Pub Date : 2024-08-02DOI: 10.1109/TSMC.2024.3430265
Guangzhu Peng;Tao Li;Chenguang Yang;C. L. Philip Chen
Humans are able to compliantly interact with the environment by adapting its motion trajectory and contact force. Robots with the human versatility can perform contact tasks more efficiently with high motion precision. Motivated by multiple capabilities, we develop an approximation-based admittance control strategy that adapts and tracks the trajectory with guaranteed performance for the robots interacting with unknown environments. In this strategy, the robot can adapt and compensate its feedforward force and stiffness to interact with the unknown environment. In particular, a reference trajectory is generated through the admittance control to achieve a desired interaction level. To improve the interaction performance, a tracking error bound for both the transient and steady states is prespecified, and a controller is designed to ensure the tracking control performance. In the presence of unknown robot dynamics, neural networks are integrated into tracking controller to compensate uncertainties. The stability and convergence conditions of the closed-loop system are analysed by the Lyapunov theory. The effectiveness of the proposed control method is demonstrated on the Baxter robot.
{"title":"Approximation-Based Admittance Control of Robot-Environment Interaction With Guaranteed Performance","authors":"Guangzhu Peng;Tao Li;Chenguang Yang;C. L. Philip Chen","doi":"10.1109/TSMC.2024.3430265","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3430265","url":null,"abstract":"Humans are able to compliantly interact with the environment by adapting its motion trajectory and contact force. Robots with the human versatility can perform contact tasks more efficiently with high motion precision. Motivated by multiple capabilities, we develop an approximation-based admittance control strategy that adapts and tracks the trajectory with guaranteed performance for the robots interacting with unknown environments. In this strategy, the robot can adapt and compensate its feedforward force and stiffness to interact with the unknown environment. In particular, a reference trajectory is generated through the admittance control to achieve a desired interaction level. To improve the interaction performance, a tracking error bound for both the transient and steady states is prespecified, and a controller is designed to ensure the tracking control performance. In the presence of unknown robot dynamics, neural networks are integrated into tracking controller to compensate uncertainties. The stability and convergence conditions of the closed-loop system are analysed by the Lyapunov theory. The effectiveness of the proposed control method is demonstrated on the Baxter robot.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":null,"pages":null},"PeriodicalIF":8.6,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142235683","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 : 2024-08-02DOI: 10.1109/TSMC.2024.3431033
Yang Wang;Junpeng Wang;Jin-Kao Hao;Jianguang Feng
We study a sensor-weapon–target assignment (S-WTA) problem that considers the desired probability of target destruction and aims to minimize the total cost of combat resources. Lower and upper bounds for the S-WTA problem are obtained by constructing linear approximation models. We also propose an adaptive large neighborhood search (ALNS) algorithm characterized by a model-driven repair phase to solve this problem. The destruction phase adaptively selects a destruction operator to remove partial resource assignments and produces an incomplete reference solution. For the destroyed solution, the repair phase generates a reduced subproblem that optimizes only the destroyed parts while keeping the other parts fixed. Each subproblem is formulated as a mixed integer programming model and solved by a general-purpose solver to repair the destroyed solution. Computational experiments show that the approximation formulations can obtain tight lower and upper bounds for most problem instances. Moreover, our proposed ALNS algorithm is competitive with the solver for small instances and effectively solves large instances. In addition, we experimentally demonstrate that our ALNS outperforms state-of-the-art algorithms in the literature, and the proposed model-driven solution repair phase outperforms the traditional heuristic repair operators.
