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Predict globally, correct locally: Parallel-in-time optimization of neural networks 全局预测，局部修正：神经网络的并行实时优化
IF 4.8 2区 计算机科学
Automatica Pub Date : 2024-10-30 DOI: 10.1016/j.automatica.2024.111976
{"title":"Predict globally, correct locally: Parallel-in-time optimization of neural networks","authors":"","doi":"10.1016/j.automatica.2024.111976","DOIUrl":"10.1016/j.automatica.2024.111976","url":null,"abstract":"<div><div>The training of neural networks can be formulated as an optimal control problem of a dynamical system. The initial conditions of the dynamical system are given by the data. The objective of the control problem is to transform the initial conditions in a form that can be easily classified or regressed using linear methods. This link between optimal control of dynamical systems and neural networks has proved beneficial both from a theoretical and from a practical point of view. Several researchers have exploited this link to investigate the stability of different neural network architectures and develop memory efficient training algorithms. In this paper, we also adopt the dynamical systems view of neural networks, but our aim is different from earlier works. Instead, we develop a novel distributed optimization algorithm. The proposed algorithm addresses the most significant obstacle for distributed algorithms for neural network optimization: the network weights cannot be updated until the forward propagation of the data, and backward propagation of the gradients are complete. Using the dynamical systems point of view, we interpret the layers of a (residual) neural network as the discretized dynamics of a dynamical system and exploit the relationship between the co-states (adjoints) of the optimal control problem and backpropagation. We then develop a parallel-in-time method that updates the parameters of the network without waiting for the forward or back propagation algorithms to complete in full. We establish the convergence of the proposed algorithm. Preliminary numerical results suggest that the algorithm is competitive and more efficient than the state-of-the-art.</div></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142552786","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Set-based value operators for non-stationary and uncertain Markov decision processes 非稳态和不确定马尔可夫决策过程的基于集合的值算子
IF 4.8 2区 计算机科学
Automatica Pub Date : 2024-10-28 DOI: 10.1016/j.automatica.2024.111970
{"title":"Set-based value operators for non-stationary and uncertain Markov decision processes","authors":"","doi":"10.1016/j.automatica.2024.111970","DOIUrl":"10.1016/j.automatica.2024.111970","url":null,"abstract":"<div><div>This paper analyzes finite-state Markov Decision Processes (MDPs) with nonstationary and uncertain parameters via set-based fixed point theory. Given compact parameter ambiguity sets, we demonstrate that a family of contraction operators, including the Bellman operator and the policy evaluation operator, can be extended to set-based contraction operators with a unique fixed point—a compact value function set. For non-stationary MDPs, we show that while the value function trajectory diverges, its Hausdorff distance from this fixed point converges to zero. In parameter uncertain MDPs, the fixed point’s extremum value functions are equivalent to the min–max value function in robust dynamic programming under the rectangularity condition. Furthermore, we show that the rectangularity condition is a sufficient condition for the fixed point to contain its own extremum value functions. Finally, we derive novel guarantees for probabilistic path planning in capricious wind fields and stratospheric station-keeping.</div></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142526131","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Nonuniqueness and convergence to equivalent solutions in observer-based inverse reinforcement learning 基于观测器的逆强化学习中的非唯一性和向等效解的收敛性
IF 4.8 2区 计算机科学
Automatica Pub Date : 2024-10-25 DOI: 10.1016/j.automatica.2024.111977
{"title":"Nonuniqueness and convergence to equivalent solutions in observer-based inverse reinforcement learning","authors":"","doi":"10.1016/j.automatica.2024.111977","DOIUrl":"10.1016/j.automatica.2024.111977","url":null,"abstract":"<div><div>A key challenge in solving the deterministic inverse reinforcement learning (IRL) problem online and in real-time is the existence of multiple solutions. Nonuniqueness necessitates the study of the notion of equivalent solutions, <em>i.e.</em>, solutions that result in a different cost functional but same feedback matrix. While <em>offline</em> algorithms that result in convergence to equivalent solutions have been developed in the literature, online, real-time techniques that address nonuniqueness are not available. In this paper, a regularized history stack observer that converges to approximately equivalent solutions of the IRL problem is developed. Novel data-richness conditions are developed to facilitate the analysis and simulation results are provided to demonstrate the effectiveness of the developed technique.</div></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142525967","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Successive over relaxation for model-free LQR control of discrete-time Markov jump systems 离散时马尔可夫跳跃系统无模型 LQR 控制的连续过度松弛
