Proceedings of the ... American Control Conference. American Control Conference最新文献

Model Predictive Path Integral Control of I²RIS Robot Using RBF Identifier and Extended Kalman Filter. 基于RBF辨识和扩展卡尔曼滤波的I2RIS机器人模型预测路径积分控制。

Proceedings of the ... American Control Conference. American Control Conference Pub Date : 2025-07-01 Epub Date: 2025-08-21 DOI: 10.23919/acc63710.2025.11107661

Mojtaba Esfandiari, Pengyuan Du, Haochen Wei, Peter Gehlbach, Adnan Munawar, Peter Kazanzides, Iulian Iordachita

{"title":"Model Predictive Path Integral Control of I2RIS Robot Using RBF Identifier and Extended Kalman Filter.","authors":"Mojtaba Esfandiari, Pengyuan Du, Haochen Wei, Peter Gehlbach, Adnan Munawar, Peter Kazanzides, Iulian Iordachita","doi":"10.23919/acc63710.2025.11107661","DOIUrl":"https://doi.org/10.23919/acc63710.2025.11107661","url":null,"abstract":"Modeling and controlling cable-driven snake robots is a challenging problem due to nonlinear mechanical properties such as hysteresis, variable stiffness, and unknown friction between the actuation cables and the robot body. This challenge is more significant for snake robots in ophthalmic surgery applications, such as the Improved Integrated Robotic Intraocular Snake (I2RIS), given its small size and lack of embedded sensory feedback. Data-driven models take advantage of global function approximations, reducing complicated analytical models' challenge and computational costs. However, their performance might deteriorate in case of new data unseen in the training phase. Therefore, adding an adaptation mechanism might improve these models' performance during snake robots' interactions with unknown environments. In this work, we applied a model predictive path integral (MPPI) controller on a data-driven model of the I2RIS based on the Gaussian mixture model (GMM) and Gaussian mixture regression (GMR). To analyze the performance of the MPPI in unseen robot-tissue interaction situations, unknown external disturbances and environmental loads are simulated and added to the GMM-GMR model. These uncertainties of the robot model are then identified online using a radial basis function (RBF) whose weights are updated using an extended Kalman filter (EKF). Simulation results demonstrated the robustness of the optimal control solutions of the MPPI algorithm and its computational superiority over a conventional model predictive control (MPC) algorithm.","PeriodicalId":74510,"journal":{"name":"Proceedings of the ... American Control Conference. American Control Conference","volume":"2025 ","pages":"3341-3347"},"PeriodicalIF":0.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12372988/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144981846","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

Data-Driven Superstabilization of Linear Systems under Quantization. 量化下线性系统的数据驱动超镇定。

Proceedings of the ... American Control Conference. American Control Conference Pub Date : 2024-09-01 DOI: 10.23919/acc60939.2024.10644558

Jared Miller, Jian Zheng, Mario Sznaier, Chris Hixenbaugh

引用次数: 0

Closed-Loop Multimodal Neuromodulation of Vagus Nerve for Control of Heart Rate. 用于控制心率的迷走神经闭环多模态神经调控技术

Proceedings of the ... American Control Conference. American Control Conference Pub Date : 2024-07-01 Epub Date: 2024-09-05 DOI: 10.23919/acc60939.2024.10644421

Shane A Bender, David B Green, Kevin L Kilgore, Niloy Bhadra, Jeffery L Ardell, Tina L Vrabec