{"title":"Efficient Adaptive Large Neighborhood Search for Sensor–Weapon–Target Assignment","authors":"Yang Wang;Junpeng Wang;Jin-Kao Hao;Jianguang Feng","doi":"10.1109/TSMC.2024.3431033","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3431033","url":null,"abstract":"We study a sensor-weapon–target assignment (S-WTA) problem that considers the desired probability of target destruction and aims to minimize the total cost of combat resources. Lower and upper bounds for the S-WTA problem are obtained by constructing linear approximation models. We also propose an adaptive large neighborhood search (ALNS) algorithm characterized by a model-driven repair phase to solve this problem. The destruction phase adaptively selects a destruction operator to remove partial resource assignments and produces an incomplete reference solution. For the destroyed solution, the repair phase generates a reduced subproblem that optimizes only the destroyed parts while keeping the other parts fixed. Each subproblem is formulated as a mixed integer programming model and solved by a general-purpose solver to repair the destroyed solution. Computational experiments show that the approximation formulations can obtain tight lower and upper bounds for most problem instances. Moreover, our proposed ALNS algorithm is competitive with the solver for small instances and effectively solves large instances. In addition, we experimentally demonstrate that our ALNS outperforms state-of-the-art algorithms in the literature, and the proposed model-driven solution repair phase outperforms the traditional heuristic repair operators.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":null,"pages":null},"PeriodicalIF":8.6,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142274910","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 : 2024-08-02DOI: 10.1109/TSMC.2024.3432000
Styliani I. Kampezidou;Justin Romberg;Kyriakos G. Vamvoudakis;Dimitri N. Mavris
In the pathway to 2030 electricity generation decarbonization and 2050 net-zero economies, scalable integration of distributed load can support environmental goals and also help alleviate smart grid operational issues through its electricity market participation. In this work, a novel Stackelberg game theoretic framework is proposed for trading the energy bidirectionally between the demand-response (DR) aggregator and the prosumers (distributed load). This formulation allows for flexible energy arbitrage and additional monetary rewards while ensuring that the prosumers’ desired daily energy demand is met. Then, a scalable (linear with the number of prosumers and the number of learning samples), the decentralized privacy-preserving algorithm is proposed to find approximate equilibria with online sampling and learning of the prosumers’ cumulative best response, which finds applications beyond this energy game. Moreover, cost bounds are provided on the quality of the approximate equilibrium solution. Finally, the real data from the California day-ahead market and the UC Davis campus building energy demands are utilized to demonstrate the efficacy of the proposed framework and the algorithm.
{"title":"Decentralized and Privacy-Preserving Learning of Approximate Stackelberg Solutions in Energy Trading Games With Demand Response Aggregators","authors":"Styliani I. Kampezidou;Justin Romberg;Kyriakos G. Vamvoudakis;Dimitri N. Mavris","doi":"10.1109/TSMC.2024.3432000","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3432000","url":null,"abstract":"In the pathway to 2030 electricity generation decarbonization and 2050 net-zero economies, scalable integration of distributed load can support environmental goals and also help alleviate smart grid operational issues through its electricity market participation. In this work, a novel Stackelberg game theoretic framework is proposed for trading the energy bidirectionally between the demand-response (DR) aggregator and the prosumers (distributed load). This formulation allows for flexible energy arbitrage and additional monetary rewards while ensuring that the prosumers’ desired daily energy demand is met. Then, a scalable (linear with the number of prosumers and the number of learning samples), the decentralized privacy-preserving algorithm is proposed to find approximate equilibria with online sampling and learning of the prosumers’ cumulative best response, which finds applications beyond this energy game. Moreover, cost bounds are provided on the quality of the approximate equilibrium solution. Finally, the real data from the California day-ahead market and the UC Davis campus building energy demands are utilized to demonstrate the efficacy of the proposed framework and the algorithm.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":null,"pages":null},"PeriodicalIF":8.6,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142274954","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 : 2024-08-01DOI: 10.1109/TSMC.2024.3428707
Pooja;Sandeep Kumar Sood
The rapid proliferation of quantum information technologies, spanning theoretical investigations to practical experiments, has generated a number of research papers and documents in quantum algorithms. Consequently, the current research serves as a gateway for interested readers to comprehend the status quo of quantum algorithms, with a specific focus on vehicular network optimization. It aims to explore the research patterns and latest trends by analyzing the dataset sourced from the Scopus and Web of Science databases. The scientometric implications offer valuable insights into publication patterns, keyword co-occurrence, author co-citation, country collaboration, and burst reference. These analyses delineate the temporal progression, prominent research topics, emerging research areas, leading collaborative nations, prolific authors, and research trends within this knowledge domain. The results reveal that smart power grids, traveling salesman problem, electric vehicle charging, battery life estimation, and air traffic control are emerging research areas. Similarly, quantum approximate optimization algorithms, adiabatic quantum computing, quantum-inspired evolutionary algorithms, and quantum annealing emerge as prominent quantum algorithms employed for vehicular network optimization problems. In addition, systematic literature analysis is objectively conducted to discern key insights, research challenges and future research directions in the current knowledge domain.
