Pub Date : 2024-12-23DOI: 10.1109/LCSYS.2024.3521645
Augusto Bozza;Tim Martin;Graziana Cavone;Raffaele Carli;Mariagrazia Dotoli;Frank Allgöwer
This letter proposes a novel Data-Driven (DD) method for controlling unknown input-affine nonlinear systems. First, we estimate the system dynamics from noisy data offline through Subspace Identification of Nonlinear Dynamics. Then, at each time step during runtime, we exploit this estimation to deduce a feedback-linearization control law that robustly regulates all the systems consistent with the data. Notably, the control law is derived by solving a Semidefinite Programming (SDP) online. Moreover, closed-loop stability is ensured by constraining a Lyapunov function to descend in each time step using a linear-matrix-inequality representation. Unlike related DD control approaches for nonlinear systems based on SDP, our approach does not require any approximation of the nonlinear dynamics, while requiring the knowledge of a library of candidate basis functions. Finally, we validate our theoretical contributions by simulations for stabilization and tracking, outperforming another DD literature-inspired controller.
{"title":"Online Data-Driven Control of Nonlinear Systems Using Semidefinite Programming","authors":"Augusto Bozza;Tim Martin;Graziana Cavone;Raffaele Carli;Mariagrazia Dotoli;Frank Allgöwer","doi":"10.1109/LCSYS.2024.3521645","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3521645","url":null,"abstract":"This letter proposes a novel Data-Driven (DD) method for controlling unknown input-affine nonlinear systems. First, we estimate the system dynamics from noisy data offline through Subspace Identification of Nonlinear Dynamics. Then, at each time step during runtime, we exploit this estimation to deduce a feedback-linearization control law that robustly regulates all the systems consistent with the data. Notably, the control law is derived by solving a Semidefinite Programming (SDP) online. Moreover, closed-loop stability is ensured by constraining a Lyapunov function to descend in each time step using a linear-matrix-inequality representation. Unlike related DD control approaches for nonlinear systems based on SDP, our approach does not require any approximation of the nonlinear dynamics, while requiring the knowledge of a library of candidate basis functions. Finally, we validate our theoretical contributions by simulations for stabilization and tracking, outperforming another DD literature-inspired controller.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"3189-3194"},"PeriodicalIF":2.4,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10812702","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142938119","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 : 2024-12-23DOI: 10.1109/LCSYS.2024.3521188
Shunan Yin;Ayush Rai;Shaoshuai Mou
Optimal control of switched systems (OCSS) is of great importance since they have significant practical implementation. This letter aims to tackle the problem of adapting OCSS to additional objective functions. We propose an algorithm to enable a tunable OCSS to adjust its parameters dynamically with respect to an additional loss function in a bi-level framework. At the higher level, the algorithm utilizes gradient descent to minimize this additional objective function while simultaneously addressing an optimal control problem at the lower level. By differentiating the maximum principle for the optimal control of switched systems, gradient computation is achieved by solving an auxiliary initial value problem. Besides theoretical analysis, the algorithm’s effectiveness is also numerically demonstrated by optimal control problems of a hypersonic vehicle with a combined-power engine.
{"title":"Parameter Tuning for Optimal Control of Switched Systems With Applications in Hypersonic Vehicles","authors":"Shunan Yin;Ayush Rai;Shaoshuai Mou","doi":"10.1109/LCSYS.2024.3521188","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3521188","url":null,"abstract":"Optimal control of switched systems (OCSS) is of great importance since they have significant practical implementation. This letter aims to tackle the problem of adapting OCSS to additional objective functions. We propose an algorithm to enable a tunable OCSS to adjust its parameters dynamically with respect to an additional loss function in a bi-level framework. At the higher level, the algorithm utilizes gradient descent to minimize this additional objective function while simultaneously addressing an optimal control problem at the lower level. By differentiating the maximum principle for the optimal control of switched systems, gradient computation is achieved by solving an auxiliary initial value problem. Besides theoretical analysis, the algorithm’s effectiveness is also numerically demonstrated by optimal control problems of a hypersonic vehicle with a combined-power engine.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"3063-3068"},"PeriodicalIF":2.4,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142962845","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 : 2024-12-23DOI: 10.1109/LCSYS.2024.3521191
Miguel Castroviejo-Fernandez;Ilya Kolmanovsky
An approach to safe and fast online learning of constraints for a continuous-time linear system subject to linear inequality constraints is developed, assuming that the number of constraints is known and measurements of the constraint signals are available. During the identification phase, a constant reference command input is applied for the duration of an epoch and constraint measurements are collected. Based on these measurements, the set of feasible constraint parameters is refined using set-membership learning techniques. The reference command value is selected so that it minimizes the worst-case uncertainty in the parameters after one epoch while safety is ensured through the use of appropriately defined safe sets. The characterization of safe sets is shown to reduce to a finite set of linear inequality constraints. A numerical case study is reported for the proposed algorithm.
