Dear Editor, This letter proposes a novel dynamic vision-enabled intelligent micro-vibration estimation method with spatiotemporal pattern consistency. Inspired by biological vision, dynamic vision data are collected by the event camera, which is able to capture the micro-vibration information of mechanical equipment, due to the significant advantage of extremely high temporal sampling frequency. A specially designed vibration information extraction module is proposed for processing the dynamic vision data and extracting the hidden micro-vibration information. The proposed method can also simultaneously fuse vibration information extracted from multiple areas of interest and improve the vibration estimation performance. Experiments on rotating machinery test rigs are conducted to demonstrate the effectiveness of the proposed method. The experimental results validate that the proposed method can accurately estimate micro-vibration from dynamic visual signals, thus enabling non-contact vibration monitoring from the perspective of vision.
{"title":"Dynamic Vision-Enabled Intelligent Micro-Vibration Estimation Method with Spatiotemporal Pattern Consistency","authors":"Shupeng Yu;Xiang Li;Yaguo Lei;Bin Yang;Naipeng Li","doi":"10.1109/JAS.2024.125007","DOIUrl":"https://doi.org/10.1109/JAS.2024.125007","url":null,"abstract":"Dear Editor, This letter proposes a novel dynamic vision-enabled intelligent micro-vibration estimation method with spatiotemporal pattern consistency. Inspired by biological vision, dynamic vision data are collected by the event camera, which is able to capture the micro-vibration information of mechanical equipment, due to the significant advantage of extremely high temporal sampling frequency. A specially designed vibration information extraction module is proposed for processing the dynamic vision data and extracting the hidden micro-vibration information. The proposed method can also simultaneously fuse vibration information extracted from multiple areas of interest and improve the vibration estimation performance. Experiments on rotating machinery test rigs are conducted to demonstrate the effectiveness of the proposed method. The experimental results validate that the proposed method can accurately estimate micro-vibration from dynamic visual signals, thus enabling non-contact vibration monitoring from the perspective of vision.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 11","pages":"2359-2361"},"PeriodicalIF":19.2,"publicationDate":"2025-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11271386","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145612152","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}
The dynamic average consensus (DAC) algorithm is to enable a group of networked agents to track the average of their time-varying reference signals. For most existing DAC algorithms, a necessary assumption is that the upper bounds of the reference signals and their derivatives are known in advance, thereby posing significant challenges in practical scenarios. Introducing adaptive gains in DAC algorithms provides a remedy by relaxing this assumption. However, the current adaptive gains used in this type of DAC algorithms are non-decreasing and may increase to infinity if persist disturbance exists. In order to overcome this defect, this paper presents a novel DAC algorithm with modified adaptive gains. This approach obviates the necessity for prior knowledge concerning the upper bounds of the reference signals and their derivatives. Moreover, the adaptive gains are able to remain bounded even in the presence of external disturbances. Furthermore, the proposed adaptive DAC algorithm is employed to address the distributed secondary control problem of DC microgrids. Comparative case studies are provided to verify the superiority of the proposed DAC algorithm.