从理论研究到实际实验，量子信息技术的迅速发展催生了大量量子算法方面的研究论文和文献。因此，当前的研究为感兴趣的读者提供了一个了解量子算法现状的途径，特别是在车载网络优化方面。本研究旨在通过分析 Scopus 和 Web of Science 数据库中的数据集，探索研究模式和最新趋势。科学计量学的意义在于对发表模式、关键词共现、作者共引、国家合作和突发参考文献提供有价值的见解。这些分析勾勒出该知识领域的时间进程、突出研究课题、新兴研究领域、主要合作国家、多产作者和研究趋势。结果显示，智能电网、旅行推销员问题、电动汽车充电、电池寿命估计和空中交通管制是新兴研究领域。同样，量子近似优化算法、绝热量子计算、量子启发的进化算法和量子退火也成为车载网络优化问题的主要量子算法。此外，还客观地进行了系统的文献分析，以找出当前知识领域的关键见解、研究挑战和未来研究方向。
{"title":"Scientometric Analysis of Quantum Algorithms for VANET Optimization","authors":"Pooja;Sandeep Kumar Sood","doi":"10.1109/TSMC.2024.3428707","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3428707","url":null,"abstract":"The rapid proliferation of quantum information technologies, spanning theoretical investigations to practical experiments, has generated a number of research papers and documents in quantum algorithms. Consequently, the current research serves as a gateway for interested readers to comprehend the status quo of quantum algorithms, with a specific focus on vehicular network optimization. It aims to explore the research patterns and latest trends by analyzing the dataset sourced from the Scopus and Web of Science databases. The scientometric implications offer valuable insights into publication patterns, keyword co-occurrence, author co-citation, country collaboration, and burst reference. These analyses delineate the temporal progression, prominent research topics, emerging research areas, leading collaborative nations, prolific authors, and research trends within this knowledge domain. The results reveal that smart power grids, traveling salesman problem, electric vehicle charging, battery life estimation, and air traffic control are emerging research areas. Similarly, quantum approximate optimization algorithms, adiabatic quantum computing, quantum-inspired evolutionary algorithms, and quantum annealing emerge as prominent quantum algorithms employed for vehicular network optimization problems. In addition, systematic literature analysis is objectively conducted to discern key insights, research challenges and future research directions in the current knowledge domain.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":null,"pages":null},"PeriodicalIF":8.6,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142274960","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 : 2024-08-01DOI: 10.1109/TSMC.2024.3422925
Yihui Hu;Ziyue Ma;Ruotian Liu;Maria Pia Fanti;Zhiwu Li
This research focuses on the forbidden state problem in the framework of labeled Petri nets (LPNs), i.e., to design a supervisor for a plant modeled by an LPN such that the closed-loop system cannot reach a set of predefined forbidden markings and does not contain any deadlock. Different from the traditional control scheme, the supervisor derived by this work can not only observe the observable transitions, but also the quiescence information. First, a new structure named an extended basis reachability graph (EBRG) is introduced to describe the reachability space of an LPN without computing all reachable markings. Based on an EBRG, a basis observer is then excogitated to represent the behavior of an LPN. Some states in the basis observer are defined as bad states and control-induced deadlocks, which relates to the undesirable behavior of the plant. Finally, an algorithm is introduced to compute a supervisor based on the basis observer. The consideration of system quiescence provides extra information on the marking estimation of the closed-loop system such that certain disabled transitions are re-enabled. Consequently, the developed supervisor in this article is generally more permissive than those do not observe the quiescence.
