Pub Date : 2024-12-05DOI: 10.1109/TSMC.2024.3506495
Rafael J. Escarabajal;Pau Zamora-Ortiz;José L. Pulloquinga;Marina Vallés;Ángel Valera
Conventional assistive and rehabilitative robotic systems often overlook human biomechanics, particularly muscular forces, as they predominantly operate in joint or task space and focus on position and exchanged forces. Similarly, traditional manual rehabilitation techniques employed by physiotherapists struggle to obtain quantitative measurements and make precise modifications to key human variables, resulting in predominantly qualitative methods and outcomes. In response to these limitations, this article introduces an innovative assistive robot controller that operates in the muscular space, targeting specific muscles in the lower limb, and distinguishing itself from existing solutions that focus primarily on joint or task space. A key innovation of our approach is the real-time measurement of muscular forces during dynamic tasks, obtained from a calibrated musculoskeletal model. These measurements enable the establishment of a multistep closed-loop controller, with the outer loop precisely tracking the desired muscular forces. Implemented within a configurable viscous environment, the controller provides a natural response for the user. Experimental evaluations conducted using a parallel robot designed for rehabilitation demonstrate the controller’s efficacy. Incorporating the outer loop reduced the median relative error of the tracked muscular force by nearly 80% and decreased the variability of this error by over 85% compared to a pure viscous environment defined as the baseline. These findings highlight the potential applications of this control framework in areas, such as assistive robotics and precision rehabilitation. By achieving objective measurement and control, the system may enhance rehabilitation outcomes, offering tailored exercises that match the individual needs, capabilities, and engagement of each patient.
{"title":"Muscle-Targeted Robotic Assistive Control Using Musculoskeletal Model of the Lower Limb","authors":"Rafael J. Escarabajal;Pau Zamora-Ortiz;José L. Pulloquinga;Marina Vallés;Ángel Valera","doi":"10.1109/TSMC.2024.3506495","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3506495","url":null,"abstract":"Conventional assistive and rehabilitative robotic systems often overlook human biomechanics, particularly muscular forces, as they predominantly operate in joint or task space and focus on position and exchanged forces. Similarly, traditional manual rehabilitation techniques employed by physiotherapists struggle to obtain quantitative measurements and make precise modifications to key human variables, resulting in predominantly qualitative methods and outcomes. In response to these limitations, this article introduces an innovative assistive robot controller that operates in the muscular space, targeting specific muscles in the lower limb, and distinguishing itself from existing solutions that focus primarily on joint or task space. A key innovation of our approach is the real-time measurement of muscular forces during dynamic tasks, obtained from a calibrated musculoskeletal model. These measurements enable the establishment of a multistep closed-loop controller, with the outer loop precisely tracking the desired muscular forces. Implemented within a configurable viscous environment, the controller provides a natural response for the user. Experimental evaluations conducted using a parallel robot designed for rehabilitation demonstrate the controller’s efficacy. Incorporating the outer loop reduced the median relative error of the tracked muscular force by nearly 80% and decreased the variability of this error by over 85% compared to a pure viscous environment defined as the baseline. These findings highlight the potential applications of this control framework in areas, such as assistive robotics and precision rehabilitation. By achieving objective measurement and control, the system may enhance rehabilitation outcomes, offering tailored exercises that match the individual needs, capabilities, and engagement of each patient.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 2","pages":"1537-1548"},"PeriodicalIF":8.6,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993467","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2024-12-05DOI: 10.1109/TSMC.2024.3500776
Xingqiang Zhao;Yantong Zhang;Yongduan Song
Dexterous manipulation of anthropomorphic multifinger robotic hands (MFRHs) is crucial for performing diverse and intricate tasks, where collaboration among the fingers is essential. This article presents a novel neural network-based composite learning strategy tailored for the synchronous control of multiple fingers in anthropomorphic MFRHs subjected to unknown dynamics and disturbances. By leveraging graph theory, the interconnections among fingers are delineated and integrated into the dynamic equations. The modified nonsingular terminal sliding mode (TSM) technique is employed to achieve fixed-time convergence of error variables without triggering singularity. Within the framework of composite learning, a novel computable prediction error is formulated by harnessing online historical data alongside the regression matrix. The combination of prediction errors and the regression matrix is utilized for parameter estimation, which, under a milder interval excitation (IE) condition, facilitates accurate parameter estimation without the requirement for the stringent persistent excitation (PE) condition. The feasibility and effectiveness of the proposed technique are demonstrated through simulation experiments.
