IEEE open journal of control systems最新文献_第10页

Velocity Estimation of Robot Manipulators: An Experimental Comparison 机器人速度估计的实验比较

IEEE open journal of control systems Pub Date : 2022-11-16 DOI: 10.1109/OJCSYS.2022.3222753

Stefan B. Liu;Andrea Giusti;Matthias Althoff

引用次数: 3

Convex Neural Network-Based Cost Modifications for Learning Model Predictive Control 基于凸神经网络的学习模型预测控制成本修正

IEEE open journal of control systems Pub Date : 2022-11-10 DOI: 10.1109/OJCSYS.2022.3221063

Katrine Seel;Arash Bahari Kordabad;Sébastien Gros;Jan Tommy Gravdahl

{"title":"Convex Neural Network-Based Cost Modifications for Learning Model Predictive Control","authors":"Katrine Seel;Arash Bahari Kordabad;Sébastien Gros;Jan Tommy Gravdahl","doi":"10.1109/OJCSYS.2022.3221063","DOIUrl":"https://doi.org/10.1109/OJCSYS.2022.3221063","url":null,"abstract":"Developing model predictive control (MPC) schemes can be challenging for systems where an accurate model is not available, or too costly to develop. With the increasing availability of data and tools to treat them, learning-based MPC has of late attracted wide attention. It has recently been shown that adapting not only the MPC model, but also its cost function is conducive to achieving optimal closed-loop performance when an accurate model cannot be provided. In the learning context, this modification can be performed via parametrizing the MPC cost and adjusting the parameters via, e.g., reinforcement learning (RL). In this framework, simple cost parametrizations can be effective, but the underlying theory suggests that rich parametrizations in principle can be useful. In this paper, we propose such a cost parametrization using a class of neural networks (NNs) that preserves convexity. This choice avoids creating difficulties when solving the MPC problem via sensitivity-based solvers. In addition, this choice of cost parametrization ensures nominal stability of the resulting MPC scheme. Moreover, we detail how this choice can be applied to economic MPC problems where the cost function is generic and therefore does not necessarily fulfill any specific property.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"1 ","pages":"366-379"},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9552933/9683993/09944720.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50237542","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}

引用次数: 7

Learning Discrete-Time Uncertain Nonlinear Systems With Probabilistic Safety and Stability Constraints 具有概率安全和稳定性约束的离散时间不确定非线性系统的学习

IEEE open journal of control systems Pub Date : 2022-10-21 DOI: 10.1109/OJCSYS.2022.3216545

Iman Salehi;Tyler Taplin;Ashwin P. Dani

{"title":"Learning Discrete-Time Uncertain Nonlinear Systems With Probabilistic Safety and Stability Constraints","authors":"Iman Salehi;Tyler Taplin;Ashwin P. Dani","doi":"10.1109/OJCSYS.2022.3216545","DOIUrl":"https://doi.org/10.1109/OJCSYS.2022.3216545","url":null,"abstract":"This paper presents a discrete-time dynamical system model learning method from demonstration while providing probabilistic guarantees on the safety and stability of the learned model. The controlled dynamic model of a discrete-time system with a zero-mean Gaussian process noise is approximated using an Extreme Learning Machine (ELM) whose parameters are learned subject to chance constraints derived using a discrete-time control barrier function and discrete-time control Lyapunov function in the presence of the ELM reconstruction error. To estimate the ELM parameters a quadratically constrained quadratic program (QCQP) is developed subject to the constraints that are only required to be evaluated at sampled points. Simulations validate that the system model learned using the proposed method can reproduce the demonstrations inside a prescribed safe set while converging to the desired goal location starting from various different initial conditions inside the safe set. Furthermore, it is shown that the learned model can adapt to changes in goal location during reproductions without violating the stability and safety constraints.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"1 ","pages":"354-365"},"PeriodicalIF":0.0,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9552933/9683993/09926168.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50237541","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}

引用次数: 2

Mode Reduction for Markov Jump Systems 马尔可夫跳跃系统的模式约简

IEEE open journal of control systems Pub Date : 2022-10-10 DOI: 10.1109/OJCSYS.2022.3212613

Zhe Du;Laura Balzano;Necmiye Ozay

引用次数: 0

Distributed Anytime-Feasible Resource Allocation Subject to Heterogeneous Time-Varying Delays 异构时变时滞下的分布式随时可行资源分配

IEEE open journal of control systems Pub Date : 2022-09-28 DOI: 10.1109/OJCSYS.2022.3210453

Mohammadreza Doostmohammadian;Alireza Aghasi;Apostolos I. Rikos;Andreas Grammenos;Evangelia Kalyvianaki;Christoforos N. Hadjicostis;Karl H. Johansson;Themistoklis Charalambous

