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A zeroth-order stochastic implicit method for bilevel-structured actor-critic schemes

Science China(Information Sciences) 2025年第5期68卷 53-71页

作者： Haochen TAO Shisheng CUI Zhuo LI Jian SUN School of Automation Beijing Institute of Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems School of AutomationBeijing Institute of Technology Beijing Institute of Technology Chongqing Innovation Center

Reinforcement learning algorithms are central to the cognition and decision-making of embodied intelligent agents. A bilevel optimization(BO) modeling approach, along with a host of efficient BO algorithms, has been proven to be an effective means of addressing actor-critic(AC) policy optimization problems. In this work, based on a bilevelstructured AC problem model, an implicit zeroth-order stochastic algorithm is developed. A locally randomized spherical smoothing technique, which can be applied to nonsmooth nonconvex implicit AC formulations and avoid the closed-form lower-level mapping, is introduced. In the proposed zeroth-order scheme, the gradient of the implicit function can be approximated through inexact lower-level value estimations that are practically available. Under suitable assumptions,the algorithmic framework designed for the bilevel AC method is characterized by convergence guarantees under a fixed stepsize and smoothing parameter. Moreover, the proposed algorithm is equipped with the overall iteration complexity of O(n2L20 L20?-1). The convergence performance of the proposed algorithm is verified through numerical simulations.

关键词： actor-critic bilevel optimization zeroth-order algorithm implicit programming stochastic approximation

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Position-aware pushing and grasping synergy with deep reinforcement learning in clutter

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CAAI Transactions on Intelligence technology 2024年第3期9卷 738-755页

作者： Min Zhao Guoyu Zuo Shuangyue Yu Daoxiong Gong Zihao Wang Ouattara Sie Intelligent Robotics Laboratory Faculty of Information TechnologyBeijing University of TechnologyBeijingChina Beijing Key Laboratory of Computational Intelligence and Intelligent Systems BeijingChina Laboratory of Biomechatronics and Intelligent Robotics(BIRO) Department of Mechanical and Aerospace EngineeringNorth Carolina State UniversityRaleighNorth CarolinaUSA

The positional information of objects is crucial to enable robots to perform grasping and pushing manipulations in *** effectively perform grasping and pushing manipu-lations,robots need to perceive the position information of objects,including the co-ordinates and spatial relationship between objects(e.g.,proximity,adjacency).The authors propose an end-to-end position-aware deep Q-learning framework to achieve efficient collaborative pushing and grasping in ***,a pair of conjugate pushing and grasping attention modules are proposed to capture the position information of objects and generate high-quality affordance maps of operating positions with features of pushing and grasping *** addition,the authors propose an object isolation metric and clutter metric based on instance segmentation to measure the spatial re-lationships between objects in cluttered *** further enhance the perception capacity of position information of the objects,the authors associate the change in the object isolation metric and clutter metric in cluttered environment before and after performing the action with reward function.A series of experiments are carried out in simulation and real-world which indicate that the method improves sample efficiency,task completion rate,grasping success rate and action efficiency compared to state-of-the-art end-to-end *** that the authors’system can be robustly applied to real-world use and extended to novel *** material is available at https://***/NhG\_k5v3NnM}{https://***/NhG\_k5v3NnM.

关键词： deep learning deep neural networks intelligent robots

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Distributed gradient-free and projection-free algorithm for stochastic constrained optimization

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Autonomous intelligent systems 2024年第1期4卷 320-337页

作者： Jie Hou Xianlin Zeng Chen Chen National Key Laboratory of Autonomous Intelligent Unmanned Systems School of AutomationBeijing Institute of TechnologyBeijing100081China