IF 4.8 2区 计算机科学
Automatica Pub Date : 2024-10-25 DOI: 10.1016/j.automatica.2024.111919
{"title":"Successive over relaxation for model-free LQR control of discrete-time Markov jump systems","authors":"","doi":"10.1016/j.automatica.2024.111919","DOIUrl":"10.1016/j.automatica.2024.111919","url":null,"abstract":"<div><div>This paper aims to solve the model-free linear quadratic regulator problem for discrete-time Markov jump linear systems without requiring an initial stabilizing control policy. We propose both model-based and model-free successive over relaxation algorithms to learn the optimal control policy of discrete-time Markov jump linear systems. The model-free value iteration algorithm is a special case of our model-free algorithm when the relaxation factor equals one. A sufficient condition on the relaxation factor is provided to guarantee the convergence of our algorithms. Moreover, it is proved that our model-free algorithm can obtain an approximate optimal solution when the transition probability matrix is unknown. Finally, a numerical example is used to illustrate our results.</div></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142525966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Asymmetrical vulnerability of heterogeneous multi-agent systems under false-data injection attacks 异构多代理系统在虚假数据注入攻击下的非对称脆弱性
IF 4.8 2区 计算机科学
Automatica Pub Date : 2024-10-22 DOI: 10.1016/j.automatica.2024.111947
{"title":"Asymmetrical vulnerability of heterogeneous multi-agent systems under false-data injection attacks","authors":"","doi":"10.1016/j.automatica.2024.111947","DOIUrl":"10.1016/j.automatica.2024.111947","url":null,"abstract":"<div><div>This paper investigates the vulnerability of heterogeneous multi-agent systems (MASs) in face of perfect false-data injection (FDI) attacks that stealthily destabilize the synchronization processes of agents. In contrast to homogeneous dynamics, heterogeneous dynamics can be asymmetrically worsened by attackers, which is a greater challenge for MAS security. First of all, the existence conditions of perfect FDI attacks in different agents are established based on the spectral radius of heterogeneous system matrices. Then, it is proven that the attack targets against communication links are determined by the characteristic space of different agents. Since the attack properties including the attack existence and targets are different in each agent, heterogeneous MASs have the asymmetrical vulnerability under perfect FDI attacks. Finally, a sufficient condition and a necessary condition are obtained to achieve perfect FDI attacks with minimum attack targets. A numerical simulation of heterogeneous MASs is presented to demonstrate the effectiveness of perfect FDI attacks.</div></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142525968","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Analysis of barrier function based adaptive sliding mode control in the presence of deterministic noise 存在确定性噪声时基于障碍函数的自适应滑模控制分析
IF 4.8 2区 计算机科学
Automatica Pub Date : 2024-10-21 DOI: 10.1016/j.automatica.2024.111946
{"title":"Analysis of barrier function based adaptive sliding mode control in the presence of deterministic noise","authors":"","doi":"10.1016/j.automatica.2024.111946","DOIUrl":"10.1016/j.automatica.2024.111946","url":null,"abstract":"<div><div>Barrier function-based adaptive sliding mode control (BFASMC) is analyzed in presence of deterministic measurement noise. It is shown that, considering only boundedness of the measurement noise, it is impossible to select the controller parameters to track some perturbation with unknown bound. Nonetheless, under the assumption of continuity of the noise, the tracking of such a perturbation is possible; however, the barrier function width depends on the bound of the noise. If Lipschitz continuity of the noise is assumed, then it follows that the width of the barrier function can be chosen arbitrarily.</div></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142525969","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Stabilization for fast sampling discrete-time singularly perturbed singular Markovian systems 快速采样离散时奇异扰动奇异马尔可夫系统的稳定问题
IF 4.8 2区 计算机科学
Automatica Pub Date : 2024-10-21 DOI: 10.1016/j.automatica.2024.111981