{"title":"Safe Constraint Learning for Reference Governor Implementation in Constrained Linear Systems","authors":"Miguel Castroviejo-Fernandez;Ilya Kolmanovsky","doi":"10.1109/LCSYS.2024.3521191","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3521191","url":null,"abstract":"An approach to safe and fast online learning of constraints for a continuous-time linear system subject to linear inequality constraints is developed, assuming that the number of constraints is known and measurements of the constraint signals are available. During the identification phase, a constant reference command input is applied for the duration of an epoch and constraint measurements are collected. Based on these measurements, the set of feasible constraint parameters is refined using set-membership learning techniques. The reference command value is selected so that it minimizes the worst-case uncertainty in the parameters after one epoch while safety is ensured through the use of appropriately defined safe sets. The characterization of safe sets is shown to reduce to a finite set of linear inequality constraints. A numerical case study is reported for the proposed algorithm.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"3117-3122"},"PeriodicalIF":2.4,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142962825","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 : 2024-12-23DOI: 10.1109/LCSYS.2024.3522058
Nathaniel Sisson;K. Merve Dogan
Adaptive control techniques are ubiquitous methods for controlling dynamic systems, particularly because of their ability to improve system performance in the presence of uncertainties. However, a downside to these adaptive controllers is that particular learning rates are often required to ensure system performance requirements, creating high-frequency oscillations in the control input signal. These oscillations can potentially cause the system to become unstable or to have unacceptable performance. Thus, in this letter, we introduce a low-frequency learning adaptive control architecture for a discrete dynamical system with system uncertainties. In this framework, the update law is modified to include a filtered version of the updated parameter, allowing for high-frequency content to be removed while preserving system performance requirements. Lyapunov stability analysis is provided to guarantee asymptotic tracking error convergence of the closed-loop system. The results of a numerical simulation illustrates the reduction of high-frequencies in the system response.
{"title":"Low-Frequency Learning for a Discrete Uncertain System","authors":"Nathaniel Sisson;K. Merve Dogan","doi":"10.1109/LCSYS.2024.3522058","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3522058","url":null,"abstract":"Adaptive control techniques are ubiquitous methods for controlling dynamic systems, particularly because of their ability to improve system performance in the presence of uncertainties. However, a downside to these adaptive controllers is that particular learning rates are often required to ensure system performance requirements, creating high-frequency oscillations in the control input signal. These oscillations can potentially cause the system to become unstable or to have unacceptable performance. Thus, in this letter, we introduce a low-frequency learning adaptive control architecture for a discrete dynamical system with system uncertainties. In this framework, the update law is modified to include a filtered version of the updated parameter, allowing for high-frequency content to be removed while preserving system performance requirements. Lyapunov stability analysis is provided to guarantee asymptotic tracking error convergence of the closed-loop system. The results of a numerical simulation illustrates the reduction of high-frequencies in the system response.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"3111-3116"},"PeriodicalIF":2.4,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142962830","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 : 2024-12-23DOI: 10.1109/LCSYS.2024.3521432
Poorva Shukla;Bassam Bamieh
We study phenomena where some eigenvectors of a graph Laplacian are largely confined in small subsets of the graph. These localization phenomena are similar to those generally termed Anderson Localization in the Physics literature, and are related to the complexity of the structure of large graphs in still unexplored ways. Using perturbation analysis and pseudo-spectrum analysis, we explain how the presence of localized eigenvectors gives rise to fragilities (low robustness margins) to unmodeled node or link dynamics. Our analysis is demonstrated by examples of networks with relatively low complexity, but with features that appear to induce eigenvector localization. The implications of this newly-discovered fragility phenomenon are briefly discussed.
