Pub Date : 2024-07-09DOI: 10.1109/TCST.2024.3405668
Wanwan Zhu;Yukai Yang;Bailing Tian;Qun Zong
In this brief, a global finite-time adaptive attitude control algorithm is investigated for flexible spacecraft with slosh structure under model uncertainty, external disturbance, and actuator fault. First, the arctangent function (AF) surface is designed to realize the faster convergence time of quaternion-descripted attitude control system. Then, based on AF, an adaptive fault-tolerant control (FTC) algorithm is proposed to realize global finite-time attitude control of coupled spacecraft. The merit of the designed control algorithm is that it assumes that the upper bound of the lumped uncertainty (including model uncertainty related to angular velocity) is less than the combination of a positive constant, angular velocity, and control torque, rather than just less than a positive constant. The Lyapunov technique is used to prove the stability of whole closed-loop system. Numerical examples are performed to prove the efficiency of the designed control algorithm.
本文研究了一种全局有限时间自适应姿态控制算法，适用于在模型不确定性、外部扰动和致动器故障情况下具有滑移结构的柔性航天器。首先，设计了反正切函数（AF）曲面，以实现四元数描述姿态控制系统更快的收敛时间。然后，在 AF 的基础上提出了一种自适应容错控制（FTC）算法，以实现耦合航天器的全局有限时间姿态控制。所设计的控制算法的优点在于，它假定集合不确定性（包括与角速度相关的模型不确定性）的上界小于正常数、角速度和控制力矩的组合，而不仅仅是小于正常数。利用 Lyapunov 技术证明了整个闭环系统的稳定性。通过数值示例证明了所设计控制算法的效率。
{"title":"Global Finite-Time Adaptive Attitude Control for Coupled Spacecraft With Model Uncertainty and Actuator Faults","authors":"Wanwan Zhu;Yukai Yang;Bailing Tian;Qun Zong","doi":"10.1109/TCST.2024.3405668","DOIUrl":"10.1109/TCST.2024.3405668","url":null,"abstract":"In this brief, a global finite-time adaptive attitude control algorithm is investigated for flexible spacecraft with slosh structure under model uncertainty, external disturbance, and actuator fault. First, the arctangent function (AF) surface is designed to realize the faster convergence time of quaternion-descripted attitude control system. Then, based on AF, an adaptive fault-tolerant control (FTC) algorithm is proposed to realize global finite-time attitude control of coupled spacecraft. The merit of the designed control algorithm is that it assumes that the upper bound of the lumped uncertainty (including model uncertainty related to angular velocity) is less than the combination of a positive constant, angular velocity, and control torque, rather than just less than a positive constant. The Lyapunov technique is used to prove the stability of whole closed-loop system. Numerical examples are performed to prove the efficiency of the designed control algorithm.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"32 6","pages":"2428-2435"},"PeriodicalIF":4.9,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141568395","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}
Pub Date : 2024-07-03DOI: 10.1109/tcst.2024.3417068
Stefan Kojchev, Robert Hult, Maximilian Kneissl, Jonas Fredriksson
{"title":"Optimization-Based Coordination of Automated and Human-Driven Vehicles in Confined Sites","authors":"Stefan Kojchev, Robert Hult, Maximilian Kneissl, Jonas Fredriksson","doi":"10.1109/tcst.2024.3417068","DOIUrl":"https://doi.org/10.1109/tcst.2024.3417068","url":null,"abstract":"","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"35 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141549798","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}
Pub Date : 2024-07-02DOI: 10.1109/TCST.2024.3420012
Wei Yu;Deqing Huang;Xiao-Lei Wang;Hairong Dong
Inspired by the challenges in improving train efficiency, this study investigates the consensus tracking of multiple high-speed trains (MHSTs) under random denial-of-service (DoS) attacks. First, a linearization method is used to linearize the nonlinear dynamic model of MHSTs with external running resistance. Second, the strategy of DoS attackers is elaborated on by using random variables obeying Bernoulli distributions. After establishing a data recovery mechanism, a security model-free adaptive control (MFAC) scheme is presented for MHSTs that is fully data-driven and independent of any model information or structural data of train groups. The effectiveness of this approach is theoretically analyzed without requiring accurate system modeling. Finally, the validity of MFAC and the impact of DoS attacks on MHSTs are evaluated through simulations involving G1868 HSTs between Guiyang North and Kaili South.