{"title":"Closed-Loop Multimodal Neuromodulation of Vagus Nerve for Control of Heart Rate.","authors":"Shane A Bender, David B Green, Kevin L Kilgore, Niloy Bhadra, Jeffery L Ardell, Tina L Vrabec","doi":"10.23919/acc60939.2024.10644421","DOIUrl":"10.23919/acc60939.2024.10644421","url":null,"abstract":"The use of electrical current to modulate neurons for autonomic regulation requires the ability to both up-regulate and down-regulate the nervous system. An implanted system employing this electrical neuromodulation would also need to adapt to changes in autonomic state in real-time. Stimulation of autonomic nerves at frequencies in the range 1-30 Hz has been a well-established technique for increasing neural activity. Vagus nerve stimulation (VNS) has been shown to be sensitive to frequency adjustments, which can be used to more precisely control the effect as compared to amplitude modulation. Kilohertz frequency alternating current (KHFAC) is a proven technique for blocking action potential conduction to reduce neural activity. Additionally, KHFAC can be reliably modulated by simple amplitude modulation. Although there are many types of commonly used closed-loop controllers, many conventional methods do not respond well to long system delays or discontinuities. Fuzzy logic control (FLC) is a state-based controller that can describe the discontinuities of the system linguistically and then translate the state transition to a continuous output signal. In our preparation, a single bipolar electrode was placed on the vagus nerve and controlled by a fuzzy logic controller to deliver both stimulation and KHFAC to control heart rate. The FLC was able to both change the heart rate to selected values and maintain the heart rate at a constant value in response to a physiological perturbation.","PeriodicalId":74510,"journal":{"name":"Proceedings of the ... American Control Conference. American Control Conference","volume":"2024 ","pages":"4536-4541"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11407065/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142302990","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

System Identification and Hybrid Model Predictive Control in Personalized mHealth Interventions for Physical Activity. 身体活动个性化mHealth干预中的系统识别和混合模型预测控制。

Proceedings of the ... American Control Conference. American Control Conference Pub Date : 2023-05-01 Epub Date: 2023-07-03 DOI: 10.23919/acc55779.2023.10156652

Mohamed El Mistiri, Owais Khan, Daniel E Rivera, Eric Hekler

{"title":"System Identification and Hybrid Model Predictive Control in Personalized mHealth Interventions for Physical Activity.","authors":"Mohamed El Mistiri, Owais Khan, Daniel E Rivera, Eric Hekler","doi":"10.23919/acc55779.2023.10156652","DOIUrl":"10.23919/acc55779.2023.10156652","url":null,"abstract":"The application of control systems principles in behavioral medicine includes developing interventions that can be individualized to promote healthy behaviors, such as sustained engagement in adequate levels of physical activity (PA). This paper presents the use of system identification and control engineering methods in the design of behavioral interventions through the novel formalism of a control-optimization trial (COT). The multiple stages of a COT, from experimental design in system identification through controller implementation, are illustrated using participant data from Just Walk, an intervention to promote walking behavior in sedentary adults. ARX models for individual participants are estimated using multiple estimation and validation data combinations, with the model leading to the best performance over a weighted norm being selected. This model serves as the internal model in a hybrid MPC controller formulated with three degree-of-freedom (3DoF) tuning that properly balances the requirements of physical activity interventions. Its performance in a realistic closed-loop setting is evaluated via simulation. These results serve as proof of concept for the COT approach, which is currently being evaluated with human participants in the clinical trial YourMove.","PeriodicalId":74510,"journal":{"name":"Proceedings of the ... American Control Conference. American Control Conference","volume":"2023 ","pages":"2240-2245"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10327579/pdf/nihms-1897910.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9865773","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

Reinforcement Learning Data-Acquiring for Causal Inference of Regulatory Networks. 监管网络因果推断的强化学习数据获取。

Proceedings of the ... American Control Conference. American Control Conference Pub Date : 2023-05-01 Epub Date: 2023-07-03 DOI: 10.23919/acc55779.2023.10155867

Mohammad Alali, Mahdi Imani

{"title":"Reinforcement Learning Data-Acquiring for Causal Inference of Regulatory Networks.","authors":"Mohammad Alali, Mahdi Imani","doi":"10.23919/acc55779.2023.10155867","DOIUrl":"10.23919/acc55779.2023.10155867","url":null,"abstract":"Gene regulatory networks (GRNs) consist of multiple interacting genes whose activities govern various cellular processes. The limitations in genomics data and the complexity of the interactions between components often pose huge uncertainties in the models of these biological systems. Meanwhile, inferring/estimating the interactions between components of the GRNs using data acquired from the normal condition of these biological systems is a challenging or, in some cases, an impossible task. Perturbation is a well-known genomics approach that aims to excite targeted components to gather useful data from these systems. This paper models GRNs using the Boolean network with perturbation, where the network uncertainty appears in terms of unknown interactions between genes. Unlike the existing heuristics and greedy data-acquiring methods, this paper provides an optimal Bayesian formulation of the data-acquiring process in the reinforcement learning context, where the actions are perturbations, and the reward measures step-wise improvement in the inference accuracy. We develop a semi-gradient reinforcement learning method with function approximation for learning near-optimal data-acquiring policy. The obtained policy yields near-exact Bayesian optimality with respect to the entire uncertainty in the regulatory network model, and allows learning the policy offline through planning. We demonstrate the performance of the proposed framework using the well-known p53-Mdm2 negative feedback loop gene regulatory network.","PeriodicalId":74510,"journal":{"name":"Proceedings of the ... American Control Conference. American Control Conference","volume":"2023 ","pages":"3957-3964"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10382224/pdf/nihms-1914206.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9922030","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

