Pub Date : 2024-12-09DOI: 10.1109/LCSYS.2024.3513232
Alexander Benvenuti;Brendan Bialy;Miriam Dennis;Matthew Hale
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.
{"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}
Pub Date : 2024-12-09DOI: 10.1109/LCSYS.2024.3513814
Mohammad Hussein Yoosefian Nooshabadi;Rifat Sipahi;Laurent Lessard
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.
{"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}
Pub Date : 2024-12-09DOI: 10.1109/LCSYS.2024.3513813
Brooks A. Butler;Philip E. Paré
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.
{"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}
Pub Date : 2024-12-09DOI: 10.1109/LCSYS.2024.3514998
David E. J. van Wijk;Samuel Coogan;Tamas G. Molnar;Manoranjan Majji;Kerianne L. Hobbs
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.
{"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}
Pub Date : 2024-12-09DOI: 10.1109/LCSYS.2024.3514472
Eleftherios E. Vlahakis;Lars Lindemann;Pantelis Sopasakis;Dimos V. Dimarogonas
We address an optimal control problem for linear stochastic systems with unknown noise distributions and joint chance constraints using conformal prediction. Our approach involves designing a feedback controller to maintain an error system within a prediction region (PR). We define PRs as sublevel sets of a nonconformity score over error trajectories, enabling the handling of joint chance constraints. We propose two methods to design feedback control and PRs: one through direct optimization over error trajectory samples, and the other indirectly using the S-procedure with a disturbance ellipsoid obtained from data. By tightening constraints with PRs, we solve a relaxed problem to synthesize a feedback policy. Our method ensures reliable probabilistic guarantees based on marginal coverage, independent of data size.
{"title":"Conformal Prediction for Distribution-Free Optimal Control of Linear Stochastic Systems","authors":"Eleftherios E. Vlahakis;Lars Lindemann;Pantelis Sopasakis;Dimos V. Dimarogonas","doi":"10.1109/LCSYS.2024.3514472","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3514472","url":null,"abstract":"We address an optimal control problem for linear stochastic systems with unknown noise distributions and joint chance constraints using conformal prediction. Our approach involves designing a feedback controller to maintain an error system within a prediction region (PR). We define PRs as sublevel sets of a nonconformity score over error trajectories, enabling the handling of joint chance constraints. We propose two methods to design feedback control and PRs: one through direct optimization over error trajectory samples, and the other indirectly using the S-procedure with a disturbance ellipsoid obtained from data. By tightening constraints with PRs, we solve a relaxed problem to synthesize a feedback policy. Our method ensures reliable probabilistic guarantees based on marginal coverage, independent of data size.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"2835-2840"},"PeriodicalIF":2.4,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142912588","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}
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.
{"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}
Pub Date : 2024-12-09DOI: 10.1109/LCSYS.2024.3514818
Abdulrahman U. Alsaggaf;Maryam Saberi;Tyrus Berry;Donald Ebeigbe
This letter introduces a Kalman Filter framework for systems with process noise and measurements characterized by state-dependent, nonlinear conditional means and covariances. Estimating such general nonlinear models is challenging because traditional methods, such as the Extended Kalman Filter, linearize only functions – not noise – and require state-independent covariances. These limitations often necessitate Bayesian approaches that rely on specific distribution assumptions. To address these challenges, we propose a framework that employs a recursive least squares method that relies solely on conditional means and covariances, eliminating the need for explicit probability distributions. By applying first-order linearizations and incorporating targeted modifications to manage state dependence, the filter simplifies implementation, reduces computational demands, and provides a practical solution for systems that deviate from the assumptions underlying traditional Kalman filters. Simulation results on a compartmental model demonstrate performance comparable to sequential Monte Carlo methods while significantly lowering computational costs, effectively addressing real-world challenges of scalability and precision.
