000 root-mean-squared error: PI-EKF 4.7%, PI-GSEKF 4.3%, PI-P-GSEKF 3.0%). We also showed that population-level generative sampling and MCMC sampling both had respective roles in improving \u0000<inline-formula> <tex-math>${mathrm { SvO}}_{2}$ </tex-math></inline-formula>\u0000 estimation accuracy. In sum, the PI-P-GSEKF demonstrated its proof-of-principle to enable non-invasive continuous \u0000<inline-formula> <tex-math>${mathrm { SvO}}_{2}$ </tex-math></inline-formula>\u0000 estimation.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"2799-2804"},"PeriodicalIF":2.4,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142858887","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}

{"title":"Robust Parametric Shrinking Horizon Model Predictive Control and its Application to Spacecraft Rendezvous","authors":"Miguel Castroviejo-Fernandez;Michele Ambrosino;Ilya Kolmanovsky","doi":"10.1109/LCSYS.2024.3514975","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3514975","url":null,"abstract":"This letter introduces a robust Model Predictive Control approach in which a shrinking prediction horizon and a system input parameterization are exploited to control a linear system with set-bounded disturbances while satisfying state and control constraints. By exploiting input parameterization, the number of decision variables in the optimal control problem and the computational time can be reduced. The simulated spacecraft rendezvous maneuver is used to highlight the potential of the proposed approach for practical applications.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"2781-2786"},"PeriodicalIF":2.4,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142858885","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}

{"title":"A Novel Plug-and-Play Cooperative Disturbance Compensator for Heterogeneous Uncertain Linear Multi-Agent Systems","authors":"Yizhou Gong;Yang Wang","doi":"10.1109/LCSYS.2024.3514822","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3514822","url":null,"abstract":"Cooperative output regulation (COR) for multi-agent systems (MAS) has garnered significant attention due to its broad applications. This letter offers a fresh perspective on the COR problem for a class of heterogeneous, uncertain, linear SISO MAS facing two major challenges simultaneously: (1) the agents are highly uncertain and heterogeneous, and (2) communication is restricted to a directed spanning tree with only local information exchanged among agents. We propose a novel plug-and-play cooperative feedforward disturbance compensator that requires minimal prior knowledge of follower agents’ dynamics. In contrast to traditional methods, our compensator is fully distributed, adaptive, and highly robust to agent heterogeneity. It eliminates the need for system identification and handles large uncertainties without relying on typical assumptions such as minimum phase, identical dimensionality, or uniform relative degree across agents. Additionally, the compensator is designed for scalability, offering plug-and-play functionality that allows seamless addition or removal of agents without requiring controller redesign, provided the network maintains a spanning tree. Theoretical analysis and simulations demonstrate the compensator’s effectiveness in solving the COR problem across various scenarios.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"2811-2816"},"PeriodicalIF":2.4,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142858888","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}

{"title":"Data-Driven Dynamic Intervention Design in Network Games","authors":"Xiupeng Chen;Nima Monshizadeh","doi":"10.1109/LCSYS.2024.3511420","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3511420","url":null,"abstract":"Targeted interventions in games present a challenging problem due to the asymmetric information available to the regulator and the agents. This note addresses the problem of steering the actions of self-interested agents in quadratic network games towards a target action profile. A common starting point in the literature assumes prior knowledge of utility functions and/or network parameters. The goal of the results presented here is to remove this assumption and address scenarios where such a priori knowledge is unavailable. To this end, we design a data-driven dynamic intervention mechanism that relies solely on historical observations of agent actions and interventions. Additionally, we modify this mechanism to limit the amount of interventions, thereby considering budget constraints. Analytical convergence guarantees are provided for both mechanisms, and a numerical case study further demonstrates their effectiveness.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"2667-2672"},"PeriodicalIF":2.4,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142810555","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}

{"title":"Distributed Optimization for Traffic Light Control and Connected Automated Vehicle Coordination in Mixed-Traffic Intersections","authors":"Viet-Anh Le;Andreas A. Malikopoulos","doi":"10.1109/LCSYS.2024.3512332","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3512332","url":null,"abstract":"In this letter, 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 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.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"2721-2726"},"PeriodicalIF":2.4,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142821291","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}