Distributed stochastic zeroth-order optimization(DSZO),in which the objective function is allocated over multiple agents and the derivative of cost functions is unavailable,arises frequently in large-scale machine learning and reinforcement *** paper introduces a distributed stochastic algorithm for DSZO in a projection-free and gradient-free manner via the Frank-Wolfe framework and the stochastic zeroth-order oracle(SZO).Such a scheme is particularly useful in large-scale constrained optimization problems where calculating gradients or projection operators is impractical,costly,or when the objective function is not differentiable ***,the proposed algorithm,enhanced by recursive momentum and gradient tracking techniques,guarantees convergence with just a single batch per *** significant improvement over existing algorithms substantially lowers the computational *** mild conditions,we prove that the complexity bounds on SZO of the proposed 1 algorithm are O(n/ϵ^(2))andO(n(21/ϵ))for convex and nonconvex cases,*** efficacy of the algorithm is verified on black-box binary classification problems against several competing alternatives.

关键词： Zeroth-order optimization Projection-free method Stochastic constrained optimization Distributed optimization

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Distributed Momentum-Based Frank-Wolfe Algorithm for Stochastic Optimization

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IEEE/CAA Journal of Automatica Sinica 2023年第3期10卷 685-699页

作者： Jie Hou Xianlin Zeng Gang Wang Jian Sun Jie Chen National Key Laboratory of Autonomous Intelligent Unmanned Systems School of AutomationBeijing Institute of TechnologyBeijing 100081China National Key Laboratory of Autonomous Intelligent Unmanned Systems School of AutomationBeijing Institute of TechnologyBeijing 100081 Beijing Institute of Technology Chongqing Innovation Center Chongqing 401120China School of Electronic and Information Engineering Tongji UniversityShanghai 200082and also with the National Key Laboratory of Autonomous Intelligent Unmanned Systemsthe School of AutomationBeijing Institute of TechnologyBeijing 100081China IEEE

This paper considers distributed stochastic optimization,in which a number of agents cooperate to optimize a global objective function through local computations and information exchanges with neighbors over a *** optimization problems are usually tackled by variants of projected stochastic gradient ***,projecting a point onto a feasible set is often *** Frank-Wolfe(FW)method has well-documented merits in handling convex constraints,but existing stochastic FW algorithms are basically developed for centralized *** this context,the present work puts forth a distributed stochastic Frank-Wolfe solver,by judiciously combining Nesterov's momentum and gradient tracking techniques for stochastic convex and nonconvex optimization over *** is shown that the convergence rate of the proposed algorithm is O(k^(-1/2))for convex optimization,and O(1/log_(2)(k))for nonconvex *** efficacy of the algorithm is demonstrated by numerical simulations against a number of competing alternatives.

关键词： Distributed optimization Frank-Wolfe(FW)algorithms momentum-based method stochastic optimization

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A nonlinear hysteresis compensation control of hydro-viscous drive in air transport operations

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Chinese Journal of Aeronautics 2025年第2期38卷 161-174页

作者： Liang WANG Jianjian LIANG Lin ZHANG Shoukun WANG Junzheng WANG School of Automation and Software Shanxi UniversityTaiyuan 030031China Key Laboratory of Autonomous Intelligent Unmanned Systems School of AutomationBeijing Institute of TechnologyBeijing 100081China

The Hydro-Viscous Drive(HVD)speed regulating system finds extensive application in air transport transmission systems to regulate the stepless speed or conduct overload ***,its intrinsic hysteretic behaviors,such as the asymmetric hysteretic and dead zone,could introduce inaccuracy and delay in control applications,posing challenges to system *** paper investigates a Nonlinear Hysteresis Compensation Control(NHCC)that consists of two parts to control the HVD output speed by operating the valve under different engine operating *** the first part,the Inverse Hysteresis Compensator(IHC)based on major loop data is introduced for the asymmetric hysteresis characterization and compensation of the HVD speed control system of the power generation and distribution,which aims to reduce the hysteresis and dead zone effect and expand the effective input *** the second part,the Active Disturbance Rejection Controller(ADRC)is employed to mitigate the hysteresis effects of the compensated system and remove the steady-state error,which allows real-time compensation of the estimated perturbations as state feedback to achieve the required *** experimental laboratory station has been fabricated to evaluate the proposed *** test results show that the NHCC method can regulate the fan speed to the desired value(45 r/min at steady state)and broaden the effective input range to the full range under different engine ***,the proposed control method can reduce the non-linearity of the input and output curves(from 18%to 4%)and compensate for the asymmetric hysteresis(from 38%to 5%).

