IEEE Transactions on Control Systems Technology最新文献_第9页

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

Control of Periodically Waked Wind Turbines 周期性尾迹风力发电机的控制

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.2024.3508577

Aemilius A. W. van Vondelen;Atindriyo K. Pamososuryo;Sachin T. Navalkar;Jan-Willem van Wingerden

{"title":"Control of Periodically Waked Wind Turbines","authors":"Aemilius A. W. van Vondelen;Atindriyo K. Pamososuryo;Sachin T. Navalkar;Jan-Willem van Wingerden","doi":"10.1109/TCST.2024.3508577","DOIUrl":"https://doi.org/10.1109/TCST.2024.3508577","url":null,"abstract":"Periodic wakes are created on upstream wind turbines by pitching strategies, such as the Helix approach, to enhance wake mixing and thereby increase power production for wind turbines directly in their wake. Consequently, a cyclic load is not only generated on the actuating turbine’s blades but also on the waked wind turbine. While the upstream load is the result of the pitching required for wake mixing, the downstream load originates from interaction with the periodic wake and only causes fatigue damage. This study proposes two novel individual pitch control schemes in which such a periodic load on the downstream turbine can be treated: by attenuation or amplification. The former method improves the fatigue life of the downstream turbine, whereas the latter enhances wake mixing further downstream by exploiting the already-present periodic content in the wake; both were validated on a three-turbine wind farm in high-fidelity large-eddy simulations. Fatigue damage reductions of around 10% were found in the load mitigation case, while an additional power enhancement of 6% was generated on the third turbine when implementing the amplification strategy. Both objectives can easily be toggled depending on a wind farm operator’s demands and the desired loads/energy capture tradeoff.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"33 2","pages":"700-713"},"PeriodicalIF":4.9,"publicationDate":"2024-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10798995","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143489119","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

An Optimization-Based Dynamic Reordering Heuristic for Coordination of Vehicles in Mixed Traffic Intersections 基于优化的混合交通交叉口车辆协调动态重排序启发式算法

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.2024.3508542

Muhammad Faris;Mario Zanon;Paolo Falcone

{"title":"An Optimization-Based Dynamic Reordering Heuristic for Coordination of Vehicles in Mixed Traffic Intersections","authors":"Muhammad Faris;Mario Zanon;Paolo Falcone","doi":"10.1109/TCST.2024.3508542","DOIUrl":"https://doi.org/10.1109/TCST.2024.3508542","url":null,"abstract":"In this article, we address a coordination problem for connected and autonomous vehicles (CAVs) in mixed traffic settings with human-driven vehicles (HDVs). The main objective is to have a safe and optimal crossing order for vehicles approaching unsignalized intersections. This problem results in a mixed-integer quadratic programming (MIQP) formulation, which is unsuitable for real-time applications. Therefore, we propose a computationally tractable optimization-based heuristic that monitors platoons of CAVs and HDVs to evaluate whether alternative crossing orders can perform better. It first checks the future constraint violation that consistently occurs between pairs of platoons to determine a potential swap. Next, the costs of quadratic programming (QP) formulations associated with the current and alternative orders are compared in a depth-first branching fashion. In simulations, we show that our heuristic can be a hundred times faster than the original and simplified MIQPs (SMIQPs) and yields solutions that are close to optimal and have better order consistency.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"33 4","pages":"1387-1402"},"PeriodicalIF":4.9,"publicationDate":"2024-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10799096","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144501944","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

Fast Zone Model Predictive Control for High Capacity Battery Subpack of Electric Vehicle 电动汽车大容量电池子组快速区域模型预测控制

IF 4.9 2区计算机科学

IEEE Transactions on Control Systems Technology Pub Date : 2024-12-11 DOI: 10.1109/TCST.2024.3508579

Changbeom Hong;Sanghoon Shin;Hyeonwoo Cho;Daeki Hong;Se-Kyu Oh;Yeonsoo Kim

{"title":"Fast Zone Model Predictive Control for High Capacity Battery Subpack of Electric Vehicle","authors":"Changbeom Hong;Sanghoon Shin;Hyeonwoo Cho;Daeki Hong;Se-Kyu Oh;Yeonsoo Kim","doi":"10.1109/TCST.2024.3508579","DOIUrl":"https://doi.org/10.1109/TCST.2024.3508579","url":null,"abstract":"As the demand for electric vehicles (EVs) increases, battery thermal management is required to guarantee safety and improve driving performance. The batteries need to be operated within an appropriate temperature range while minimizing energy consumption. We propose a fast zone model predictive control (MPC), which determines the optimal flow rate and inlet temperature of the coolant to control the temperatures of 48 cells in subpack. When the battery temperatures are within the proper temperature range, the proposed zone MPC focuses on minimizing power consumption while maintaining the temperature within the zone. When the temperature is outside the zone, the set-point MPC with terminal cost is used to determine the optimal input sequence. To achieve computational efficiency, a control-oriented battery thermal model is first established considering the temperature distribution of all cells. Second, the zone MPC formulation is converted into quadratic programming (QP). The nondifferentiable objective function of zone nonlinear MPC (NMPC) is approximated with a soft-plus function, and then, new variables are introduced to convert the nonlinear objective function into the quadratic form. Finally, the nonlinear dynamics are handled with the successive linearization method, which leads to QP formulation. By applying the proposed MPC, total energy consumption for cooling and heating cases under 1C-rate discharge was reduced by 27.11% and 78.73%, respectively, compared with the set-point MPC.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"33 2","pages":"729-743"},"PeriodicalIF":4.9,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143489085","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

Resilient Microgrid Scheduling With Synthetic Inertia From Electric Vehicles Within a Network of Charging Stations 充电站网络中考虑电动汽车综合惯性的弹性微电网调度

IF 4.9 2区计算机科学

IEEE Transactions on Control Systems Technology Pub Date : 2024-12-11 DOI: 10.1109/TCST.2024.3512432

Yixun Wen;Zhongda Chu;Amber Srivastava;Fei Teng;Boli Chen

{"title":"Resilient Microgrid Scheduling With Synthetic Inertia From Electric Vehicles Within a Network of Charging Stations","authors":"Yixun Wen;Zhongda Chu;Amber Srivastava;Fei Teng;Boli Chen","doi":"10.1109/TCST.2024.3512432","DOIUrl":"https://doi.org/10.1109/TCST.2024.3512432","url":null,"abstract":"Vehicle-to-grid technologies are proposed as potential providers of virtual inertia for microgrids (MGs). This article addresses an energy and charging scheduling problem for an MG and investigates how to utilize a network of electric vehicle (EV) charging stations (CSs) to provide sufficient virtual initial for frequency regulation that guarantees the safe transition of MG to the islanded operation during extreme events. The charging behavior of EV within a CS network is complex and can be actively influenced by charge point power and tariff set up by the CS network operator subject to MG operation requirements. A novel modeling framework is proposed to capture these aspects and integrate them into the MG energy management. The goal is to determine the optimal power allocation among distributed energy resources within an MG, minimizing operation costs while ensuring sufficient frequency support with virtual inertia contribution from EVs. To deal with inevitable uncertainties associated with EV arrivals at a CS, we employ joint distributionally robust chance constraints (DRCCs) to mitigate the impact of uncertainty and enhance the robustness of the algorithm. These joint DRCCs are decomposed into individual ones via an optimized Bonferroni approximation (BoA) method, then suitably relaxed into convex forms, which maintains the solvability of the overall problem. The effectiveness of the method is validated with case studies based on a modified IEEE 14-bus system.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"33 2","pages":"775-787"},"PeriodicalIF":4.9,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143489189","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