Integral Quadratic Constraints with Infinite-Dimensional Channels. 具有无限维通道的积分二次约束。

Proceedings of the ... American Control Conference. American Control Conference Pub Date : 2023-05-01 Epub Date: 2023-07-03 DOI: 10.23919/acc55779.2023.10156335

Aleksandr Talitckii, Matthew M Peet, Peter Seiler

{"title":"Integral Quadratic Constraints with Infinite-Dimensional Channels.","authors":"Aleksandr Talitckii, Matthew M Peet, Peter Seiler","doi":"10.23919/acc55779.2023.10156335","DOIUrl":"10.23919/acc55779.2023.10156335","url":null,"abstract":"Modern control theory provides us with a spectrum of methods for studying the interconnection of dynamic systems using input-output properties of the interconnected subsystems. Perhaps the most advanced framework for such input-output analysis is the use of Integral Quadratic Constraints (IQCs), which considers the interconnection of a nominal linear system with an unmodelled nonlinear or uncertain subsystem with known input-output properties. Although these methods are widely used for Ordinary Differential Equations (ODEs), there have been fewer attempts to extend IQCs to infinite-dimensional systems. In this paper, we present an IQC-based framework for Partial Differential Equations (PDEs) and Delay Differential Equations (DDEs). First, we introduce infinite-dimensional signal spaces, operators, and feedback interconnections. Next, in the main result, we propose a formulation of hard IQC-based input-output stability conditions, allowing for infinite-dimensional multipliers. We then show how to test hard IQC conditions with infinite-dimensional multipliers on a nominal linear PDE or DDE system via the Partial Integral Equation (PIE) state-space representation using a sufficient version of the Kalman-Yakubovich-Popov lemma (KYP). The results are then illustrated using four example problems with uncertainty and nonlinearity.","PeriodicalId":74510,"journal":{"name":"Proceedings of the ... American Control Conference. American Control Conference","volume":"2023 ","pages":"1576-1583"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10387136/pdf/nihms-1917830.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9922032","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

Idiographic Dynamic Modeling for Behavioral Interventions with Mixed Data Partitioning and Discrete Simultaneous Perturbation Stochastic Approximation. 具有混合数据划分和离散同时扰动随机近似的行为干预的特征动态建模。

Proceedings of the ... American Control Conference. American Control Conference Pub Date : 2023-05-01 Epub Date: 2023-07-03 DOI: 10.23919/acc55779.2023.10156304

Rachael T Kha, Daniel E Rivera, Predrag Klasnja, Eric Hekler

{"title":"Idiographic Dynamic Modeling for Behavioral Interventions with Mixed Data Partitioning and Discrete Simultaneous Perturbation Stochastic Approximation.","authors":"Rachael T Kha, Daniel E Rivera, Predrag Klasnja, Eric Hekler","doi":"10.23919/acc55779.2023.10156304","DOIUrl":"10.23919/acc55779.2023.10156304","url":null,"abstract":"This paper presents the use of discrete simultaneous perturbation stochastic approximation (DSPSA) as a routine method to efficiently determine features and parameters of idiographic (i.e. single subject) dynamic models for personalized behavioral interventions using various partitions of estimation and validation data. DSPSA is demonstrated as a valuable method to search over model features and regressor orders of AutoRegressive with eXogenous input estimated models using participant data from Just Walk (a behavioral intervention to promote physical activity in sedentary adults); results of DSPSA are compared to those of exhaustive search. In Just Walk, DSPSA efficiently and quickly estimates models of walking behavior, which can then be used to develop control systems to optimize the impacts of behavioral interventions. The use of DSPSA to evaluate models using various partitions of individual data into estimation and validation data sets also highlights data partitioning as an important feature of idiographic modeling that should be carefully considered.","PeriodicalId":74510,"journal":{"name":"Proceedings of the ... American Control Conference. American Control Conference","volume":"2023 ","pages":"283-288"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10327578/pdf/nihms-1897912.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9811443","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