受提高列车效率所面临挑战的启发，本研究探讨了在随机拒绝服务（DoS）攻击下多列高速列车（MHST）的一致跟踪问题。首先，采用线性化方法将具有外部运行阻力的 MHST 非线性动态模型线性化。其次，利用服从伯努利分布的随机变量阐述了 DoS 攻击者的策略。在建立数据恢复机制后，提出了一种针对 MHST 的安全无模型自适应控制（MFAC）方案，该方案完全由数据驱动，与列车组的任何模型信息或结构数据无关。理论上分析了这种方法的有效性，而无需精确的系统建模。最后，通过模拟贵阳北至凯里南的G1868次高铁，评估了MFAC的有效性以及DoS攻击对MHST的影响。
{"title":"Data-Driven Security Consensus Tracking of Multiple High-Speed Trains Under Random Topologies With Data Recovery Mechanism","authors":"Wei Yu;Deqing Huang;Xiao-Lei Wang;Hairong Dong","doi":"10.1109/TCST.2024.3420012","DOIUrl":"10.1109/TCST.2024.3420012","url":null,"abstract":"Inspired by the challenges in improving train efficiency, this study investigates the consensus tracking of multiple high-speed trains (MHSTs) under random denial-of-service (DoS) attacks. First, a linearization method is used to linearize the nonlinear dynamic model of MHSTs with external running resistance. Second, the strategy of DoS attackers is elaborated on by using random variables obeying Bernoulli distributions. After establishing a data recovery mechanism, a security model-free adaptive control (MFAC) scheme is presented for MHSTs that is fully data-driven and independent of any model information or structural data of train groups. The effectiveness of this approach is theoretically analyzed without requiring accurate system modeling. Finally, the validity of MFAC and the impact of DoS attacks on MHSTs are evaluated through simulations involving G1868 HSTs between Guiyang North and Kaili South.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"32 6","pages":"2298-2309"},"PeriodicalIF":4.9,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141526711","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}
Pub Date : 2024-07-02DOI: 10.1109/TCST.2024.3415377
Y. M. H. Xiao;H. Wang;Y. Pan
In this work, we develop a time-optimal path planning algorithm for a mobile robot constrained by a minimum turning radius in an environment cluttered with an arbitrary number of moving and deforming obstacles. The algorithm builds on our previous work and involves substantial extensions to handle the turning radius constraint by adding the heading angle of the robot to the state space in addition to its location in a 2-D plane. The developed planner involves two stages: 1) forward propagation of the reachable set in the state space to preset destination through a newly derived variational inequality (VI) which encodes the obstacle avoidance and 2) backtracking to obtain the waypoints of the optimal path (corresponding to optimal control of the turning rate and speed), solved through an ODE-based scheme or a new and more robust backward-set-based scheme. The planned path represents a rigorous global optimal solution (except numerical errors) to the problem that can be used as a benchmark for other simplified planners or implemented together with a receding horizon for path planning with limited perception ability. We demonstrate both applications in several test cases.