{"title":"Supervisor Synthesis Using Labeled Petri Nets for Forbidden State Specifications","authors":"Yihui Hu;Ziyue Ma;Ruotian Liu;Maria Pia Fanti;Zhiwu Li","doi":"10.1109/TSMC.2024.3422925","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3422925","url":null,"abstract":"This research focuses on the forbidden state problem in the framework of labeled Petri nets (LPNs), i.e., to design a supervisor for a plant modeled by an LPN such that the closed-loop system cannot reach a set of predefined forbidden markings and does not contain any deadlock. Different from the traditional control scheme, the supervisor derived by this work can not only observe the observable transitions, but also the quiescence information. First, a new structure named an extended basis reachability graph (EBRG) is introduced to describe the reachability space of an LPN without computing all reachable markings. Based on an EBRG, a basis observer is then excogitated to represent the behavior of an LPN. Some states in the basis observer are defined as bad states and control-induced deadlocks, which relates to the undesirable behavior of the plant. Finally, an algorithm is introduced to compute a supervisor based on the basis observer. The consideration of system quiescence provides extra information on the marking estimation of the closed-loop system such that certain disabled transitions are re-enabled. Consequently, the developed supervisor in this article is generally more permissive than those do not observe the quiescence.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":null,"pages":null},"PeriodicalIF":8.6,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142274848","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 : 2024-07-31DOI: 10.1109/TSMC.2024.3408473
You Zhao;Xing He;Mingliang Zhou;Junzhi Yu;Tingwen Huang
This article investigates a fully distributed inertial neurodynamic approach for sparse recovery. The approach is based on proximal operators and inertia items. It aims to solve the $L_{1}$ -norm minimization problem with consensus and linear observation constraints over directed communication networks. The proposed neurodynamic approach has the advantages of only requiring the communication network to be directed and weight-balanced, does not involve a central processing node and global parameters, which means that no single node can access the entire network and observe it at any time, so it is fully distributed. To effectively deal with the nonsmooth objective function, $L_{1}$ -norm, the proximal operator method is used here. For efficiently handling linear observation and consensus constraints, a primal-dual method is applied to the inertial dynamic system. With the aid of maximal monotone operator theory and Baillon-Haddad lemmas, it reveals that the trajectories of our approach can converge to consensus solution at the optimal solution, provided that the distributed parameters satisfy technical conditions. In addition, we aim to demonstrate the weak convergence of the trajectories in our proposed neurodynamic approach toward the zeros of the optimal operator in Hilbert space, using Opial’s lemma. Finally, comparative experiments on sparse signal and image recovery confirm the efficiency and effectiveness of our proposed neurodynamic approach.