{"title":"Localization Phenomena in Large-Scale Networked Systems: Implications for Fragility","authors":"Poorva Shukla;Bassam Bamieh","doi":"10.1109/LCSYS.2024.3521432","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3521432","url":null,"abstract":"We study phenomena where some eigenvectors of a graph Laplacian are largely confined in small subsets of the graph. These localization phenomena are similar to those generally termed Anderson Localization in the Physics literature, and are related to the complexity of the structure of large graphs in still unexplored ways. Using perturbation analysis and pseudo-spectrum analysis, we explain how the presence of localized eigenvectors gives rise to fragilities (low robustness margins) to unmodeled node or link dynamics. Our analysis is demonstrated by examples of networks with relatively low complexity, but with features that appear to induce eigenvector localization. The implications of this newly-discovered fragility phenomenon are briefly discussed.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"3087-3092"},"PeriodicalIF":2.4,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142962848","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 : 2024-12-23DOI: 10.1109/LCSYS.2024.3521360
Fat-Hy Omar Rajab;Jeff S. Shamma
A scenario-based risk-sensitive optimization framework is presented to approximate minimax solutions with high confidence. The approach involves first drawing several random samples from the maximizing variable, then solving a sample-based risk-sensitive optimization problem. This letter derives the sample complexity and the required risk-sensitivity level to ensure a specified tolerance and confidence in approximating the minimax solution. The derived sample complexity highlights the impact of the underlying probability distribution of the random samples. The framework is demonstrated through applications to zero-sum games and model predictive control for linear dynamical systems with bounded disturbances.
{"title":"Scenario-Based Risk-Sensitive Computations of Equilibria for Two-Person Zero-Sum Games","authors":"Fat-Hy Omar Rajab;Jeff S. Shamma","doi":"10.1109/LCSYS.2024.3521360","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3521360","url":null,"abstract":"A scenario-based risk-sensitive optimization framework is presented to approximate minimax solutions with high confidence. The approach involves first drawing several random samples from the maximizing variable, then solving a sample-based risk-sensitive optimization problem. This letter derives the sample complexity and the required risk-sensitivity level to ensure a specified tolerance and confidence in approximating the minimax solution. The derived sample complexity highlights the impact of the underlying probability distribution of the random samples. The framework is demonstrated through applications to zero-sum games and model predictive control for linear dynamical systems with bounded disturbances.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"3207-3212"},"PeriodicalIF":2.4,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142938055","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}
Soft robots offer a frontier in robotics with enormous potential for safe human-robot interaction and agility in uncertain environments. A stepping stone towards unlocking their potential is a control theory tailored to soft robotics, including a principled framework for gait design. We analyze the problem of optimal gait design for a soft crawling body – the crawler. The crawler is an elastic body with the control signal defined as actuation forces between segments of the body. We consider the simplest such crawler: a two-segmented body with a passive mechanical connection modeling the viscoelastic body dynamics and a symmetric control force modeling actuation between the two body segments. The model accounts for the nonlinear asymmetric friction with the ground, which together with the symmetric actuation forces enable the crawler’s locomotion. Using a describing-function analysis, we show that when the body is forced sinusoidally, the optimal actuator contraction frequency corresponds to the body’s natural frequency when operating with only passive dynamics. We then use the framework of Optimal Periodic Control (OPC) to design optimal force cycles of arbitrary waveform and the corresponding crawling gaits. We provide a hill-climbing algorithm to solve the OPC problem numerically. Our proposed methods and results inform the design of optimal forcing and gaits for more complex and multi-segmented crawling soft bodies.