{"title":"Reachability-Based Planning of Time-Optimal Curvature-Constrained Path With Moving and Deforming Obstacles","authors":"Y. M. H. Xiao;H. Wang;Y. Pan","doi":"10.1109/TCST.2024.3415377","DOIUrl":"10.1109/TCST.2024.3415377","url":null,"abstract":"In this work, we develop a time-optimal path planning algorithm for a mobile robot constrained by a minimum turning radius in an environment cluttered with an arbitrary number of moving and deforming obstacles. The algorithm builds on our previous work and involves substantial extensions to handle the turning radius constraint by adding the heading angle of the robot to the state space in addition to its location in a 2-D plane. The developed planner involves two stages: 1) forward propagation of the reachable set in the state space to preset destination through a newly derived variational inequality (VI) which encodes the obstacle avoidance and 2) backtracking to obtain the waypoints of the optimal path (corresponding to optimal control of the turning rate and speed), solved through an ODE-based scheme or a new and more robust backward-set-based scheme. The planned path represents a rigorous global optimal solution (except numerical errors) to the problem that can be used as a benchmark for other simplified planners or implemented together with a receding horizon for path planning with limited perception ability. We demonstrate both applications in several test cases.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"32 6","pages":"2240-2252"},"PeriodicalIF":4.9,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141526712","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}
Pub Date : 2024-06-26DOI: 10.1109/TCST.2024.3415236
Geraldo Silveira;Luiz Mirisola;Pascal Morin
This brief investigates the problem of vision-based robot control where the equilibrium is defined via a reference image. Specifically, this work considers the class of intensity-based nonmetric solutions, which provide for high accuracy, versatility, and robustness. The existing general techniques within that class present either a fully coupled control error dynamics or at best only achieve decoupling of the translational part, i.e., they can only obtain a triangular system in the general case. These couplings in the system dynamics increase analysis complexity and may degrade system performance. This work proposes a new nonlinear observer-based strategy for completely decoupling the translational and rotational parts, i.e., to obtain a diagonal system in the general case. A Lyapunov-based analysis of local stability and convergence, as well as proofs of diffeomorphism and of that decoupling property are provided. Improved performances are also experimentally confirmed using a camera-mounted six-degree-of-freedom (DoF) robotic manipulator in a challenging setup. In particular, execution times are drastically reduced by using the proposed diagonal technique.
{"title":"A Nonlinear Observer Approach to Diagonally Decoupled Direct Visual Servo Control","authors":"Geraldo Silveira;Luiz Mirisola;Pascal Morin","doi":"10.1109/TCST.2024.3415236","DOIUrl":"10.1109/TCST.2024.3415236","url":null,"abstract":"This brief investigates the problem of vision-based robot control where the equilibrium is defined via a reference image. Specifically, this work considers the class of intensity-based nonmetric solutions, which provide for high accuracy, versatility, and robustness. The existing general techniques within that class present either a fully coupled control error dynamics or at best only achieve decoupling of the translational part, i.e., they can only obtain a triangular system in the general case. These couplings in the system dynamics increase analysis complexity and may degrade system performance. This work proposes a new nonlinear observer-based strategy for completely decoupling the translational and rotational parts, i.e., to obtain a diagonal system in the general case. A Lyapunov-based analysis of local stability and convergence, as well as proofs of diffeomorphism and of that decoupling property are provided. Improved performances are also experimentally confirmed using a camera-mounted six-degree-of-freedom (DoF) robotic manipulator in a challenging setup. In particular, execution times are drastically reduced by using the proposed diagonal technique.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"32 6","pages":"2460-2467"},"PeriodicalIF":4.9,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141503706","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}
Pub Date : 2024-06-25DOI: 10.1109/TCST.2024.3416416
Zheng Tian;Xinming Wang;Jun Yang;Shihua Li;Dan Niu;Qi Li
Crane systems generally operate in challenging environments (e.g., harsh weather conditions and high-altitude work), which heightens the requirements of control systems for the safety and disturbances rejection. However, underactuated nature poses difficulties in achieving the efficient positioning and swing elimination under these factors. To this end, we propose a method using a quadratic program (QP) formulation that combines an enhanced-coupling control Lyapunov function (ECCLF) with a new composite state control barrier function (CSCBF). Additionally, disturbance observers (DOBs) are employed to handle matched and unmatched disturbances effectively. The ECCLF introduces a new coupled control variable, where its tracking error ultimately exhibits an exponential convergence, elegantly overcoming the inability of full-state feedback linearization in underactuated systems. The CSCBF imposes time-varying safety constraints on the unilateral swing distance (USD), ensuring swing safety and meeting industrial payload positioning accuracy requirements. Especially, the traditional control barrier function (CBF) approach is not applicable for the proposed problem due to the infeasibility when the control coefficient of the CBF tends to zero, which is addressed by the proposed CSCBF approach. The safety of the CSCBF and the effectiveness of the controller synthesis are rigorously proven. Experimental validation demonstrates the effectiveness, safety, and disturbance rejection performance under practical working conditions.