{"title":"Neuroadaptive Fixed-Time Synchronous Control With Composite Learning Policy for Robotic Multifingers","authors":"Xingqiang Zhao;Yantong Zhang;Yongduan Song","doi":"10.1109/TSMC.2024.3500776","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3500776","url":null,"abstract":"Dexterous manipulation of anthropomorphic multifinger robotic hands (MFRHs) is crucial for performing diverse and intricate tasks, where collaboration among the fingers is essential. This article presents a novel neural network-based composite learning strategy tailored for the synchronous control of multiple fingers in anthropomorphic MFRHs subjected to unknown dynamics and disturbances. By leveraging graph theory, the interconnections among fingers are delineated and integrated into the dynamic equations. The modified nonsingular terminal sliding mode (TSM) technique is employed to achieve fixed-time convergence of error variables without triggering singularity. Within the framework of composite learning, a novel computable prediction error is formulated by harnessing online historical data alongside the regression matrix. The combination of prediction errors and the regression matrix is utilized for parameter estimation, which, under a milder interval excitation (IE) condition, facilitates accurate parameter estimation without the requirement for the stringent persistent excitation (PE) condition. The feasibility and effectiveness of the proposed technique are demonstrated through simulation experiments.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 2","pages":"1230-1240"},"PeriodicalIF":8.6,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993460","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2024-12-04DOI: 10.1109/TSMC.2024.3505152
Huaguang Zhang;Jiawei Ma;Juan Zhang;Le Wang
The issue of predictor-based neural fixed-time dynamic surface control for the nonlinear systems with unknown backlash-like hysteresis is the research focus of this article. By applying the predictor-based neural control scheme, the system nonlinear functions can be smoothly estimated. In addition, an improved dynamics surface is proposed to decrease the difficulty of the controller design procedure while ensuring that the dynamic surface compensating signals can satisfy the fixed-time stability. Further, on the basis of fixed-time theorem and backstepping control technology, the designed controller can ensure all signals of the considered closed-loop systems are fixed-time bounded in the presence of unknown backlash-like hysteresis. Eventually, the simulation cases are given to imply the effectiveness of the designed method.
{"title":"Predictor-Based Fixed-Time Neural Dynamics Surface Tracking Control for Nonlinear Systems With Unknown Backlash-Like Hysteresis","authors":"Huaguang Zhang;Jiawei Ma;Juan Zhang;Le Wang","doi":"10.1109/TSMC.2024.3505152","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3505152","url":null,"abstract":"The issue of predictor-based neural fixed-time dynamic surface control for the nonlinear systems with unknown backlash-like hysteresis is the research focus of this article. By applying the predictor-based neural control scheme, the system nonlinear functions can be smoothly estimated. In addition, an improved dynamics surface is proposed to decrease the difficulty of the controller design procedure while ensuring that the dynamic surface compensating signals can satisfy the fixed-time stability. Further, on the basis of fixed-time theorem and backstepping control technology, the designed controller can ensure all signals of the considered closed-loop systems are fixed-time bounded in the presence of unknown backlash-like hysteresis. Eventually, the simulation cases are given to imply the effectiveness of the designed method.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 2","pages":"1506-1515"},"PeriodicalIF":8.6,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993470","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This article copes with the distributed control and target tracking (DCTT) problem in general linear and Lipschitz multiagent systems (MASs). In comparison to the traditional DCTT algorithms that were developed for MASs in ideal conditions, two schemes based upon a resilient protocol are proposed for linear and nonlinear MASs to estimate and track a mobile target where all agents are subject to composite attacks, including camouflage attacks, DoS attacks, sensor attacks, and actuator attacks. Based on the digital twin approach, a twin layer (TL) with high privacy and security is introduced to separate the problem of DCTT into two tasks: 1) handling DoS attacks on the TL and defending against sensor and 2) actuator attacks on the cyber-physical layer (CPL). First, two distributed estimation algorithms are established to reconstruct the agents and target dynamics for every agent on the TL in the presence of DoS attacks. Second, using the reconstructed agents and target dynamics on the TL, a resilient distributed control protocol is designed to resist sensor and actuator attacks on the CPL. The current scheme guarantees the achievement of control and target tracking such that the DCTT error of the proposed design is ultimately bounded in terms of linear matrix inequality. By applying two simulation examples, the presented algorithms are also validated.
