IEEE open journal of control systems最新文献_第9页

Cross Apprenticeship Learning Framework: Properties and Solution Approaches 跨学徒制学习框架：特性与解决方法

IEEE open journal of control systems Pub Date : 2023-01-09 DOI: 10.1109/OJCSYS.2023.3235248

Ashwin Aravind;Debasish Chatterjee;Ashish Cherukuri

{"title":"Cross Apprenticeship Learning Framework: Properties and Solution Approaches","authors":"Ashwin Aravind;Debasish Chatterjee;Ashish Cherukuri","doi":"10.1109/OJCSYS.2023.3235248","DOIUrl":"https://doi.org/10.1109/OJCSYS.2023.3235248","url":null,"abstract":"Apprenticeship learning is a framework in which an agent learns a policy to perform a given task in an environment using example trajectories provided by an expert. In the real world, one might have access to expert trajectories in different environments where system dynamics is different while the learning task is the same. For such scenarios, two types of learning objectives can be defined. One where the learned policy performs very well in one specific environment and another when it performs well across all environments. To balance these two objectives in a principled way, our work presents the cross apprenticeship learning (CAL) framework. This consists of an optimization problem where an optimal policy for each environment is sought while ensuring that all policies remain close to each other. This nearness is facilitated by one tuning parameter in the optimization problem. We derive properties of the optimizers of the problem as the tuning parameter varies. We identify conditions under which an agent prefers using the policy obtained from CAL over the traditional apprenticeship learning. Since the CAL problem is nonconvex, we provide a convex outer approximation. Finally, we demonstrate the attributes of our framework in the context of a navigation task in a windy gridworld environment.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"2 ","pages":"36-48"},"PeriodicalIF":0.0,"publicationDate":"2023-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9552933/9973428/10011555.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50376168","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: 0

Modeling and Characterization of Pre-Charged Collapse-Mode CMUTs 预充电坍缩模式cmut的建模与表征

IEEE open journal of control systems Pub Date : 2023-01-01 DOI: 10.1109/OJUFFC.2023.3240699

M. Saccher, Shinnosuke Kawasaki, J. Klootwijk, R. van Schaijk, Ronald Dekker

{"title":"Modeling and Characterization of Pre-Charged Collapse-Mode CMUTs","authors":"M. Saccher, Shinnosuke Kawasaki, J. Klootwijk, R. van Schaijk, Ronald Dekker","doi":"10.1109/OJUFFC.2023.3240699","DOIUrl":"https://doi.org/10.1109/OJUFFC.2023.3240699","url":null,"abstract":"Recently, the applications of ultrasound transducers expanded from high-end diagnostic tools to point of care diagnostic devices and wireless power receivers for implantable devices. These new applications additionally require that the transducer technology must comply to biocompatibility and manufacturing scalability. In this respect, Capacitive Micromachined Ultrasound Transducers (CMUTs) have a strong advantage compared to the conventional PZT based transducers. However, current CMUTs require a large DC bias voltage for their operation, which limits the miniaturizability of these devices. In this study, we propose a pre-charged collapse-mode CMUT for immersive applications that can operate without an external bias by means of a charge trapping Al2O3 layer embedded in the dielectrics between the top and bottom electrodes. The built-in charge layer was analytically modeled and four layer stack combinations were investigated and characterized. The measurement results of the CMUTs were then used to fit the model and to quantify the amount and type of trapped charge. It was found that these devices polarize due to the ferroelectric-like behavior of the Al2O3, and the amount of charge stored in the charge-trapping layer was estimated to be approximately 0.02 C/m2. Their acoustic performance shows a transmit and receive sensitivity of 8.8 kPa/V and 13.1 V/MPa respectively. In addition, we show that increasing the charging temperature, the charging duration, and the charging voltage results in a higher amount of stored charge. Finally, results of ALT tests showed that these devices have a lifetime of more than 2.5 years at body temperature.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"3 1","pages":"14-28"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62907489","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}

引用次数: 3

Exact Decomposition of Optimal Control Problems via Simultaneous Block Diagonalization of Matrices 最优控制问题的矩阵同时块对角化的精确分解

IEEE open journal of control systems Pub Date : 2022-12-22 DOI: 10.1109/OJCSYS.2022.3231553

Amirhossein Nazerian;Kshitij Bhatta;Francesco Sorrentino

引用次数: 1

The Robotarium: A Remotely-Accessible, Multi-Robot Testbed for Control Research and Education 机器人博物馆：用于控制研究和教育的可远程访问的多机器人试验台

IEEE open journal of control systems Pub Date : 2022-12-22 DOI: 10.1109/OJCSYS.2022.3231523

Sean Wilson;Magnus Egerstedt

引用次数: 0

IEEE Open Journal of Control Systems Publication Information IEEE控制系统公开期刊出版信息

IEEE open journal of control systems Pub Date : 2022-12-02 DOI: 10.1109/OJCSYS.2022.3219740

引用次数: 0

IEEE Control Systems Society Information IEEE控制系统协会信息

IEEE open journal of control systems Pub Date : 2022-12-02 DOI: 10.1109/OJCSYS.2022.3219735

引用次数: 0

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