{"title":"Safety-Critical Disturbance Rejection Control of Overhead Crane Systems: Methods and Experimental Validation","authors":"Zheng Tian;Xinming Wang;Jun Yang;Shihua Li;Dan Niu;Qi Li","doi":"10.1109/TCST.2024.3416416","DOIUrl":"10.1109/TCST.2024.3416416","url":null,"abstract":"Crane systems generally operate in challenging environments (e.g., harsh weather conditions and high-altitude work), which heightens the requirements of control systems for the safety and disturbances rejection. However, underactuated nature poses difficulties in achieving the efficient positioning and swing elimination under these factors. To this end, we propose a method using a quadratic program (QP) formulation that combines an enhanced-coupling control Lyapunov function (ECCLF) with a new composite state control barrier function (CSCBF). Additionally, disturbance observers (DOBs) are employed to handle matched and unmatched disturbances effectively. The ECCLF introduces a new coupled control variable, where its tracking error ultimately exhibits an exponential convergence, elegantly overcoming the inability of full-state feedback linearization in underactuated systems. The CSCBF imposes time-varying safety constraints on the unilateral swing distance (USD), ensuring swing safety and meeting industrial payload positioning accuracy requirements. Especially, the traditional control barrier function (CBF) approach is not applicable for the proposed problem due to the infeasibility when the control coefficient of the CBF tends to zero, which is addressed by the proposed CSCBF approach. The safety of the CSCBF and the effectiveness of the controller synthesis are rigorously proven. Experimental validation demonstrates the effectiveness, safety, and disturbance rejection performance under practical working conditions.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"32 6","pages":"2253-2266"},"PeriodicalIF":4.9,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141503707","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}
Pub Date : 2024-06-18DOI: 10.1109/TCST.2024.3412529
Josef Matouš;Claudio Paliotta;Kristin Ytterstad Pettersen;Damiano Varagnolo
This article extends the hand position concept to underactuated underwater vehicles. Compared with previous works that utilize this concept, our approach works on six degrees-of-freedom (DOFs) vehicles and does not introduce singularities. By choosing the hand position as the output of the controlled system, we can apply output feedback linearization to simplify the dynamics of the vehicle. Specifically, we can then transform the six DOFs nonlinear underactuated vehicle model into a double integrator. This transformation enables the use of numerous control strategies that could otherwise not be used on nonholonomic or underactuated vehicles. After defining the concept, we analyze the closed-loop behavior of a general hand position-based controller. Specifically, we analyze the effects of external disturbances on the hand position and derive the sufficient conditions under which the rotational dynamics of the AUV remain bounded. Next, we present two examples of hand position-based controllers for solving the trajectory-tracking and path-following problems and analyze their closed-loop behavior. The theoretical results are verified both in numerical simulations and experiments.