{"title":"A Novel Adaptive Dynamic Average Consensus Algorithm with Application to DC Microgrids","authors":"Jing Wu;Lantao Xing;Zhengguang Wu","doi":"10.1109/JAS.2025.125387","DOIUrl":"https://doi.org/10.1109/JAS.2025.125387","url":null,"abstract":"The dynamic average consensus (DAC) algorithm is to enable a group of networked agents to track the average of their time-varying reference signals. For most existing DAC algorithms, a necessary assumption is that the upper bounds of the reference signals and their derivatives are known in advance, thereby posing significant challenges in practical scenarios. Introducing adaptive gains in DAC algorithms provides a remedy by relaxing this assumption. However, the current adaptive gains used in this type of DAC algorithms are non-decreasing and may increase to infinity if persist disturbance exists. In order to overcome this defect, this paper presents a novel DAC algorithm with modified adaptive gains. This approach obviates the necessity for prior knowledge concerning the upper bounds of the reference signals and their derivatives. Moreover, the adaptive gains are able to remain bounded even in the presence of external disturbances. Furthermore, the proposed adaptive DAC algorithm is employed to address the distributed secondary control problem of DC microgrids. Comparative case studies are provided to verify the superiority of the proposed DAC algorithm.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 11","pages":"2342-2352"},"PeriodicalIF":19.2,"publicationDate":"2025-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145612165","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}
The paper presents an adaptive controller formulated for a class of nonaffine discrete-time systems with non-strict forms and unknown dynamics. The controller operates based solely on the measured output, thus obviating the need for knowledge of the physical order of the controlled plant. Utilizing an ideal solution and equivalent dynamics, the approach integrates an adaptive network with feedback and robust controllers to establish a closed-loop system. A learning law is derived under practical conditions of the designed parameters, ensuring effective closed-loop performance based on pure-output feedback. The controller's effectiveness is validated through both numerical and experimental systems, with results meeting the conditions specified in the main theorem. Comparative analysis highlights the controller's highly satisfactory performance and its advantages. This research offers a promising approach to adaptive control for discrete-time systems with non-strict dynamics, providing practical solutions for systems with unknown dynamics and indeterminate system order.
{"title":"Pure-Output Feedback Controller for a Class of Unknown Nonaffine Discrete-Time Systems with Indeterminate Order and Non-Strict Dynamics","authors":"Chidentree Treesatayapun","doi":"10.1109/JAS.2025.125270","DOIUrl":"https://doi.org/10.1109/JAS.2025.125270","url":null,"abstract":"The paper presents an adaptive controller formulated for a class of nonaffine discrete-time systems with non-strict forms and unknown dynamics. The controller operates based solely on the measured output, thus obviating the need for knowledge of the physical order of the controlled plant. Utilizing an ideal solution and equivalent dynamics, the approach integrates an adaptive network with feedback and robust controllers to establish a closed-loop system. A learning law is derived under practical conditions of the designed parameters, ensuring effective closed-loop performance based on pure-output feedback. The controller's effectiveness is validated through both numerical and experimental systems, with results meeting the conditions specified in the main theorem. Comparative analysis highlights the controller's highly satisfactory performance and its advantages. This research offers a promising approach to adaptive control for discrete-time systems with non-strict dynamics, providing practical solutions for systems with unknown dynamics and indeterminate system order.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 11","pages":"2253-2263"},"PeriodicalIF":19.2,"publicationDate":"2025-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145612127","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}
In this study, a novel observer-based scalable control scheme for large-scale systems (LSSs) with several interconnected subsystems is explored. Firstly, a scalable observer-based controller is designed to address complex situations where system states are difficult to measure directly. Secondly, unlike the limited cascade and ring topology connections in previous results, this study considers a universal arbitrary topology. Furthermore, it is noteworthy that the plug-and-play (PnP) capability of LSSs is guaranteed thanks to the proposed scalable scheme. Specifically, when subsystems are added or removed, only the controller gains of directly connected neighbors need updating, eliminating the need to redesign the entire system. Moreover, by choosing a Lyapunov-Krasovskii function with a quadratic matrix-valued polynomial, sufficient conditions are deduced to guarantee the global exponential stability with the desired extended dissipative performance for the resulting LSSs. Finally, the effectiveness of the employed scheme is verified by numerical and microgrid examples.