{"title":"Resilient Distributed Control and Target Tracking in Multiagent Systems Against Composite Attacks","authors":"Yukang Cui;Ahmadreza Jenabzadeh;Zahoor Ahmed;Weidong Zhang;Tingwen Huang","doi":"10.1109/TSMC.2024.3494769","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3494769","url":null,"abstract":"This article copes with the distributed control and target tracking (DCTT) problem in general linear and Lipschitz multiagent systems (MASs). In comparison to the traditional DCTT algorithms that were developed for MASs in ideal conditions, two schemes based upon a resilient protocol are proposed for linear and nonlinear MASs to estimate and track a mobile target where all agents are subject to composite attacks, including camouflage attacks, DoS attacks, sensor attacks, and actuator attacks. Based on the digital twin approach, a twin layer (TL) with high privacy and security is introduced to separate the problem of DCTT into two tasks: 1) handling DoS attacks on the TL and defending against sensor and 2) actuator attacks on the cyber-physical layer (CPL). First, two distributed estimation algorithms are established to reconstruct the agents and target dynamics for every agent on the TL in the presence of DoS attacks. Second, using the reconstructed agents and target dynamics on the TL, a resilient distributed control protocol is designed to resist sensor and actuator attacks on the CPL. The current scheme guarantees the achievement of control and target tracking such that the DCTT error of the proposed design is ultimately bounded in terms of linear matrix inequality. By applying two simulation examples, the presented algorithms are also validated.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 2","pages":"1252-1263"},"PeriodicalIF":8.6,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993393","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2024-12-04DOI: 10.1109/TSMC.2024.3504342
Davide De Benedittis;Franco Angelini;Manolo Garabini
The robotics research community has developed several effective techniques for quadrupedal locomotion. Most of these methods ease the modeling and control problem by assuming a rigid contact between the feet and the terrain. However, in the case of compliant terrain or robots equipped with soft feet, this assumption no longer holds, as the contact point moves and the reaction forces experience a delay. This article presents a novel approach for quadrupedal locomotion in the presence of soft contacts. The control architecture consists of two blocks: 1) upstream, the motion planner (MP) computes a feasible trajectory using model predictive control (MPC) and 2) downstream, the tracking controller (TC) employs hierarchical optimization (HO) to achieve motion tracking. This choice allows the control architecture to employ a large time horizon without heavily compromising the model’s accuracy. For the first time, both blocks consider the contact compliance: in the MP, the classic linear inverted pendulum model is extended by proposing the soft bilinear inverted pendulum (SBIP) model; conversely, the TC is a whole-body controller (WBC) that considers the full dynamics model, including the soft contacts. Simulations with multiple quadrupedal robots demonstrate that the proposed approach enables traversing soft terrains with improved stability and efficiency. Furthermore, the performance benefits of including the compliance in the MP and TC are evaluated. Finally, experiments on the SOLO12 robot walking on soft terrain validate the proposed approach’s effectiveness.