Estimation of Locomotive Adhesion Coefficients and Slip Ratios 机车附着系数和滑移率的估计

Proceedings of the ... American Control Conference. American Control Conference Pub Date : 2023-01-01 DOI: 10.23919/ACC55779.2023.10155953

C. V. V. D. Merwe, J. D. L. Roux

引用次数: 0

Optimal Energy Shaping Control for a Backdrivable Hip Exoskeleton. 可反向驱动髋关节外骨骼的最佳能量成形控制。

Proceedings of the ... American Control Conference. American Control Conference Pub Date : 2023-01-01 Epub Date: 2023-07-03 DOI: 10.23919/acc55779.2023.10155839

Jiefu Zhang, Jianping Lin, Vamsi Peddinti, Robert D Gregg

{"title":"Optimal Energy Shaping Control for a Backdrivable Hip Exoskeleton.","authors":"Jiefu Zhang, Jianping Lin, Vamsi Peddinti, Robert D Gregg","doi":"10.23919/acc55779.2023.10155839","DOIUrl":"10.23919/acc55779.2023.10155839","url":null,"abstract":"Task-dependent controllers widely used in exoskeletons track predefined trajectories, which overly constrain the volitional motion of individuals with remnant voluntary mobility. Energy shaping, on the other hand, provides task-invariant assistance by altering the human body's dynamic characteristics in the closed loop. While human-exoskeleton systems are often modeled using Euler-Lagrange equations, in our previous work we modeled the system as a port-controlled-Hamiltonian system, and a task-invariant controller was designed for a knee-ankle exoskeleton using interconnection-damping assignment passivity-based control. In this paper, we extend this framework to design a controller for a backdrivable hip exoskeleton to assist multiple tasks. A set of basis functions that contains information of kinematics is selected and corresponding coefficients are optimized, which allows the controller to provide torque that fits normative human torque for different activities of daily life. Human-subject experiments with two able-bodied subjects demonstrated the controller's capability to reduce muscle effort across different tasks.","PeriodicalId":74510,"journal":{"name":"Proceedings of the ... American Control Conference. American Control Conference","volume":"2023 ","pages":"2065-2070"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10544752/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41179828","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

Model Predictive Control Strategies for Optimized mHealth Interventions for Physical Activity. 优化移动医疗体育锻炼干预的模型预测控制策略。

Proceedings of the ... American Control Conference. American Control Conference Pub Date : 2022-06-01 Epub Date: 2022-09-05 DOI: 10.23919/acc53348.2022.9867350

Mohamed El Mistiri, Daniel E Rivera, Predrag Klasnja, Junghwan Park, Eric Hekler

{"title":"Model Predictive Control Strategies for Optimized mHealth Interventions for Physical Activity.","authors":"Mohamed El Mistiri, Daniel E Rivera, Predrag Klasnja, Junghwan Park, Eric Hekler","doi":"10.23919/acc53348.2022.9867350","DOIUrl":"10.23919/acc53348.2022.9867350","url":null,"abstract":"Many individuals fail to engage in sufficient physical activity (PA), despite its well-known health benefits. This paper examines Model Predictive Control (MPC) as a means to deliver optimized, personalized behavioral interventions to improve PA, as reflected by the number of steps walked per day. Using a health behavior fluid analogy model representing Social Cognitive Theory, a series of diverse strategies are evaluated in simulated scenarios that provide insights into the most effective means for implementing MPC in PA behavioral interventions. The interplay of measurement, information, and decision is explored, with the results illustrating MPC's potential to deliver feasible, personalized, and user-friendly behavioral interventions, even under circumstances involving limited measurements. Our analysis demonstrates the effectiveness of sensibly formulated constrained MPC controllers for optimizing PA interventions, which is a preliminary though essential step to experimental evaluation of constrained MPC control strategies under real-life conditions.","PeriodicalId":74510,"journal":{"name":"Proceedings of the ... American Control Conference. American Control Conference","volume":" ","pages":"1392-1397"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9555804/pdf/nihms-1804961.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"33512233","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