{"title":"Extended Dissipative Observer-Based Plug-and-Play Control for Large-Scale Interconnected Systems","authors":"Xiaohui Hu;Chen Peng;Hao Shen;Engang Tian","doi":"10.1109/JAS.2025.125360","DOIUrl":"https://doi.org/10.1109/JAS.2025.125360","url":null,"abstract":"In this study, a novel observer-based scalable control scheme for large-scale systems (LSSs) with several interconnected subsystems is explored. Firstly, a scalable observer-based controller is designed to address complex situations where system states are difficult to measure directly. Secondly, unlike the limited cascade and ring topology connections in previous results, this study considers a universal arbitrary topology. Furthermore, it is noteworthy that the plug-and-play (PnP) capability of LSSs is guaranteed thanks to the proposed scalable scheme. Specifically, when subsystems are added or removed, only the controller gains of directly connected neighbors need updating, eliminating the need to redesign the entire system. Moreover, by choosing a Lyapunov-Krasovskii function with a quadratic matrix-valued polynomial, sufficient conditions are deduced to guarantee the global exponential stability with the desired extended dissipative performance for the resulting LSSs. Finally, the effectiveness of the employed scheme is verified by numerical and microgrid examples.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 11","pages":"2207-2217"},"PeriodicalIF":19.2,"publicationDate":"2025-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145612172","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 paper considers adaptive event-triggered stabilization for a class of uncertain time-varying nonlinear systems. Remarkably, the systems contain intrinsic time-varying unknown parameters which are allowed to be non-differentiable and in turn can be fast-varying. Moreover, the systems admit unknown control directions. To counteract the different uncertainties, more than one compensation mechanism has to be incorporated. However, in the context of event-triggered control, ensuring the effectiveness of these compensation mechanisms under reduced execution necessitates delicate design and analysis. This paper proposes a tight and powerful strategy for adaptive event-triggered control (ETC) by integrating the state-of-the-art adaptive techniques. In particular, the strategy substantially mitigates the conservatism caused by repetitive inequality-based treatments of uncertainties. Specifically, by leveraging the congelation-of-variables method and tuning functions, the conservatism in the treatment of the fast-varying parameters is significantly reduced. With multiple Nussbaum functions employed to handle unknown control directions, a set of dynamic compensations is designed to counteract unknown amplitudes of control coefficients without relying on inequality-based treatments. Moreover, a dedicated dynamic compensation is introduced to deal with the control coefficient coupled with the execution error, based on which a relative-threshold event-triggering mechanism (ETM) is rigorously validated. It turns out that the adaptive event-triggered controller achieves the closed-loop convergence while guaranteeing a uniform lower bound for inter-execution times. Simulation results verify the effectiveness and superiority of the proposed strategy.
{"title":"Adaptive Event-Triggered Control of Time-Varying Nonlinear Systems: A Tight and Powerful Strategy","authors":"Lei Chu;Yungang Liu","doi":"10.1109/JAS.2025.125786","DOIUrl":"https://doi.org/10.1109/JAS.2025.125786","url":null,"abstract":"This paper considers adaptive event-triggered stabilization for a class of uncertain time-varying nonlinear systems. Remarkably, the systems contain intrinsic time-varying unknown parameters which are allowed to be non-differentiable and in turn can be fast-varying. Moreover, the systems admit unknown control directions. To counteract the different uncertainties, more than one compensation mechanism has to be incorporated. However, in the context of event-triggered control, ensuring the effectiveness of these compensation mechanisms under reduced execution necessitates delicate design and analysis. This paper proposes a tight and powerful strategy for adaptive event-triggered control (ETC) by integrating the state-of-the-art adaptive techniques. In particular, the strategy substantially mitigates the conservatism caused by repetitive inequality-based treatments of uncertainties. Specifically, by leveraging the congelation-of-variables method and tuning functions, the conservatism in the treatment of the fast-varying parameters is significantly reduced. With multiple Nussbaum functions employed to handle unknown control directions, a set of dynamic compensations is designed to counteract unknown amplitudes of control coefficients without relying on inequality-based treatments. Moreover, a dedicated dynamic compensation is introduced to deal with the control coefficient coupled with the execution error, based on which a relative-threshold event-triggering mechanism (ETM) is rigorously validated. It turns out that the adaptive event-triggered controller achieves the closed-loop convergence while guaranteeing a uniform lower bound for inter-execution times. Simulation results verify the effectiveness and superiority of the proposed strategy.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 11","pages":"2194-2206"},"PeriodicalIF":19.2,"publicationDate":"2025-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145612126","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}
Dear Editor, This letter deals with distributed resource allocation (DRA) over multiple interacting coalitions, where conflicts of interest may arise due to the relevance of one coalition's decision to other coalitions' benefits. To address this challenge, a new model called intra-independent resource allocation game (IIRAG) is formulated under the framework of multi-coalition games. A new DRA algorithm is developed, which draws on techniques of variable replacement and leader-following consensus. The proposed algorithm ensures linear convergence of the collective decision to the Nash equilibrium (NE) of the IIRAG, as well as satisfaction of the resource constraint throughout the iteration process. Numerical simulations validate the effectiveness of the proposed approach.