{"title":"Distributed Inertial Proximal Neurodynamic Approach for Sparse Recovery on Directed Networks","authors":"You Zhao;Xing He;Mingliang Zhou;Junzhi Yu;Tingwen Huang","doi":"10.1109/TSMC.2024.3408473","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3408473","url":null,"abstract":"This article investigates a fully distributed inertial neurodynamic approach for sparse recovery. The approach is based on proximal operators and inertia items. It aims to solve the \u0000<inline-formula> <tex-math>$L_{1}$ </tex-math></inline-formula>\u0000-norm minimization problem with consensus and linear observation constraints over directed communication networks. The proposed neurodynamic approach has the advantages of only requiring the communication network to be directed and weight-balanced, does not involve a central processing node and global parameters, which means that no single node can access the entire network and observe it at any time, so it is fully distributed. To effectively deal with the nonsmooth objective function, \u0000<inline-formula> <tex-math>$L_{1}$ </tex-math></inline-formula>\u0000-norm, the proximal operator method is used here. For efficiently handling linear observation and consensus constraints, a primal-dual method is applied to the inertial dynamic system. With the aid of maximal monotone operator theory and Baillon-Haddad lemmas, it reveals that the trajectories of our approach can converge to consensus solution at the optimal solution, provided that the distributed parameters satisfy technical conditions. In addition, we aim to demonstrate the weak convergence of the trajectories in our proposed neurodynamic approach toward the zeros of the optimal operator in Hilbert space, using Opial’s lemma. Finally, comparative experiments on sparse signal and image recovery confirm the efficiency and effectiveness of our proposed neurodynamic approach.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":null,"pages":null},"PeriodicalIF":8.6,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142275007","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 proposes a distributed capture strategy optimization method for the pursuit-evasion game involving multiple unmanned surface vehicles. Considering the limited perception range of each pursuer, a multiagent proximal policy optimization method combined with a novel velocity control mechanism is utilized to guide the pursuers in approaching the evader and form a dynamic encirclement. Moreover, to facilitate deep reinforcement learning (DRL) training, a bidirectional gated recurrent unit feature network is constructed to extract the fixed-length vector representations from the variable-length observation sequences. In terms of the policy training, by employing virtual barriers and curriculum learning techniques during the training process, the generalization capabilities and convergence speed of the policy have been further improved. Finally, our method is compared with the other DRL methods through the comparative simulation experiments and virtual reality scene testing based on the gazebo three dimensional physics engine, verifying its significant advantages in the policy convergence speed, capture efficiency, and generalization capabilities.
{"title":"Distributed Pursuit-Evasion Game of Limited Perception USV Swarm Based on Multiagent Proximal Policy Optimization","authors":"Fanbiao Li;Mengmeng Yin;Tengda Wang;Tingwen Huang;Chunhua Yang;Weihua Gui","doi":"10.1109/TSMC.2024.3429467","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3429467","url":null,"abstract":"This article proposes a distributed capture strategy optimization method for the pursuit-evasion game involving multiple unmanned surface vehicles. Considering the limited perception range of each pursuer, a multiagent proximal policy optimization method combined with a novel velocity control mechanism is utilized to guide the pursuers in approaching the evader and form a dynamic encirclement. Moreover, to facilitate deep reinforcement learning (DRL) training, a bidirectional gated recurrent unit feature network is constructed to extract the fixed-length vector representations from the variable-length observation sequences. In terms of the policy training, by employing virtual barriers and curriculum learning techniques during the training process, the generalization capabilities and convergence speed of the policy have been further improved. Finally, our method is compared with the other DRL methods through the comparative simulation experiments and virtual reality scene testing based on the gazebo three dimensional physics engine, verifying its significant advantages in the policy convergence speed, capture efficiency, and generalization capabilities.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":null,"pages":null},"PeriodicalIF":8.6,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142274918","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 : 2024-07-30DOI: 10.1109/TSMC.2024.3427312
Shaohua Luo;Yongduan Song;Guangwei Deng;Junxing Zhang;Hassen M. Ouakad
This article mainly investigates the model design, field programmable gate array (FPGA) implementation, and prescribed-time fault-tolerant control of a fractional-order (FO) decoupled dual-mass micro-electro-mechanical system (MEMS) gyro with deferred constraints. First, the structure of such MEMS gyro is designed to eliminate the linear acceleration in the sensing direction and its mathematical model is built based on the Lagrange’s equation. The dynamical analysis shows that such gyro can generate unpredictable, random, and disorder motions under various FOs, stiffness cross the coupling coefficients and proof masses. The designed FPGA circuit further demonstrates the undesirable chaotic oscillations of such MEMS gyro and good hardware resources utilization, avoiding the time consuming and board redesign. Second, to better solve the problems of constraints, actuator faults, uncertainties, drive couplings, and chaotic oscillations, a dependent deferred-error function superimposed to a prescribed-time function is used to guarantee no violation of constraints after a finite time. Furthermore, a $beta $ -cut type-2 fuzzy logic system (T2FLS) is employed to solve the uncertainty, and an FO hyperbolic tangent tracking differentiator (HTTD) is utilized to deal with the direct FO derivative and repeated derivative in the framework of the backstepping control. Then, a prescribed-time fault-tolerant control scheme of the FO decoupled dual-mass MEMS gyro is proposed under the actuator fault. Finally, the abundant simulation experimental results verify the feasibility and effectiveness of our scheme.