{"title":"Distributed Anytime-Feasible Resource Allocation Subject to Heterogeneous Time-Varying Delays","authors":"Mohammadreza Doostmohammadian;Alireza Aghasi;Apostolos I. Rikos;Andreas Grammenos;Evangelia Kalyvianaki;Christoforos N. Hadjicostis;Karl H. Johansson;Themistoklis Charalambous","doi":"10.1109/OJCSYS.2022.3210453","DOIUrl":"https://doi.org/10.1109/OJCSYS.2022.3210453","url":null,"abstract":"This paper considers distributed allocation strategies, formulated as a distributed sum-preserving (fixed-sum) allocation of resources over a multi-agent network in the presence of heterogeneous arbitrary time-varying delays. We propose a double time-scale scenario for unknown delays and a faster single time-scale scenario for known delays. Further, the links among the nodes are considered subject to certain nonlinearities (e.g, quantization and saturation/clipping). We discuss different models for nonlinearities and how they may affect the convergence, sum-preserving feasibility constraint, and solution optimality over general weight-balanced uniformly strongly connected networks and, further, time-delayed undirected networks. Our proposed scheme works in a variety of applications with general non-quadratic strongly-convex smooth objective functions. The non-quadratic part, for example, can be due to additive convex penalty or barrier functions to address the local box constraints. The network can change over time, is not necessarily connected at all times, but is only assumed to be uniformly-connected. The novelty of this work is to address all-time feasible Laplacian gradient solutions in presence of nonlinearities, switching digraph topology (not necessarily all-time connected), and heterogeneous time-varying delays.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"1 ","pages":"255-267"},"PeriodicalIF":0.0,"publicationDate":"2022-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9552933/9683993/09904851.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50348955","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}

引用次数: 6

Reinforcement Learning With Safety and Stability Guarantees During Exploration For Linear Systems 线性系统探索过程中具有安全性和稳定性保证的强化学习

IEEE open journal of control systems Pub Date : 2022-09-28 DOI: 10.1109/OJCSYS.2022.3209945

Zahra Marvi;Bahare Kiumarsi

{"title":"Reinforcement Learning With Safety and Stability Guarantees During Exploration For Linear Systems","authors":"Zahra Marvi;Bahare Kiumarsi","doi":"10.1109/OJCSYS.2022.3209945","DOIUrl":"https://doi.org/10.1109/OJCSYS.2022.3209945","url":null,"abstract":"The satisfaction of the safety and stability properties of reinforcement learning (RL) algorithms has been a long-standing challenge. These properties must be satisfied even during learning, for which exploration is required to collect rich data. However, satisfying the safety of actions when little is known about the system dynamics is a daunting challenge. After all, predicting the consequence of RL actions requires knowing the system dynamics. This paper presents a novel RL scheme that ensures the safety and stability of the linear systems during the exploration and exploitation phases. To do so, a fast and data-efficient model-learning with the convergence guarantee is employed along and simultaneously with an off-policy RL scheme to find the optimal controller. The accurate bound of the model-learning error is derived and its characteristic is employed in the formation of a novel adaptive robustified control barrier function (ARCBF) which guarantees that states of the system remain in the safe set even when the learning is incomplete. Therefore, after satisfaction of a mild rank condition, the noisy input in the exploratory data collection phase and the optimal controller in the exploitation phase are minimally altered such that the ARCBF criterion is satisfied and, therefore, safety is guaranteed in both phases. It is shown that under the proposed RL framework, the model learning error is a vanishing perturbation to the original system. Therefore, a stability guarantee is also provided even in the exploration when noisy random inputs are applied to the system.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"1 ","pages":"322-334"},"PeriodicalIF":0.0,"publicationDate":"2022-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9552933/9683993/09904857.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50237539","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}

引用次数: 1

Real Time Li-Ion Battery Bank Parameters Estimation via Universal Adaptive Stabilization 基于通用自适应稳定的实时锂电池组参数估计

IEEE open journal of control systems Pub Date : 2022-09-15 DOI: 10.1109/OJCSYS.2022.3206710

Shayok Mukhopadhyay;Hafiz M. Usman;Habibur Rehman

引用次数: 5

Differentially Private Algorithms for Statistical Verification of Cyber-Physical Systems 用于网络物理系统统计验证的差分私有算法

IEEE open journal of control systems Pub Date : 2022-09-15 DOI: 10.1109/OJCSYS.2022.3207108