{"title":"The Hand Position Concept for Control of Underactuated Underwater Vehicles","authors":"Josef Matouš;Claudio Paliotta;Kristin Ytterstad Pettersen;Damiano Varagnolo","doi":"10.1109/TCST.2024.3412529","DOIUrl":"10.1109/TCST.2024.3412529","url":null,"abstract":"This article extends the hand position concept to underactuated underwater vehicles. Compared with previous works that utilize this concept, our approach works on six degrees-of-freedom (DOFs) vehicles and does not introduce singularities. By choosing the hand position as the output of the controlled system, we can apply output feedback linearization to simplify the dynamics of the vehicle. Specifically, we can then transform the six DOFs nonlinear underactuated vehicle model into a double integrator. This transformation enables the use of numerous control strategies that could otherwise not be used on nonholonomic or underactuated vehicles. After defining the concept, we analyze the closed-loop behavior of a general hand position-based controller. Specifically, we analyze the effects of external disturbances on the hand position and derive the sufficient conditions under which the rotational dynamics of the AUV remain bounded. Next, we present two examples of hand position-based controllers for solving the trajectory-tracking and path-following problems and analyze their closed-loop behavior. The theoretical results are verified both in numerical simulations and experiments.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"32 6","pages":"2223-2239"},"PeriodicalIF":4.9,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141939881","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}
Pub Date : 2024-06-18DOI: 10.1109/TCST.2024.3410128
Jared Town;Zachary Morrison;Rushikesh Kamalapurkar
The focus of this brief is behavior modeling for pilots of unmanned aerial systems. The pilot is assumed to make decisions that optimize an unknown cost functional. The cost functional is estimated from observed trajectories using a novel inverse reinforcement learning (IRL) framework. The resulting IRL problem often admits multiple solutions. In this brief, a recently developed IRL observer is adapted to the pilot behavior modeling problem. The observer is shown to converge to one of the equivalent solutions of the corresponding IRL problem. The developed technique is implemented on a quadcopter where the pilot is a surrogate linear-quadratic controller that generates velocity commands for set-point regulation of the quadcopter. Experimental results demonstrate the ability of the developed method to learn equivalent cost functionals.
{"title":"Pilot Performance Modeling via Observer-Based Inverse Reinforcement Learning","authors":"Jared Town;Zachary Morrison;Rushikesh Kamalapurkar","doi":"10.1109/TCST.2024.3410128","DOIUrl":"10.1109/TCST.2024.3410128","url":null,"abstract":"The focus of this brief is behavior modeling for pilots of unmanned aerial systems. The pilot is assumed to make decisions that optimize an unknown cost functional. The cost functional is estimated from observed trajectories using a novel inverse reinforcement learning (IRL) framework. The resulting IRL problem often admits multiple solutions. In this brief, a recently developed IRL observer is adapted to the pilot behavior modeling problem. The observer is shown to converge to one of the equivalent solutions of the corresponding IRL problem. The developed technique is implemented on a quadcopter where the pilot is a surrogate linear-quadratic controller that generates velocity commands for set-point regulation of the quadcopter. Experimental results demonstrate the ability of the developed method to learn equivalent cost functionals.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"32 6","pages":"2444-2451"},"PeriodicalIF":4.9,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141939883","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}
Pub Date : 2024-06-13DOI: 10.1109/TCST.2024.3410138
Tongjia Zheng;Zhenyuan Yuan;Mollik Nayyar;Alan R. Wagner;Minghui Zhu;Hai Lin
Emergency evacuation describes a complex situation involving time-critical decision-making by evacuees. Mobile robots are being actively explored as a potential solution to provide timely guidance. This work studies a robot-guided crowd evacuation problem where a small group of robots is used to guide a large human crowd to safe locations. The challenge lies in how to use microlevel human-robot interactions to indirectly influence a population that significantly outnumbers the robots to achieve the collective evacuation objective. To address the challenge, we follow a two-scale modeling strategy and explore hydrodynamic models, which consist of a family of microscopic social force models that describe how human movements are locally affected by other humans, the environment, and robots, and associated macroscopic equations for the temporal and spatial evolution of the crowd density and flow velocity. We design controllers for the robots, such that they not only automatically explore the environment (with unknown dynamic obstacles) to cover it as much as possible, but also dynamically adjust the directions of their local navigation force fields based on the real-time macrostates of the crowd to guide the crowd to a safe location. We prove the stability of the proposed evacuation algorithm and conduct extensive simulations to investigate the performance of the algorithm with different combinations of human numbers, robot numbers, and obstacle settings.