{"title":"Prescribed-Time Fault-Tolerant Control of the FO Decoupled Dual-Mass MEMS Gyro With Deferred Constraints-Design and Implementation","authors":"Shaohua Luo;Yongduan Song;Guangwei Deng;Junxing Zhang;Hassen M. Ouakad","doi":"10.1109/TSMC.2024.3427312","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3427312","url":null,"abstract":"This article mainly investigates the model design, field programmable gate array (FPGA) implementation, and prescribed-time fault-tolerant control of a fractional-order (FO) decoupled dual-mass micro-electro-mechanical system (MEMS) gyro with deferred constraints. First, the structure of such MEMS gyro is designed to eliminate the linear acceleration in the sensing direction and its mathematical model is built based on the Lagrange’s equation. The dynamical analysis shows that such gyro can generate unpredictable, random, and disorder motions under various FOs, stiffness cross the coupling coefficients and proof masses. The designed FPGA circuit further demonstrates the undesirable chaotic oscillations of such MEMS gyro and good hardware resources utilization, avoiding the time consuming and board redesign. Second, to better solve the problems of constraints, actuator faults, uncertainties, drive couplings, and chaotic oscillations, a dependent deferred-error function superimposed to a prescribed-time function is used to guarantee no violation of constraints after a finite time. Furthermore, a \u0000<inline-formula> <tex-math>$beta $ </tex-math></inline-formula>\u0000-cut type-2 fuzzy logic system (T2FLS) is employed to solve the uncertainty, and an FO hyperbolic tangent tracking differentiator (HTTD) is utilized to deal with the direct FO derivative and repeated derivative in the framework of the backstepping control. Then, a prescribed-time fault-tolerant control scheme of the FO decoupled dual-mass MEMS gyro is proposed under the actuator fault. Finally, the abundant simulation experimental results verify the feasibility and effectiveness of our scheme.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":null,"pages":null},"PeriodicalIF":8.6,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142274970","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 : 2024-07-29DOI: 10.1109/TSMC.2024.3428482
Ding Wang;Jiangyu Wang;Lingzhi Hu;Liguo Zhang
Parallelization is widely employed to improve the exploration ability of controllers. However, it is rare to provide a lightweight scheme for reducing homogeneous policies with theoretical guarantees. This article is concerned with a novel parallel scheme for solving optimal control problems. In brief, we design a novel global indicator that inherits the theoretical guarantees of a class of iterative reinforcement learning algorithms. By generating a tentative function, the global indicator can guide and communicate with parallel controllers to accelerate the learning process. Using two typical exploration policies, the novel parallel scheme can rapidly compress the neighborhood of the optimal cost function. Besides, two parallel algorithms based on value iteration and Q-learning are established to improve the data efficiency through different extensions. Finally, two benchmark problems are presented to demonstrate the learning effectiveness of the novel parallel scheme.