{"title":"Soft Bilinear Inverted Pendulum: A Model to Enable Locomotion With Soft Contacts","authors":"Davide De Benedittis;Franco Angelini;Manolo Garabini","doi":"10.1109/TSMC.2024.3504342","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3504342","url":null,"abstract":"The robotics research community has developed several effective techniques for quadrupedal locomotion. Most of these methods ease the modeling and control problem by assuming a rigid contact between the feet and the terrain. However, in the case of compliant terrain or robots equipped with soft feet, this assumption no longer holds, as the contact point moves and the reaction forces experience a delay. This article presents a novel approach for quadrupedal locomotion in the presence of soft contacts. The control architecture consists of two blocks: 1) upstream, the motion planner (MP) computes a feasible trajectory using model predictive control (MPC) and 2) downstream, the tracking controller (TC) employs hierarchical optimization (HO) to achieve motion tracking. This choice allows the control architecture to employ a large time horizon without heavily compromising the model’s accuracy. For the first time, both blocks consider the contact compliance: in the MP, the classic linear inverted pendulum model is extended by proposing the soft bilinear inverted pendulum (SBIP) model; conversely, the TC is a whole-body controller (WBC) that considers the full dynamics model, including the soft contacts. Simulations with multiple quadrupedal robots demonstrate that the proposed approach enables traversing soft terrains with improved stability and efficiency. Furthermore, the performance benefits of including the compliance in the MP and TC are evaluated. Finally, experiments on the SOLO12 robot walking on soft terrain validate the proposed approach’s effectiveness.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 2","pages":"1478-1491"},"PeriodicalIF":8.6,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10777856","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993484","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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}
Pub Date : 2024-12-04DOI: 10.1109/TSMC.2024.3505613
Bang Giang Le;Viet Cuong Ta
Soft actor-critic (SAC) is a reinforcement learning algorithm that employs the maximum entropy framework to train a stochastic policy. This work examines a specific failure case of SAC where the stochastic policy is trained to maximize the expected entropy from a sparse reward environment. We demonstrate that the over-exploration of SAC can make the entropy temperature collapse, followed by unstable updates to the actor. Based on our analyses, we introduce Reg-SAC, an improved version of SAC, to mitigate the detrimental effects of the entropy temperature on the learning stability of the stochastic policy. Reg-SAC incorporates a clipping value to prevent the entropy temperature collapse and regularizes the gradient updates of the policy via Kullback-Leibler divergence. Through experiments on discrete benchmarks, our proposed Reg-SAC outperforms the standard SAC in spare-reward grid world environments while it is able to maintain competitive performance in the dense-reward Atari benchmark. The results highlight that our regularized version makes the stochastic policy of SAC more stable in discrete-action domains.
{"title":"On the Effectiveness of Regularization Methods for Soft Actor-Critic in Discrete-Action Domains","authors":"Bang Giang Le;Viet Cuong Ta","doi":"10.1109/TSMC.2024.3505613","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3505613","url":null,"abstract":"Soft actor-critic (SAC) is a reinforcement learning algorithm that employs the maximum entropy framework to train a stochastic policy. This work examines a specific failure case of SAC where the stochastic policy is trained to maximize the expected entropy from a sparse reward environment. We demonstrate that the over-exploration of SAC can make the entropy temperature collapse, followed by unstable updates to the actor. Based on our analyses, we introduce Reg-SAC, an improved version of SAC, to mitigate the detrimental effects of the entropy temperature on the learning stability of the stochastic policy. Reg-SAC incorporates a clipping value to prevent the entropy temperature collapse and regularizes the gradient updates of the policy via Kullback-Leibler divergence. Through experiments on discrete benchmarks, our proposed Reg-SAC outperforms the standard SAC in spare-reward grid world environments while it is able to maintain competitive performance in the dense-reward Atari benchmark. The results highlight that our regularized version makes the stochastic policy of SAC more stable in discrete-action domains.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 2","pages":"1425-1438"},"PeriodicalIF":8.6,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993485","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2024-12-03DOI: 10.1109/TSMC.2024.3502661
Hyeong Jin Kim;Sung Jin Yoo
We propose a prescribed-time nonlinear disturbance observer (PTNDO) approach for adaptive prescribed-time tracking of state-constrained strict-feedback systems with unmatched disturbances and nonlinearities. In contrast to existing control methods that address the state constraint problem, the key contribution of this article is the development of a neural-network-based adaptive PTNDO to compensate for unmatched disturbances within a prescribed time while dealing with unknown nonlinearities in the field of the adaptive prescribed-time tracking. Based on a nonlinear transformation function technique that eliminates the conventional feasibility conditions of virtual control laws in recursive design steps, the original state-constrained system is transformed into an unconstrained system. Subsequently, by deriving a practical prescribed-time adjustment function and its related stability lemma, a PTNDO-based adaptive control strategy is established to guarantee that the disturbance observation and tracking errors converge to the adjustable bound, including zero at a prescribed settling time, while maintaining state constraints. Simulation results verify the resulting approach.