{"title":"Multirobot-Guided Crowd Evacuation: Two-Scale Modeling and Control","authors":"Tongjia Zheng;Zhenyuan Yuan;Mollik Nayyar;Alan R. Wagner;Minghui Zhu;Hai Lin","doi":"10.1109/TCST.2024.3410138","DOIUrl":"10.1109/TCST.2024.3410138","url":null,"abstract":"Emergency evacuation describes a complex situation involving time-critical decision-making by evacuees. Mobile robots are being actively explored as a potential solution to provide timely guidance. This work studies a robot-guided crowd evacuation problem where a small group of robots is used to guide a large human crowd to safe locations. The challenge lies in how to use microlevel human-robot interactions to indirectly influence a population that significantly outnumbers the robots to achieve the collective evacuation objective. To address the challenge, we follow a two-scale modeling strategy and explore hydrodynamic models, which consist of a family of microscopic social force models that describe how human movements are locally affected by other humans, the environment, and robots, and associated macroscopic equations for the temporal and spatial evolution of the crowd density and flow velocity. We design controllers for the robots, such that they not only automatically explore the environment (with unknown dynamic obstacles) to cover it as much as possible, but also dynamically adjust the directions of their local navigation force fields based on the real-time macrostates of the crowd to guide the crowd to a safe location. We prove the stability of the proposed evacuation algorithm and conduct extensive simulations to investigate the performance of the algorithm with different combinations of human numbers, robot numbers, and obstacle settings.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"32 6","pages":"2194-2206"},"PeriodicalIF":4.9,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141939882","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}
Pub Date : 2024-06-10DOI: 10.1109/TCST.2024.3407582
Kimberly J. Chan;Joel A. Paulson;Ali Mesbah
The digital age has made embedded control a key component to user-oriented, portable, and the Internet of Things (IoT) devices. In addition, with emergent complex systems, there is a need for advanced optimization-based control strategies such as model predictive control (MPC). However, the unified implementation of these advanced strategies on hardware remains a challenge. Designing complex control policies for embedded systems is inherently an interwoven process between the algorithmic design and hardware implementation, which will require a hardware-software co-design perspective. We propose an end-to-end framework for the automated design and tuning of arbitrary control policies on arbitrary hardware. The proposed framework relies on deep learning as a universal control policy representation and multiobjective Bayesian optimization (MOBO) to facilitate iterative systematic controller design. The large representation power of deep learning and its ability to decouple hardware and software design are a central component to determining feasible control-on-a-chip (CoC) policies. Then, Bayesian optimization (BO) provides a flexible sequential decision-making framework where practical considerations, such as multiobjective optimization (MOO) concepts and categorical decisions, can be incorporated to efficiently design embedded control policies that are directly implemented on hardware. We demonstrate the proposed framework via closed-loop simulations and real-time experiments on an atmospheric pressure plasma jet (APPJ) for plasma processing of biomaterials.
{"title":"A Practical Multiobjective Learning Framework for Optimal Hardware-Software Co-Design of Control-on-a-Chip Systems","authors":"Kimberly J. Chan;Joel A. Paulson;Ali Mesbah","doi":"10.1109/TCST.2024.3407582","DOIUrl":"10.1109/TCST.2024.3407582","url":null,"abstract":"The digital age has made embedded control a key component to user-oriented, portable, and the Internet of Things (IoT) devices. In addition, with emergent complex systems, there is a need for advanced optimization-based control strategies such as model predictive control (MPC). However, the unified implementation of these advanced strategies on hardware remains a challenge. Designing complex control policies for embedded systems is inherently an interwoven process between the algorithmic design and hardware implementation, which will require a hardware-software co-design perspective. We propose an end-to-end framework for the automated design and tuning of arbitrary control policies on arbitrary hardware. The proposed framework relies on deep learning as a universal control policy representation and multiobjective Bayesian optimization (MOBO) to facilitate iterative systematic controller design. The large representation power of deep learning and its ability to decouple hardware and software design are a central component to determining feasible control-on-a-chip (CoC) policies. Then, Bayesian optimization (BO) provides a flexible sequential decision-making framework where practical considerations, such as multiobjective optimization (MOO) concepts and categorical decisions, can be incorporated to efficiently design embedded control policies that are directly implemented on hardware. We demonstrate the proposed framework via closed-loop simulations and real-time experiments on an atmospheric pressure plasma jet (APPJ) for plasma processing of biomaterials.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"32 6","pages":"2178-2193"},"PeriodicalIF":4.9,"publicationDate":"2024-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141939747","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}
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