IEEE Transactions on Control Systems Technology最新文献_第3页

OA-MPC: Occlusion-Aware MPC for Guaranteed Safe Robot Navigation With Unseen Dynamic Obstacles 具有不可见动态障碍物的机器人安全导航的闭塞感知MPC

IF 4.9 2区计算机科学

IEEE Transactions on Control Systems Technology Pub Date : 2024-12-31 DOI: 10.1109/TCST.2024.3520462

Roya Firoozi;Alexandre Mir;Gadiel Sznaier Camps;Mac Schwager

{"title":"OA-MPC: Occlusion-Aware MPC for Guaranteed Safe Robot Navigation With Unseen Dynamic Obstacles","authors":"Roya Firoozi;Alexandre Mir;Gadiel Sznaier Camps;Mac Schwager","doi":"10.1109/TCST.2024.3520462","DOIUrl":"https://doi.org/10.1109/TCST.2024.3520462","url":null,"abstract":"For safe navigation in dynamic uncertain environments, robotic systems rely on the perception and prediction of other agents. Particularly, in occluded areas, where cameras and light detection and ranging (LiDAR) give no data, the robot must be able to reason about the potential movements of invisible dynamic agents. This work presents a provably safe motion planning scheme for real-time navigation in an a priori unmapped environment, where occluded dynamic agents are present. Safety guarantees are provided based on the reachability analysis. Forward reachable sets associated with potential occluded agents, such as pedestrians, are computed and incorporated into planning. An iterative optimization-based planner is presented that alternates between two optimizations: nonlinear model predictive control (NMPC) and collision avoidance. The recursive feasibility of the MPC is guaranteed by introducing a terminal stopping constraint. The effectiveness of the proposed algorithm is demonstrated through simulation studies and hardware experiments with a TurtleBot robot equipped with a LiDAR system. The video of experimental results is also available at: <uri>https://youtu.be/OUnkB5Feyuk</uri>.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"33 3","pages":"940-951"},"PeriodicalIF":4.9,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143883417","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

Cooperative Approximate Output Regulation of Nonlinear Multiagent Systems: Theory and Application 非线性多智能体系统的协同近似输出调节：理论与应用

IF 4.9 2区计算机科学

IEEE Transactions on Control Systems Technology Pub Date : 2024-12-27 DOI: 10.1109/TCST.2024.3520461

Zhaowu Ping;Junyi Zhang;Hongwei Zhang

引用次数: 0

Switched Hybrid Control for Spacecraft Attitude Control With Flexible and Guaranteed Performance 柔性和性能保证航天器姿态控制的切换混合控制

IF 4.9 2区计算机科学

IEEE Transactions on Control Systems Technology Pub Date : 2024-12-27 DOI: 10.1109/TCST.2024.3508580

Jiakun Lei;Tao Meng;Dongyu Li;Kun Wang;Weijia Wang;Zhonghe Jin

{"title":"Switched Hybrid Control for Spacecraft Attitude Control With Flexible and Guaranteed Performance","authors":"Jiakun Lei;Tao Meng;Dongyu Li;Kun Wang;Weijia Wang;Zhonghe Jin","doi":"10.1109/TCST.2024.3508580","DOIUrl":"https://doi.org/10.1109/TCST.2024.3508580","url":null,"abstract":"This article addresses the challenge of achieving spacecraft attitude control with guaranteed performance while significantly reducing actuator activation frequency. To tackle this issue, we propose the concept of switched hybrid control and further integrate it with a modified prescribed-performance control (PPC) scheme. To enhance the robustness of the PPC control, we introduce the concept of a zeroing barrier function (ZBF). Coupled with a projection-operator-based modification dynamics, this approach assesses and adjusts the envelope in response to the risk of violating performance envelope constraints. Subsequently, a control mode switching strategy, considering the safety of the performance envelope and the system’s motion velocity, is proposed. This strategy automatically switches between intermittent and continuous control modes to select an appropriate control command execution strategy, thereby reducing actuator activation frequency under proper circumstances. Furthermore, we demonstrate the boundedness of the closed-loop system for different control modes and establish a uniform upper bound of the Lyapunov certificate throughout the entire time domain, thereby proving the overall uniformly ultimately bounded (UUB) of the system. Finally, numerical simulation results are presented to validate the effectiveness of the proposed control scheme.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"33 2","pages":"582-596"},"PeriodicalIF":4.9,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143489118","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

Reference-Adaptation Predictive Control Based on a Deep Parallel Model for Piezo-Actuated Stages 基于深度并联模型的参考自适应预测控制

IF 4.9 2区计算机科学

IEEE Transactions on Control Systems Technology Pub Date : 2024-12-25 DOI: 10.1109/TCST.2024.3518920