{"title":"Adaptive Neural Tracking of Uncertain State-Constrained Nonlinear Systems With Unmatched Disturbances: Prescribed-Time Disturbance Observer Approach","authors":"Hyeong Jin Kim;Sung Jin Yoo","doi":"10.1109/TSMC.2024.3502661","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3502661","url":null,"abstract":"We propose a prescribed-time nonlinear disturbance observer (PTNDO) approach for adaptive prescribed-time tracking of state-constrained strict-feedback systems with unmatched disturbances and nonlinearities. In contrast to existing control methods that address the state constraint problem, the key contribution of this article is the development of a neural-network-based adaptive PTNDO to compensate for unmatched disturbances within a prescribed time while dealing with unknown nonlinearities in the field of the adaptive prescribed-time tracking. Based on a nonlinear transformation function technique that eliminates the conventional feasibility conditions of virtual control laws in recursive design steps, the original state-constrained system is transformed into an unconstrained system. Subsequently, by deriving a practical prescribed-time adjustment function and its related stability lemma, a PTNDO-based adaptive control strategy is established to guarantee that the disturbance observation and tracking errors converge to the adjustable bound, including zero at a prescribed settling time, while maintaining state constraints. Simulation results verify the resulting approach.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 2","pages":"1439-1450"},"PeriodicalIF":8.6,"publicationDate":"2024-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993486","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2024-12-03DOI: 10.1109/TSMC.2024.3501389
Jing Luo;Shiyang Liu;Weiyong Si;Chao Zeng
Supernumerary robotic limb (SRL) is recognized as being at the forefront of robotics innovation, aimed at augmenting human capabilities in complex working environments. Despite their potential to significantly enhance operational efficiency, the integration of SRL for dynamic and intricate tasks presents challenges in teleoperation, precise positioning, and dynamic balance control. To address challenges in initiating control when targets or the SRL’s end-effector are outside the camera’s visual range, a coarse teleoperation strategy is implemented. This strategy utilizes the inertial measurement unit (IMU) and the extended Kalman filter (EKF), enabling basic orientation and movement toward the target area without reliance on visual cues. Challenges in achieving fine-tuned control for accurate task completion, particularly in visual navigation and precise positioning of the SRL’s end-effector, are addressed by integrating object detection via YOLOX with the tangential artificial potential field (T-APF) method for exact path planning. This integration significantly enhances the system’s ability to fine-tune the placement of end-effector. The challenge of conducting balance tasks without force sensors is tackled by adopting a dual-spring model combined with autoregressive (AR) predictive modeling, enabling effective balance support through anticipatory motion adjustments. Experiments have demonstrated the system’s enhanced positional accuracy and maintained synchronization with human movements, underscoring the effectiveness of the integrated approach in facilitating complex human-robot collaborative tasks.
{"title":"Enhancing Human–Robot Collaboration: Supernumerary Robotic Limbs for Object Balance","authors":"Jing Luo;Shiyang Liu;Weiyong Si;Chao Zeng","doi":"10.1109/TSMC.2024.3501389","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3501389","url":null,"abstract":"Supernumerary robotic limb (SRL) is recognized as being at the forefront of robotics innovation, aimed at augmenting human capabilities in complex working environments. Despite their potential to significantly enhance operational efficiency, the integration of SRL for dynamic and intricate tasks presents challenges in teleoperation, precise positioning, and dynamic balance control. To address challenges in initiating control when targets or the SRL’s end-effector are outside the camera’s visual range, a coarse teleoperation strategy is implemented. This strategy utilizes the inertial measurement unit (IMU) and the extended Kalman filter (EKF), enabling basic orientation and movement toward the target area without reliance on visual cues. Challenges in achieving fine-tuned control for accurate task completion, particularly in visual navigation and precise positioning of the SRL’s end-effector, are addressed by integrating object detection via YOLOX with the tangential artificial potential field (T-APF) method for exact path planning. This integration significantly enhances the system’s ability to fine-tune the placement of end-effector. The challenge of conducting balance tasks without force sensors is tackled by adopting a dual-spring model combined with autoregressive (AR) predictive modeling, enabling effective balance support through anticipatory motion adjustments. Experiments have demonstrated the system’s enhanced positional accuracy and maintained synchronization with human movements, underscoring the effectiveness of the integrated approach in facilitating complex human-robot collaborative tasks.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 2","pages":"1334-1347"},"PeriodicalIF":8.6,"publicationDate":"2024-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993139","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2024-12-03DOI: 10.1109/TSMC.2024.3496488