{"title":"Nonlinear Kalman Filtering in the Absence of Direct Functional Relationships Between Measurement and State","authors":"Abdulrahman U. Alsaggaf;Maryam Saberi;Tyrus Berry;Donald Ebeigbe","doi":"10.1109/LCSYS.2024.3514818","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3514818","url":null,"abstract":"This letter introduces a Kalman Filter framework for systems with process noise and measurements characterized by state-dependent, nonlinear conditional means and covariances. Estimating such general nonlinear models is challenging because traditional methods, such as the Extended Kalman Filter, linearize only functions – not noise – and require state-independent covariances. These limitations often necessitate Bayesian approaches that rely on specific distribution assumptions. To address these challenges, we propose a framework that employs a recursive least squares method that relies solely on conditional means and covariances, eliminating the need for explicit probability distributions. By applying first-order linearizations and incorporating targeted modifications to manage state dependence, the filter simplifies implementation, reduces computational demands, and provides a practical solution for systems that deviate from the assumptions underlying traditional Kalman filters. Simulation results on a compartmental model demonstrate performance comparable to sequential Monte Carlo methods while significantly lowering computational costs, effectively addressing real-world challenges of scalability and precision.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"2865-2870"},"PeriodicalIF":2.4,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142890180","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}
Pub Date : 2024-12-09DOI: 10.1109/LCSYS.2024.3514780
Parham Rezaei;Joseph S. Friedberg;Hosam K. Fathy;Jin-Oh Hahn
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 ${mathrm { SvO}}_{2}$ estimation from arterial oxygen saturation (SpO2) measurement. The main challenge in ${mathrm { SvO}}_{2}$ 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 ${mathrm { SvO}}_{2}$ from ${mathrm { SpO}}_{2}$ 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 ${mathrm { SvO}}_{2}$ 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 ${mathrm { SvO}}_{2}$ estimation accuracy. In sum, the PI-P-GSEKF demonstrated its proof-of-principle to enable non-invasive continuous ${mathrm { SvO}}_{2}$ estimation.
{"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}
Pub Date : 2024-12-09DOI: 10.1109/LCSYS.2024.3513896
Chongyang Shi;Shuo Han;Michael Dorothy;Jie Fu
This letter studies the synthesis of an active perception policy that maximizes the information leakage of the initial state in a stochastic system modeled as a hidden Markov model (HMM). Specifically, the emission function of the HMM is controllable with a set of perception or sensor query actions. Given the goal is to infer the initial state from partial observations in the HMM, we use Shannon conditional entropy as the planning objective and develop a novel policy gradient method with convergence guarantees. By leveraging a variant of observable operators in HMMs, we prove several important properties of the gradient of the conditional entropy with respect to the policy parameters, which allow efficient computation of the policy gradient and stable and fast convergence. We demonstrate the effectiveness of our solution by applying it to an inference problem in a stochastic grid world environment.
{"title":"Active Perception With Initial-State Uncertainty: A Policy Gradient Method","authors":"Chongyang Shi;Shuo Han;Michael Dorothy;Jie Fu","doi":"10.1109/LCSYS.2024.3513896","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3513896","url":null,"abstract":"This letter studies the synthesis of an active perception policy that maximizes the information leakage of the initial state in a stochastic system modeled as a hidden Markov model (HMM). Specifically, the emission function of the HMM is controllable with a set of perception or sensor query actions. Given the goal is to infer the initial state from partial observations in the HMM, we use Shannon conditional entropy as the planning objective and develop a novel policy gradient method with convergence guarantees. By leveraging a variant of observable operators in HMMs, we prove several important properties of the gradient of the conditional entropy with respect to the policy parameters, which allow efficient computation of the policy gradient and stable and fast convergence. We demonstrate the effectiveness of our solution by applying it to an inference problem in a stochastic grid world environment.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"3147-3152"},"PeriodicalIF":2.4,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142962864","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}
Pub Date : 2024-12-09DOI: 10.1109/LCSYS.2024.3514975
Miguel Castroviejo-Fernandez;Michele Ambrosino;Ilya Kolmanovsky
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.
{"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}
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