{"title":"Intra-Independent Distributed Resource Allocation Game","authors":"Jialing Zhou;Guanghui Wen;Yuezu Lv;Tao Yang;Guanrong Chen","doi":"10.1109/JAS.2023.123906","DOIUrl":"https://doi.org/10.1109/JAS.2023.123906","url":null,"abstract":"Dear Editor, This letter deals with distributed resource allocation (DRA) over multiple interacting coalitions, where conflicts of interest may arise due to the relevance of one coalition's decision to other coalitions' benefits. To address this challenge, a new model called intra-independent resource allocation game (IIRAG) is formulated under the framework of multi-coalition games. A new DRA algorithm is developed, which draws on techniques of variable replacement and leader-following consensus. The proposed algorithm ensures linear convergence of the collective decision to the Nash equilibrium (NE) of the IIRAG, as well as satisfaction of the resource constraint throughout the iteration process. Numerical simulations validate the effectiveness of the proposed approach.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 10","pages":"2150-2152"},"PeriodicalIF":19.2,"publicationDate":"2025-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11224454","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145428888","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}
This paper addresses the parallel control of autonomous surface vehicles subject to external disturbances, state constraints, and input constraints in complex ocean environments with multiple obstacles. A safety-certified parallel model predictive control scheme with collision-avoiding capability is proposed for autonomous surface vehicles in the framework of parallel control. Specifically, an extended state observer is designed by leveraging historical and real-time data for concur-rent learning to map the motion of autonomous surface vehicles from its physical system to its artificial counterpart. A parallel model predictive control law is developed on the basis of the artificial system for both physical and artificial autonomous surface vehicles to realize virtual-physical tracking control of vehicles subject to state and input constraints. To ensure safety, high-order discrete control barrier functions are encoded in the parallel model predictive control law as safety constraints such that collision avoidance with obstacles can be achieved. A receding-horizon constrained optimization problem is constructed with the safety constraints encoded by control barrier functions for parallel model predictive control of autonomous surface vehicles and solved via neurodynamic optimization with projection neural networks. The effectiveness and characteristics of the proposed method are demonstrated via simulations for the safe trajectory tracking and automatic berthing of autonomous surface vehicles.