Mingqi Xing;Dazhong Ma;Jing Zhao;Pak Kin Wong
This article investigates the issue of privacy preservation in distributed optimization, where each node possesses a local private objective function and collaborates to minimize the sum of those functions. A novel dynamic average consensus-based distributed Newton algorithm is introduced to achieve consensus, optimality, and differential privacy. Each node utilizes its local gradient and Hessian as time-varying reference signals, facilitating information exchange with neighbors for tracking the average. To safeguard privacy, persistent Laplace noise is introduced into the exchanged data, affecting the estimated optimal solution, gradient, and Hessian averages. To counteract the noise’s impact, the internode coupling strength is adaptively reduced over time through decay factors, allowing for noise attenuation as the algorithm progresses. The algorithm’s convergence to the optimal solution, assuming global function smoothness and strong convexity, is theoretically proven. The algorithm’s accurate convergence to the optimal solution, assuming global function smoothness and strong convexity, is theoretically proven. Furthermore, the efficiency and reliability of the algorithm are empirically validated through simulations of an IEEE 14-bus test system.
{"title":"Differentially Private Dynamic Average Consensus-Based Newton Method for Distributed Optimization Over General Networks","authors":"Mingqi Xing;Dazhong Ma;Jing Zhao;Pak Kin Wong","doi":"10.1109/TSMC.2024.3496488","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3496488","url":null,"abstract":"This article investigates the issue of privacy preservation in distributed optimization, where each node possesses a local private objective function and collaborates to minimize the sum of those functions. A novel dynamic average consensus-based distributed Newton algorithm is introduced to achieve consensus, optimality, and differential privacy. Each node utilizes its local gradient and Hessian as time-varying reference signals, facilitating information exchange with neighbors for tracking the average. To safeguard privacy, persistent Laplace noise is introduced into the exchanged data, affecting the estimated optimal solution, gradient, and Hessian averages. To counteract the noise’s impact, the internode coupling strength is adaptively reduced over time through decay factors, allowing for noise attenuation as the algorithm progresses. The algorithm’s convergence to the optimal solution, assuming global function smoothness and strong convexity, is theoretically proven. The algorithm’s accurate convergence to the optimal solution, assuming global function smoothness and strong convexity, is theoretically proven. Furthermore, the efficiency and reliability of the algorithm are empirically validated through simulations of an IEEE 14-bus test system.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 2","pages":"1348-1361"},"PeriodicalIF":8.6,"publicationDate":"2024-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993138","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2024-12-03DOI: 10.1109/TSMC.2024.3504819
Mengyang Xu;Xia Chen;Fei Hao
This article investigates the problem of distributed formation tracking control for second-order multiagent systems with unknown inertias. The leader-follower longitudinal formation control is considered and the target is to make followers achieve the same speed with the leader and maintain a desired longitudinal spacing. To make sure that the transient time is within user’s preset time and the communication and computation resources are reduced, we focus on the event-triggered predefined-time control problem. To solve the problem, we design a node-based event-triggered controller in which coupling weights are updated based on an adaptive mechanism. Moreover, a state transformation is considered, and by analyzing the predefined-time stability of the transformed state, both the predefined-time longitudinal formation and the boundedness of controller are proved. Note that, with the adaptive updating mechanism, the control parameters do not depend on the information of Laplacian matrix and the bounds of unknown inertias. Thus, the formation is scalable for the case where some agents leave or join in the formation. Furthermore, to avoid updating the desired spacing manually when agents join or leave, we propose a fully distributed event-triggered predefined-time desired spacing decision algorithm based on distributed resource allocation algorithm. With the combination of the proposed spacing decision algorithm and controller, the longitudinal formation control is more scalable, time-saving and energy-saving.
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