Fei Dong;Xinyu Wang;Qinglei Hu;Jianpeng Zhong;Keyou You

{"title":"Reference-Adaptation Predictive Control Based on a Deep Parallel Model for Piezo-Actuated Stages","authors":"Fei Dong;Xinyu Wang;Qinglei Hu;Jianpeng Zhong;Keyou You","doi":"10.1109/TCST.2024.3518920","DOIUrl":"https://doi.org/10.1109/TCST.2024.3518920","url":null,"abstract":"The intrinsic hysteresis nonlinearity of piezo-actuated stages (piezo stages) poses a significant challenge for precise trajectory tracking at high speeds. In response, we propose a deep parallel (dPara) model that effectively captures the dynamics of the piezo stage using historical voltage–displacement data over a concise time period. The dPara model, incorporating a parallel combination of a linear block and a feedforward neural network (FNN), exhibits exceptional performance with relative prediction errors ranging between 0.10% and 0.18% on sinusoidal trajectories at frequencies up to 72% of the resonance frequency of the piezo stage. By leveraging this parallel structure, we adapt the reference trajectory for a complex nonlinear model predictive control (MPC), leading to the development of the reference-adaptation MPC (RA-MPC). Furthermore, we design a coordinate ascent algorithm to solve the quadratic programming (QP) problem derived from the RA-MPC at a high frequency of 10 kHz. To assess the superiority of the proposed RA-MPC, comprehensive experiments are conducted under sinusoid, sawtooth, and staircase reference trajectories. Notably, it achieves maximum tracking errors (MTEs) ranging from 0.0263 to <inline-formula> <tex-math>$0.7136 ; mu $ </tex-math></inline-formula>m for desired speeds spanning from 40 to <inline-formula> <tex-math>$20,000 ; mu $ </tex-math></inline-formula>m/s.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"33 3","pages":"915-927"},"PeriodicalIF":4.9,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143883379","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

A Joint Analysis and Estimation Effort for Cell-to-Cell Variations in Lithium-Ion Battery Packs 锂离子电池组电池间差异的联合分析与估计

IF 4.9 2区计算机科学

IEEE Transactions on Control Systems Technology Pub Date : 2024-12-24 DOI: 10.1109/TCST.2024.3516364

Preston T. Abadie;Tania R. Jahan;Donald J. Docimo

{"title":"A Joint Analysis and Estimation Effort for Cell-to-Cell Variations in Lithium-Ion Battery Packs","authors":"Preston T. Abadie;Tania R. Jahan;Donald J. Docimo","doi":"10.1109/TCST.2024.3516364","DOIUrl":"https://doi.org/10.1109/TCST.2024.3516364","url":null,"abstract":"This article studies parameter variations in battery packs and estimation of the imbalance propagated by such heterogeneity. Battery pack use has drastically increased in several areas, ranging from personal vehicles to utility-scale power distribution. However, manufacturing tolerances allow for slight variations between battery cells, which can cause uneven current distributions and hinder pack operation. Current work in the literature studies these parameter discrepancies by analyzing their effects or estimating the imbalances, but there are scarce efforts toward combining these tenets of addressing parameter mismatch. This article presents a modeling framework conducive to both analysis and estimation, allowing for investigation of battery dynamics due to unequal parameters, providing analytical representations of the impact of cell mismatch on state and output dynamics. Furthermore, the framework facilitates the development of an online state estimator with reduced computational cost. After parameterization of 66 lithium-ion cells, the framework is used to determine the contributions of multiple types of parameter heterogeneity on output imbalances. The proposed estimator is then validated experimentally, showing how the fewer required calculations benefit estimation runtime. The results show that this estimation scheme is capable of providing estimates within 0.6% state of charge (SOC) of a baseline estimator’s error while providing over a 60% reduction in computational cost.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"33 2","pages":"760-774"},"PeriodicalIF":4.9,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10813457","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143489115","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

Continuous-Time-Constrained Model Predictive Control With a Parallel Solver 基于并行求解器的连续时间约束模型预测控制

IF 4.9 2区计算机科学

IEEE Transactions on Control Systems Technology Pub Date : 2024-12-24 DOI: 10.1109/TCST.2024.3516386

Bo Yang;Zishuo Li;Jiayun Li;Yilin Mo;Jiaqi Yan

{"title":"Continuous-Time-Constrained Model Predictive Control With a Parallel Solver","authors":"Bo Yang;Zishuo Li;Jiayun Li;Yilin Mo;Jiaqi Yan","doi":"10.1109/TCST.2024.3516386","DOIUrl":"https://doi.org/10.1109/TCST.2024.3516386","url":null,"abstract":"In this article, we address the model predictive control (MPC) problem for continuous-time linear time-invariant systems, with both state and input constraints. For computational efficiency, existing approaches typically discretize both dynamics and constraints, which potentially leads to constraint violations in between discrete-time instants. In contrast, to ensure strict constraint satisfaction, we equivalently replace the differential equations with linear mappings between state, input, and flat output, leveraging the differential flatness property of linear systems. By parameterizing the flat output with piecewise polynomials and employing Markov-Lukács theorem, the original MPC problem is then transformed into a semidefinite programming (SDP) problem, which guarantees the strict constraints satisfaction at all time. Furthermore, exploiting the fact that the proposed SDP contains numerous small-sized positive semidefinite (PSD) matrices as optimization variables, we propose a primal-dual hybrid gradient (PDHG) algorithm that can be efficiently parallelized, expediting the optimization procedure with GPU parallel computing. The simulation and experimental results demonstrate that our approach guarantees rigorous adherence to constraints at all time, and our solver exhibits superior computational speed compared to existing solvers for the proposed SDP problem.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"33 3","pages":"845-857"},"PeriodicalIF":4.9,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143883419","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