关键词： Hydro-viscous drive Nonlinear hysteresis compensation Inverse hysteresis compensation Speed regulating system ADRC

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A Stability-guaranteed Model Predictive Control for Autonomous Surface Vessel Trajectory Tracking based on Lyapunov Theory

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IEEE Transactions on Transportation Electrification 2025年第3期11卷 7885-7896页

作者： Chen, Linying Qiu, Qianqian Li, Mengxia Chen, Pengfei Huang, Yaming Mou, Junmin Wuhan University of Technology State Key Laboratory of Maritime Technology and Safety School of Navigation Wuhan430063 China Wuhan University of Technology State Key Laboratory of Maritime Technology and Safety Intelligent Transportation Systems Research Center Wuhan430063 China

This article explores the trajectory tracking problem of an Autonomous Surface Vessel (ASV). As an optimization-based time-domain method, Model Predictive Control (MPC) has incomparable advantages in solving constrained multi-input multi-output system problems. However, ensuring the stability of the MPC is challenging. This article proposes a Stability-Guaranteed Model Predictive Control (SMPC) method by adding the Lyapunov constraint into the linearized MPC framework. The Lyapunov constraint transformed from the backstepping control law restricts the first action of MPC to guarantee closed-loop stability. Furthermore, the stability and recursive feasibility conditions are theoretically derived. Simulation results demonstrate that the proposed SMPC has better tracking and robustness compared with the backstepping methods. © 2025 IEEE.

关键词： Predictive control systems

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Hybrid Dynamic Variables-Dependent Event-Triggered Fuzzy Model Predictive Control

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IEEE/CAA Journal of Automatica Sinica 2024年第3期11卷 723-733页

作者： Xiongbo Wan Chaoling Zhang Fan Wei Chuan-Ke Zhang Min Wu IEEE the School of Automation China University of Geosciencesthe Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systemsand the Engineering Research Center of Intelligent Technology for Geo-ExplorationMinistry of EducationWuhan 430074China

This article focuses on dynamic event-triggered mechanism(DETM)-based model predictive control(MPC) for T-S fuzzy systems.A hybrid dynamic variables-dependent DETM is carefully devised,which includes a multiplicative dynamic variable and an additive dynamic *** addressed DETM-based fuzzy MPC issue is described as a “min-max” optimization problem(OP).To facilitate the co-design of the MPC controller and the weighting matrix of the DETM,an auxiliary OP is proposed based on a new Lyapunov function and a new robust positive invariant(RPI) set that contain the membership functions and the hybrid dynamic variables.A dynamic event-triggered fuzzy MPC algorithm is developed accordingly,whose recursive feasibility is analysed by employing the RPI *** the designed controller,the involved fuzzy system is ensured to be asymptotically *** examples show that the new DETM and DETM-based MPC algorithm have the advantages of reducing resource consumption while yielding the anticipated performance.

关键词： Dynamic event-triggered mechanism(DETM) hybrid dynamic variables model predictive control(MPC) robust positive invariant(RPI)set T-S fuzzy systems

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Active learning attraction basins of dynamical system

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Chinese Physics B 2025年第5期34卷 115-122页

作者： Xiao-Wei Cao Xiao-Lei Ru Gang Yan MOE Key Laboratory of Advanced Micro-Structured Materials and School of Physical Science and EngineeringTongji UniversityShanghai 200092China National Key Laboratory of Autonomous Intelligent Unmanned Systems MOE Frontiers Science Center for Intelligent Autonomous Systemsand Shanghai Research Institute of Intelligence Science and TechnologyTongji UniversityShanghai 200092China

