IEEE Transactions on Control Systems Technology最新文献_第7页

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

Robust Inversion-Based Feedforward Control With Hybrid Modeling for Feed Drives 基于混合建模的鲁棒逆前馈控制

IF 4.9 2区计算机科学

IEEE Transactions on Control Systems Technology Pub Date : 2024-12-13 DOI: 10.1109/TCST.2024.3512862

Haijia Xu;Christoph Hinze;Andrea Iannelli;Alexander Verl

{"title":"Robust Inversion-Based Feedforward Control With Hybrid Modeling for Feed Drives","authors":"Haijia Xu;Christoph Hinze;Andrea Iannelli;Alexander Verl","doi":"10.1109/TCST.2024.3512862","DOIUrl":"https://doi.org/10.1109/TCST.2024.3512862","url":null,"abstract":"This article presents a robust feedforward design approach using hybrid modeling to improve the output tracking performance of feed drives. Geared toward the use for feedforward design, the hybrid model represents the dominant linear dynamics with a flat analytical model and captures the output nonlinearity by Gaussian process (GP) regression. The feedforward control is based on the model inversion, and the design procedure is formulated as a signal-based robust control problem, considering multiple performance objectives of tracking, disturbance rejection, and input reduction under uncertainties. In addition, the technique of structured <inline-formula> <tex-math>$mu $ </tex-math></inline-formula> synthesis is applied, which allows direct robust tuning of the fixed-structure feedforward gains and ensures the applicability in industrial hardware. The proposed methodological approach covers the entire procedure from modeling to control architecture selection and weights design, delivering an end-to-end strategy that accounts for performance and robustness requirements. Validated on an industrial milling machine with real-time capability, the proposed robust controller reduces the mean absolute tracking error in the transient phase by 83% and 63% compared to the industrial standard baseline feedforward and the nominal design, respectively. Even with a variation of 20% in the model parameters, the robust feedforward still reduces the error by 58% in the worst case with respect to the baseline.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"33 3","pages":"858-871"},"PeriodicalIF":4.9,"publicationDate":"2024-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10798993","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143883378","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

Explicit Spacecraft Thruster Control Allocation With Minimum Impulse Bit 基于最小脉冲位的显式航天器推进器控制分配

IF 4.9 2区计算机科学

IEEE Transactions on Control Systems Technology Pub Date : 2024-12-13 DOI: 10.1109/TCST.2024.3511266

Afonso Botelho;Paulo Rosa;João M. Lemos

{"title":"Explicit Spacecraft Thruster Control Allocation With Minimum Impulse Bit","authors":"Afonso Botelho;Paulo Rosa;João M. Lemos","doi":"10.1109/TCST.2024.3511266","DOIUrl":"https://doi.org/10.1109/TCST.2024.3511266","url":null,"abstract":"Thruster control allocation (TCA) is a key functionality for many spacecraft, with a significant impact on control performance, propellant consumption, and fault tolerance. Propellant-optimal solutions are desirable and are either based on onboard numerical optimization, or explicit optimization via the use of offline-generated look-up tables (LUTs). This article proposes a TCA and modulation method of the latter type by using multiparametric programming and presents a novel fast LUT evaluation algorithm. Fault tolerance and the handling of non-attainable control commands with full controllability exploitation are also addressed. Furthermore, the solution is extended to include the non-convex minimum impulse bit (MIB) constraint, where the proposed solution can find the global optimum. The use of this constraint is demonstrated in a close-range orbital rendezvous scenario, yielding significant improvements to the performance of boosts, forced motions, and station-keeping maneuvers, at the cost of greater propellant consumption and computation time. Results in consumer hardware for a12-thruster configuration show a worst case onboard computation time of <inline-formula> <tex-math>$7~mu $ </tex-math></inline-formula>s and 0.5 ms for the cases without and with the MIB constraint, which are up to two orders of magnitude lower than those for numerical optimization with a state-of-the-art optimizer. The proposed onboard algorithms are simple, non-iterative, and have worst case computational effort guarantees.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"33 3","pages":"833-844"},"PeriodicalIF":4.9,"publicationDate":"2024-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143883421","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