Yu Wang;Hussein Sibai;Mark Yen;Sayan Mitra;Geir E. Dullerud

{"title":"Differentially Private Algorithms for Statistical Verification of Cyber-Physical Systems","authors":"Yu Wang;Hussein Sibai;Mark Yen;Sayan Mitra;Geir E. Dullerud","doi":"10.1109/OJCSYS.2022.3207108","DOIUrl":"https://doi.org/10.1109/OJCSYS.2022.3207108","url":null,"abstract":"Statistical model checking is a class of sequential algorithms that can verify specifications of interest on an ensemble of cyber-physical systems (e.g., whether 99% of cars from a batch meet a requirement on their functionality). These algorithms infer the probability that given specifications are satisfied by the systems with provable statistical guarantees by drawing sufficient numbers of independent and identically distributed samples. During the process of statistical model checking, the values of the samples (e.g., a user's car trajectory) may be inferred by intruders, causing privacy concerns in consumer-level applications (e.g., automobiles and medical devices). This paper addresses the privacy of statistical model checking algorithms from the point of view of differential privacy. These algorithms are sequential, drawing samples until a condition on their values is met. We show that revealing the number of samples drawn can violate privacy. We also show that the standard exponential mechanism that randomizes the output of an algorithm to achieve differential privacy fails to do so in the context of sequential algorithms. Instead, we relax the conservative requirement in differential privacy that the sensitivity of the output of the algorithm should be bounded to any perturbation for any data set. We propose a new notion of differential privacy which we call \u0000<italic>expected differential privacy</i>\u0000 (EDP). Then, we propose a novel expected sensitivity analysis for the sequential algorithm and propose a corresponding exponential mechanism that randomizes the termination time to achieve the EDP. We apply the proposed exponential mechanism to statistical model checking algorithms to preserve the privacy of the samples they draw. The utility of the proposed algorithm is demonstrated in a case study.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"1 ","pages":"294-305"},"PeriodicalIF":0.0,"publicationDate":"2022-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9552933/9683993/09893303.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50381126","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}

引用次数: 3

Online Optimization of Dynamical Systems With Deep Learning Perception 具有深度学习感知的动态系统在线优化

IEEE open journal of control systems Pub Date : 2022-09-14 DOI: 10.1109/OJCSYS.2022.3205871

Liliaokeawawa Cothren;Gianluca Bianchin;Emiliano Dall'Anese

{"title":"Online Optimization of Dynamical Systems With Deep Learning Perception","authors":"Liliaokeawawa Cothren;Gianluca Bianchin;Emiliano Dall'Anese","doi":"10.1109/OJCSYS.2022.3205871","DOIUrl":"https://doi.org/10.1109/OJCSYS.2022.3205871","url":null,"abstract":"This paper considers the problem of controlling a dynamical system when the state cannot be directly measured and the control performance metrics are unknown or only partially known. In particular, we focus on the design of data-driven controllers to regulate a dynamical system to the solution of a constrained convex optimization problem where: i) the state must be estimated from nonlinear and possibly high-dimensional data; and ii) the cost of the optimization problem – which models control objectives associated with inputs and states of the system – is not available and must be learned from data. We propose a data-driven feedback controller that is based on adaptations of a projected gradient-flow method; the controller includes neural networks as integral components for the estimation of the unknown functions. Leveraging stability theory for perturbed systems, we derive sufficient conditions to guarantee exponential input-to-state stability (ISS) of the control loop. In particular, we show that the interconnected system is ISS with respect to the approximation errors of the neural network and unknown disturbances affecting the system. The transient bounds combine the universal approximation property of deep neural networks with the ISS characterization. Illustrative numerical results are presented in the context of robotics and control of epidemics.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"1 ","pages":"306-321"},"PeriodicalIF":0.0,"publicationDate":"2022-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9552933/9683993/09891838.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50348787","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}

引用次数: 5

Neural Network Optimal Feedback Control With Guaranteed Local Stability 具有保证局部稳定性的神经网络最优反馈控制

IEEE open journal of control systems Pub Date : 2022-09-12 DOI: 10.1109/OJCSYS.2022.3205863

Tenavi Nakamura-Zimmerer;Qi Gong;Wei Kang

{"title":"Neural Network Optimal Feedback Control With Guaranteed Local Stability","authors":"Tenavi Nakamura-Zimmerer;Qi Gong;Wei Kang","doi":"10.1109/OJCSYS.2022.3205863","DOIUrl":"https://doi.org/10.1109/OJCSYS.2022.3205863","url":null,"abstract":"Recent research shows that supervised learning can be an effective tool for designing near-optimal feedback controllers for high-dimensional nonlinear dynamic systems. But the behavior of neural network controllers is still not well understood. In particular, some neural networks with high test accuracy can fail to even locally stabilize the dynamic system. To address this challenge we propose several novel neural network architectures, which we show guarantee local asymptotic stability while retaining the approximation capacity to learn the optimal feedback policy semi-globally. The proposed architectures are compared against standard neural network feedback controllers through numerical simulations of two high-dimensional nonlinear optimal control problems: stabilization of an unstable Burgers-type partial differential equation, and altitude and course tracking for an unmanned aerial vehicle. The simulations demonstrate that standard neural networks can fail to stabilize the dynamics even when trained well, while the proposed architectures are always at least locally stabilizing and can achieve near-optimal performance.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"1 ","pages":"210-222"},"PeriodicalIF":0.0,"publicationDate":"2022-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9552933/9683993/09887885.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50348954","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}

引用次数: 3