{"title":"Optimal Gait Design for Nonlinear Soft Robotic Crawlers","authors":"Yenan Shen;Naomi Ehrich Leonard;Bassam Bamieh;Juncal Arbelaiz","doi":"10.1109/LCSYS.2024.3521872","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3521872","url":null,"abstract":"Soft robots offer a frontier in robotics with enormous potential for safe human-robot interaction and agility in uncertain environments. A stepping stone towards unlocking their potential is a control theory tailored to soft robotics, including a principled framework for gait design. We analyze the problem of optimal gait design for a soft crawling body – the crawler. The crawler is an elastic body with the control signal defined as actuation forces between segments of the body. We consider the simplest such crawler: a two-segmented body with a passive mechanical connection modeling the viscoelastic body dynamics and a symmetric control force modeling actuation between the two body segments. The model accounts for the nonlinear asymmetric friction with the ground, which together with the symmetric actuation forces enable the crawler’s locomotion. Using a describing-function analysis, we show that when the body is forced sinusoidally, the optimal actuator contraction frequency corresponds to the body’s natural frequency when operating with only passive dynamics. We then use the framework of Optimal Periodic Control (OPC) to design optimal force cycles of arbitrary waveform and the corresponding crawling gaits. We provide a hill-climbing algorithm to solve the OPC problem numerically. Our proposed methods and results inform the design of optimal forcing and gaits for more complex and multi-segmented crawling soft bodies.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"1-1"},"PeriodicalIF":2.4,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142937954","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 : 2024-12-23DOI: 10.1109/LCSYS.2024.3521187
Ke Wang;Di Wu;Pengyuan Li;Xu Li
In practical aero-engine control systems, saturation constraints on the control input are inherently asymmetric and the engine outputs may exceed their physical or safety limits during the operation. This letter proposes a switching anti-windup compensation for aero-engines incorporating the asymmetric input saturation and output constraint simultaneously. To tackle the asymmetric saturation, we transform the aero-engine linear model with asymmetrically saturated input into a switched system, and all its subsystems possess symmetric saturation property. Based on this transformation, a switching anti-windup compensator with multiple anti-windup gains is developed to mitigate the performance deterioration induced by the saturation constraints. Furthermore, output constraints are also incorporated into the compensation design, which prevents engine outputs from exceeding their allowable limits during the control process. The switching between different anti-windup gains in real time can fully utilize the actuator capability, and further achieving a more desirable engine performance. Finally, hardware-in-loop (HIL) testing and comparative results demonstrate its effectiveness and superiority.
{"title":"Output-Constrained Switching Anti-Windup Compensation for Aero-Engines With Asymmetric Input Saturation","authors":"Ke Wang;Di Wu;Pengyuan Li;Xu Li","doi":"10.1109/LCSYS.2024.3521187","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3521187","url":null,"abstract":"In practical aero-engine control systems, saturation constraints on the control input are inherently asymmetric and the engine outputs may exceed their physical or safety limits during the operation. This letter proposes a switching anti-windup compensation for aero-engines incorporating the asymmetric input saturation and output constraint simultaneously. To tackle the asymmetric saturation, we transform the aero-engine linear model with asymmetrically saturated input into a switched system, and all its subsystems possess symmetric saturation property. Based on this transformation, a switching anti-windup compensator with multiple anti-windup gains is developed to mitigate the performance deterioration induced by the saturation constraints. Furthermore, output constraints are also incorporated into the compensation design, which prevents engine outputs from exceeding their allowable limits during the control process. The switching between different anti-windup gains in real time can fully utilize the actuator capability, and further achieving a more desirable engine performance. Finally, hardware-in-loop (HIL) testing and comparative results demonstrate its effectiveness and superiority.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"3075-3080"},"PeriodicalIF":2.4,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142962826","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 : 2024-12-23DOI: 10.1109/LCSYS.2024.3522196
Manali Dutta;Rahul Singh
We consider a remote estimation setup, where data packets containing sensor observations are transmitted over a Gilbert-Elliot channel to a remote estimator, and design scheduling policies that minimize a risk-sensitive cost, which is equal to the expected value of the exponential of the cumulative cost incurred during a finite horizon, that is the sum of the cumulative transmission power consumed, and the cumulative squared estimation error. More specifically, consider a sensor that observes a discrete-time autoregressive Markov process, and at each time decides whether or not to transmit its observations to a remote estimator using an unreliable wireless communication channel after encoding these observations into data packets. Modeling the communication channel as a Gilbert-Elliot channel allows us to take into account the temporal correlations in its fading. We pose this dynamic optimization problem as a Markov decision process (MDP), and show that there exists an optimal policy that has a threshold structure, i.e., at each time t it transmits only when the current channel state is good, and the magnitude of the current “error” exceeds a certain threshold.