{"title":"Stabilization for fast sampling discrete-time singularly perturbed singular Markovian systems","authors":"","doi":"10.1016/j.automatica.2024.111981","DOIUrl":"10.1016/j.automatica.2024.111981","url":null,"abstract":"<div><div>This paper considers the problems of stabilization and <span><math><msub><mrow><mi>H</mi></mrow><mrow><mi>∞</mi></mrow></msub></math></span> control for fast sampling discrete-time singularly perturbed singular Markovian systems (SPSMSs). The system equivalent approach is initially introduced to transform the discrete fast sampling SPSMS model into the augmented SPSMS for the convenience of designing system controller. Secondly, sufficient condition on stochastically mean square admissibility is established for the fast sampling SPSMS. By separating matrix variables and singularly perturbed parameter, a state feedback controller is also provided to ensure stochastically mean square admissibility of the fast sampling augmented SPSMS. Then, the results are extended to <span><math><msub><mrow><mi>H</mi></mrow><mrow><mi>∞</mi></mrow></msub></math></span> performance analysis and controller design in the presence of the external disturbances. The derived criteria can be converted to the feasible problems based on convex optimization, and the upper bound of singular perturbation parameter is also calculated. Besides, a discretized electrical circuit system is provided to verify the effectiveness and the superiority of the proposed approach.</div></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142526134","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Approximate constrained stochastic optimal control via parameterized input inference 通过参数化输入推理实现近似受限随机最优控制
IF 4.8 2区 计算机科学
Automatica Pub Date : 2024-10-21 DOI: 10.1016/j.automatica.2024.111978
{"title":"Approximate constrained stochastic optimal control via parameterized input inference","authors":"","doi":"10.1016/j.automatica.2024.111978","DOIUrl":"10.1016/j.automatica.2024.111978","url":null,"abstract":"<div><div>Approximate methods to solve stochastic optimal control (SOC) problems have received significant interest from researchers in the past decade. Probabilistic inference approaches to SOC have been developed to solve nonlinear quadratic Gaussian problems. In this work, we propose an Expectation–Maximization (EM) based inference procedure to generate state-feedback controls for constrained SOC problems. We consider the inequality constraints for the state and controls and also the structural constraints for the controls. We employ barrier functions to address state and control constraints. We show that the expectation step leads to smoothing of the state-control pair while the maximization step on the non-zero subsets of the control parameters allows inference of structured stochastic optimal controllers. We demonstrate the effectiveness of the algorithm on unicycle obstacle avoidance and four-unicycle formation control examples. In these examples, we perform an empirical study on the parametric effect of barrier functions on the state constraint satisfaction. We also present a comparative study of smoothing algorithms on the performance of the proposed approach.</div></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142526133","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Corrigendum to “Assessment of initial-state-opacity in live and bounded labeled Petri net systems via optimization techniques” [Automatica 152 (2023) 110911] 对 "通过优化技术评估实时和有界标记 Petri 网系统中的初始状态容量 "的更正[Automatica 152 (2023) 110911]
IF 4.8 2区 计算机科学
Automatica Pub Date : 2024-10-20 DOI: 10.1016/j.automatica.2024.111958
{"title":"Corrigendum to “Assessment of initial-state-opacity in live and bounded labeled Petri net systems via optimization techniques” [Automatica 152 (2023) 110911]","authors":"","doi":"10.1016/j.automatica.2024.111958","DOIUrl":"10.1016/j.automatica.2024.111958","url":null,"abstract":"","PeriodicalId":55413,"journal":{"name":"Automatica","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2024-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142525970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Non-identifier based adaptive control of a chain of integrators and perturbations with unknown delays and parameters 具有未知延迟和参数的积分器和扰动链的非识别器自适应控制
IF 4.8 2区 计算机科学
Automatica Pub Date : 2024-10-18 DOI: 10.1016/j.automatica.2024.111969
{"title":"Non-identifier based adaptive control of a chain of integrators and perturbations with unknown delays and parameters","authors":"","doi":"10.1016/j.automatica.2024.111969","DOIUrl":"10.1016/j.automatica.2024.111969","url":null,"abstract":"<div><div>This paper solves the problem of how to control a chain of integrators with <em>unknown delays</em> in both state and input by memoryless state feedback. Inspired by the non-identifier based adaptive control scheme (Lei and Lin, 2006) as well as the recent progress in stabilizing time-delay feedforward systems with unknown parameters via dynamic state compensation (Sun and Lin, 2023), we design a memoryless universal controller (finite-dimensional) which adaptively stabilizes the time-delay integrators whose all delays are not known a priori. As a byproduct of this development, global adaptive regulation of a class of nonlinear systems with unknown delays and unknown parameters is also solved by the proposed universal control strategy.</div></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142525965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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