Data-Driven Closed-Loop Reachability Analysis for Nonlinear Human-in-the-Loop Systems Using Gaussian Mixture Model 基于高斯混合模型的非线性人在环系统数据驱动闭环可达性分析

IF 4.9 2区计算机科学

IEEE Transactions on Control Systems Technology Pub Date : 2024-12-24 DOI: 10.1109/TCST.2024.3518118

Joonwon Choi;Sooyung Byeon;Inseok Hwang

{"title":"Data-Driven Closed-Loop Reachability Analysis for Nonlinear Human-in-the-Loop Systems Using Gaussian Mixture Model","authors":"Joonwon Choi;Sooyung Byeon;Inseok Hwang","doi":"10.1109/TCST.2024.3518118","DOIUrl":"https://doi.org/10.1109/TCST.2024.3518118","url":null,"abstract":"This article presents data-driven algorithms to perform the reachability analysis of nonlinear human-in-the-loop (HITL) systems. Such systems require consideration of the human control policy, otherwise might result in a conservative reachable set. However, formulating the human control policy in a mathematically tractable form is challenging, and thus, it is commonly ignored or simplified in many applications. To tackle this problem, we propose Gaussian mixture model (GMM)-based data-driven algorithms that can explicitly consider the human control policy during the reachability analysis of an HITL system. The proposed algorithms learn the human control policy as a GMM using the given trajectory. Then, the control input from the human operator is predicted based on the trained GMM by leveraging the Gaussian mixture regression (GMR), thereby facilitating the closed-loop forward stochastic reachability analysis. In this article, we examine two types of human control policies, state-independent and state-dependent, and propose the respective algorithms. We also tested our proposed algorithms using the human subject experimental data and demonstrated to generate more accurate results compared with other existing algorithms.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"33 2","pages":"788-798"},"PeriodicalIF":4.9,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143489134","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

Turbidity Control in Sedimentation Columns by Direction-Dependent Models 方向依赖模型控制沉降柱浊度

IF 4.9 2区计算机科学

IEEE Transactions on Control Systems Technology Pub Date : 2024-12-23 DOI: 10.1109/TCST.2024.3512876

Jesus-Pablo Toledo-Zucco;Daniel Sbarbaro;João Manoel Gomes da Silva

引用次数: 0

Quadrotor Fleet Autonomous Navigation: Fusing Virtual Points Control and Nonlinear Potential Fields 四旋翼机群自主导航：融合虚拟点控制和非线性势场

IF 4.9 2区计算机科学

IEEE Transactions on Control Systems Technology Pub Date : 2024-12-23 DOI: 10.1109/TCST.2024.3517232

Hernán Abaunza;Pedro Castillo;Sergey V. Drakunov

引用次数: 0

Reinforcement Learning-Based Evolving Flight Controller for Fixed-Wing Uncrewed Aircraft 基于强化学习的固定翼无人飞机演化飞行控制器

IF 4.9 2区计算机科学

IEEE Transactions on Control Systems Technology Pub Date : 2024-12-19 DOI: 10.1109/TCST.2024.3516383

Daksh Shukla;Hady Benyamen;Shawn Keshmiri;Nicole M. Beckage

{"title":"Reinforcement Learning-Based Evolving Flight Controller for Fixed-Wing Uncrewed Aircraft","authors":"Daksh Shukla;Hady Benyamen;Shawn Keshmiri;Nicole M. Beckage","doi":"10.1109/TCST.2024.3516383","DOIUrl":"https://doi.org/10.1109/TCST.2024.3516383","url":null,"abstract":"A significant challenge in designing flight controllers lies in their dependency on the quality of dynamic models. This research explores the potential of artificial intelligence-based flight controllers to generalize control actions around policies rather than relying solely on the accuracy of dynamic models. An engineering-level, low-fidelity, linearized model of fixed-wing uncrewed aircraft is used to train a multi-input multi-output (MIMO) flight controller, employing the deep deterministic policy gradients (DDPG) algorithm, to maintain cruise velocity and altitude. While existing literature often concentrates on simulation-based assessments of reinforcement learning (RL)-based flight controllers, this research employs an extensive flight test campaign including 15 flight tests to explore the reliability, robustness, and generalization capability of RL algorithms in tasks they were not specifically trained for, such as changing cruise altitude and velocity. The RL controller outperformed a well-tuned linear quadratic regulator (LQR) on several control tasks. Furthermore, a modification in the DDPG algorithm is presented to enhance the ability of RL controllers to evolve through experience gained from actual flights. The evolved controllers present different behavior compared to the original controller. Comparative flight tests underscored the crucial role of the ratio of actual flight data to the number of simulation-based training instances in optimizing the evolved controllers.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"33 3","pages":"872-886"},"PeriodicalIF":4.9,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143883309","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