{"title":"Optimal Risk-Sensitive Scheduling Policies for Remote Estimation of Autoregressive Markov Processes","authors":"Manali Dutta;Rahul Singh","doi":"10.1109/LCSYS.2024.3522196","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3522196","url":null,"abstract":"We consider a remote estimation setup, where data packets containing sensor observations are transmitted over a Gilbert-Elliot channel to a remote estimator, and design scheduling policies that minimize a risk-sensitive cost, which is equal to the expected value of the exponential of the cumulative cost incurred during a finite horizon, that is the sum of the cumulative transmission power consumed, and the cumulative squared estimation error. More specifically, consider a sensor that observes a discrete-time autoregressive Markov process, and at each time decides whether or not to transmit its observations to a remote estimator using an unreliable wireless communication channel after encoding these observations into data packets. Modeling the communication channel as a Gilbert-Elliot channel allows us to take into account the temporal correlations in its fading. We pose this dynamic optimization problem as a Markov decision process (MDP), and show that there exists an optimal policy that has a threshold structure, i.e., at each time t it transmits only when the current channel state is good, and the magnitude of the current “error” exceeds a certain threshold.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"3099-3104"},"PeriodicalIF":2.4,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142962831","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 : 2024-12-23DOI: 10.1109/LCSYS.2024.3521673
Tianchen Liu;Kushal Chakrabarti;Nikhil Chopra
We devise a novel quasi-Newton algorithm for solving unconstrained convex optimization problems. The proposed algorithm is built on our previous framework of the iteratively preconditioned gradient-descent (IPG) algorithm. IPG utilized Richardson iteration to update a preconditioner matrix that approximates the inverse of the Hessian matrix. In this letter, we substitute the Richardson iteration with a successive over-relaxation (SOR) formulation. The convergence guarantee of the proposed algorithm and its theoretical improvement over vanilla IPG are presented. The algorithm is used in a mobile robot position estimation problem for numerical validation using a moving horizon estimation (MHE) formulation. Compared with IPG, the results demonstrate an improved performance of the proposed algorithm in terms of computational time and the number of iterations needed for convergence, matching our theoretical results.
{"title":"Novel Iteratively Preconditioned Gradient-Descent Algorithm via Successive Over-Relaxation Formulation","authors":"Tianchen Liu;Kushal Chakrabarti;Nikhil Chopra","doi":"10.1109/LCSYS.2024.3521673","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3521673","url":null,"abstract":"We devise a novel quasi-Newton algorithm for solving unconstrained convex optimization problems. The proposed algorithm is built on our previous framework of the iteratively preconditioned gradient-descent (IPG) algorithm. IPG utilized Richardson iteration to update a preconditioner matrix that approximates the inverse of the Hessian matrix. In this letter, we substitute the Richardson iteration with a successive over-relaxation (SOR) formulation. The convergence guarantee of the proposed algorithm and its theoretical improvement over vanilla IPG are presented. The algorithm is used in a mobile robot position estimation problem for numerical validation using a moving horizon estimation (MHE) formulation. Compared with IPG, the results demonstrate an improved performance of the proposed algorithm in terms of computational time and the number of iterations needed for convergence, matching our theoretical results.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"3105-3110"},"PeriodicalIF":2.4,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142962846","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：