{"title":"Novel Parallel Formulation for Iterative Reinforcement Learning Control","authors":"Ding Wang;Jiangyu Wang;Lingzhi Hu;Liguo Zhang","doi":"10.1109/TSMC.2024.3428482","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3428482","url":null,"abstract":"Parallelization is widely employed to improve the exploration ability of controllers. However, it is rare to provide a lightweight scheme for reducing homogeneous policies with theoretical guarantees. This article is concerned with a novel parallel scheme for solving optimal control problems. In brief, we design a novel global indicator that inherits the theoretical guarantees of a class of iterative reinforcement learning algorithms. By generating a tentative function, the global indicator can guide and communicate with parallel controllers to accelerate the learning process. Using two typical exploration policies, the novel parallel scheme can rapidly compress the neighborhood of the optimal cost function. Besides, two parallel algorithms based on value iteration and Q-learning are established to improve the data efficiency through different extensions. Finally, two benchmark problems are presented to demonstrate the learning effectiveness of the novel parallel scheme.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":null,"pages":null},"PeriodicalIF":8.6,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142274972","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}
Leaderless rational individuals often lead the group into a resource decision dilemma in resource competition. Reducing the cost of resource competition while avoiding group decision dilemmas is a challenging task. Inspired by multiagent systems (MASs) and repeated games, we propose a decision-making reward discrimination (DRD) framework to address the resource competition dilemma of leaderless group formation. We aim to model the leaderless group’s resource gaming process using MAS and achieve optimal rewards for the group while minimizing conflict in resource competition. The proposed framework consists of three modules: 1) the decision-making module; 2) the reward module; and 3) the discriminative module. The decision-making module defines the agents and models the decision-making process, while the reward module calculates the group reward in each round using the reward matrix. The discriminative module compares the group reward with the target reward while providing the agent with environmental information. We verify the feasibility of the model through numerous experiments. The results show that agents adopt a revenge strategy to avoid resource competition dilemmas and achieve group reward optimality.
在资源竞争中，无领导的理性个体往往会导致群体陷入资源决策困境。在避免群体决策困境的同时降低资源竞争成本是一项具有挑战性的任务。受多代理系统（MAS）和重复博弈的启发，我们提出了一种决策奖赏判别（DRD）框架，以解决无领导小组形成过程中的资源竞争困境。我们的目标是利用 MAS 对无领导小组的资源博弈过程进行建模，并在资源竞争冲突最小化的同时实现小组的最优回报。所提出的框架由三个模块组成：1）决策模块；2）奖励模块；3）判别模块。决策模块定义了代理并模拟了决策过程，而奖励模块则利用奖励矩阵计算每轮的群体奖励。判别模块在为代理提供环境信息的同时，将群体奖励与目标奖励进行比较。我们通过大量实验验证了该模型的可行性。结果表明，代理采用复仇策略避免了资源竞争困境，并实现了群体奖励最优。
{"title":"Resolving the Resource Decision-Making Dilemma of Leaderless Group-Based Multiagent Systems and Repeated Games","authors":"Junxiao Xue;Mingchuang Zhang;Bowei Dong;Lei Shi;Andrés Adolfo Navarro Newball","doi":"10.1109/TSMC.2024.3427688","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3427688","url":null,"abstract":"Leaderless rational individuals often lead the group into a resource decision dilemma in resource competition. Reducing the cost of resource competition while avoiding group decision dilemmas is a challenging task. Inspired by multiagent systems (MASs) and repeated games, we propose a decision-making reward discrimination (DRD) framework to address the resource competition dilemma of leaderless group formation. We aim to model the leaderless group’s resource gaming process using MAS and achieve optimal rewards for the group while minimizing conflict in resource competition. The proposed framework consists of three modules: 1) the decision-making module; 2) the reward module; and 3) the discriminative module. The decision-making module defines the agents and models the decision-making process, while the reward module calculates the group reward in each round using the reward matrix. The discriminative module compares the group reward with the target reward while providing the agent with environmental information. We verify the feasibility of the model through numerous experiments. The results show that agents adopt a revenge strategy to avoid resource competition dilemmas and achieve group reward optimality.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":null,"pages":null},"PeriodicalIF":8.6,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142274945","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}
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