Dynamical systems often exhibit multiple attractors representing significantly different functioning conditions.A global map of attraction basins can offer valuable guidance for stabilizing or transitioning system *** a map can be constructed without prior system knowledge by identifying attractors across a sufficient number of points in the state ***,determining the attractor for each initial state can be a laborious ***,we tackle the challenge of reconstructing attraction basins using as few initial points as *** each iteration of our approach,informative points are selected through random seeding and are driven along the current classification boundary,promoting the eventual selection of points that are both diverse and *** results across various experimental dynamical systems demonstrate that our approach requires fewer points than baseline methods while achieving comparable mapping ***,the reconstructed map allows us to accurately estimate the minimum escape distance required to transition the system state to a target basin.

关键词： complex system attraction basin active learning

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An optical carbon nanotube fibre probe for measurement of nano structures with large aspect ratio

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Nano Research 2025年第3期18卷 436-443页

作者： Weihao Tao Shuhao Zhao Hanjin Dong Ze Zhang Peirui Ji Shenghan Qin Wenbo Hu Jinlong Zhu Renjie Zhou Shuming Yang State Key Laboratory for Manufacturing Systems Engineering Xi’an Jiaotong UniversityXi’an 710049China State Key Laboratory of Intelligent Manufacturing Equipment and Technology Huazhong University of Science and TechnologyWuhan 430074China Department of Biomedical Engineering The Chinese University of Hong KongHong KongChina

With the development of imaging and measurement technologies,scanning near-field optical microscopy(SNOM)has achieved high signal-to-noise *** resolution of a fibre probe-based SNOM system is capable of reaching 10 ***,SNOM applications are presently constrained to the measurement of near-field optical information to relatively straightforward structures,including quantum dots,carbon nanotubes,graphene,and so *** geometry of conventional fibre probes,with tips at an angle of 30°-60°,presents a challenge for accurately imaging complex surface *** paper proposes a carbon nanotube composite fibre probe(CNT-FP)with a large aspect *** key point is that a carbon nanotube bundle is composited at the tip of conventional surface plasmon polaritons fibre probes(SPPs-FP),which are the fibre probes coated with gold film to excite the *** coupling,propagation,and focusing effects of SPPs on the carbon nanotube bundle are ***-FPs have been fabricated and applied to measure a grating with the depth of 400 nm and the width of 400 *** experimental results show that the measurement accuracy and imaging quality of CNT-FP are nearly one order of magnitude higher than that of conventional SPPs-FP,as evidenced by evaluation criteria such as line roughness and volatility ***,it achieves an optical resolution of 72.1 nm in the measurements of a nano structure with large aspect *** provides an effective solution of measuring structures with larger aspect ratios.

关键词： carbon nanotubes fibre optic probes surface plasmon polaritons nano-focusing large aspect ratio

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Formation control and path planning of multi-robot systems via large language models

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Science China(Information Sciences) 2025年第5期68卷 72-88页

作者： Dong XUE Xuanjie ZHOU Ming WANG Fangzhou LIU Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of EducationEast China University of Science and Technology Research Institute of Intelligent Control and Systems School of Astronautics Harbin Institute of Technology

Existing path planning and coordination control methods for multi-robot systems(MRS) typically rely on predefined rules and rudimentary algorithms. However, these methods often struggle to adapt flexibly to complex environments and to adjust motion targets appropriately. To address this challenge, this study presents a large language model(LLM)-assisted framework. By integrating textual descriptions of complex motion constraints, robot information, and local environmental data as inputs, LLMs generate motion objectives and translate them into executable control commands for the robots, thereby achieving coordinated control and path planning. This framework facilitates the generation, maintenance, and reshaping of formations in MRSs during path planning, applicable to both obstacle-free and obstacle-avoidance environments. Simulation results demonstrate that LLM-based control strategies enhance the autonomy, adaptability, flexibility, and robustness of MRS by processing complex information, making intelligent decisions, adapting to environmental changes, and handling disturbances and uncertainties.

关键词： multi-robot systems formation control path planning obstacle avoidance large language models

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