{"title":"Safety-Certified Parallel Model Predictive Control of Autonomous Surface Vehicles via Neurodynamic Optimization","authors":"Guanghao Lyu;Zhouhua Peng;Jun Wang","doi":"10.1109/JAS.2024.124980","DOIUrl":"https://doi.org/10.1109/JAS.2024.124980","url":null,"abstract":"This paper addresses the parallel control of autonomous surface vehicles subject to external disturbances, state constraints, and input constraints in complex ocean environments with multiple obstacles. A safety-certified parallel model predictive control scheme with collision-avoiding capability is proposed for autonomous surface vehicles in the framework of parallel control. Specifically, an extended state observer is designed by leveraging historical and real-time data for concur-rent learning to map the motion of autonomous surface vehicles from its physical system to its artificial counterpart. A parallel model predictive control law is developed on the basis of the artificial system for both physical and artificial autonomous surface vehicles to realize virtual-physical tracking control of vehicles subject to state and input constraints. To ensure safety, high-order discrete control barrier functions are encoded in the parallel model predictive control law as safety constraints such that collision avoidance with obstacles can be achieved. A receding-horizon constrained optimization problem is constructed with the safety constraints encoded by control barrier functions for parallel model predictive control of autonomous surface vehicles and solved via neurodynamic optimization with projection neural networks. The effectiveness and characteristics of the proposed method are demonstrated via simulations for the safe trajectory tracking and automatic berthing of autonomous surface vehicles.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 10","pages":"2056-2066"},"PeriodicalIF":19.2,"publicationDate":"2025-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145428891","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}
The purpose of infrared and visible image fusion is to create a single image containing the texture details and significant object information of the source images, particularly in challenging environments. However, existing image fusion algorithms are generally suitable for normal scenes. In the hazy scene, a lot of texture information in the visible image is hidden, the results of existing methods are filled with infrared information, resulting in the lack of texture details and poor visual effect. To address the aforementioned difficulties, we propose a haze-free infrared and visible fusion method, termed HaIVFusion, which can eliminate the influence of haze and obtain richer texture information in the fused image. Specifically, we first design a scene information restoration network (SIRNet) to mine the masked texture Information in visible images. Then, a denoising fusion network (DFNet) is designed to integrate the features extracted from infrared and visible images and remove the influence of residual noise as much as possible. In addition, we use color consistency loss to reduce the color distortion resulting from haze. Further-more, we publish a dataset of hazy scenes for infrared and visible image fusion to promote research in extreme scenes. Extensive experiments show that HaIVFusion produces fused images with increased texture details and higher contrast in hazy scenes, and achieves better quantitative results, when compared to state-of-the-art image fusion methods, even combined with state-of-the-art dehazing methods.
{"title":"HaIVFusion: Haze-Free Infrared and Visible Image Fusion","authors":"Xiang Gao;Yongbiao Gao;Aimei Dong;Jinyong Cheng;Guohua Lv","doi":"10.1109/JAS.2024.124926","DOIUrl":"https://doi.org/10.1109/JAS.2024.124926","url":null,"abstract":"The purpose of infrared and visible image fusion is to create a single image containing the texture details and significant object information of the source images, particularly in challenging environments. However, existing image fusion algorithms are generally suitable for normal scenes. In the hazy scene, a lot of texture information in the visible image is hidden, the results of existing methods are filled with infrared information, resulting in the lack of texture details and poor visual effect. To address the aforementioned difficulties, we propose a haze-free infrared and visible fusion method, termed HaIVFusion, which can eliminate the influence of haze and obtain richer texture information in the fused image. Specifically, we first design a scene information restoration network (SIRNet) to mine the masked texture Information in visible images. Then, a denoising fusion network (DFNet) is designed to integrate the features extracted from infrared and visible images and remove the influence of residual noise as much as possible. In addition, we use color consistency loss to reduce the color distortion resulting from haze. Further-more, we publish a dataset of hazy scenes for infrared and visible image fusion to promote research in extreme scenes. Extensive experiments show that HaIVFusion produces fused images with increased texture details and higher contrast in hazy scenes, and achieves better quantitative results, when compared to state-of-the-art image fusion methods, even combined with state-of-the-art dehazing methods.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 10","pages":"2040-2055"},"PeriodicalIF":19.2,"publicationDate":"2025-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145428895","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}
Ao Guo;Jingwei Ge;Daniel Horti;Dimitar Filev;Fei-Yue Wang
Driven by advancements in artificial intelligence technologies such as deep learning, core intelligent driving technologies like advanced driver assistance systems (ADAS) have made significant advances. Some advanced ADAS systems, particularly in highway scenarios, have reached or even surpassed human drivers in terms of precision and reliability [1]. This mainstream development path is based on a replacement paradigm, whose central goal is to relieve human drivers of monotonous, repetitive tasks such as highway commuting, maximizing traffic efficiency and safety [2]. This paradigm aims to replace error-prone human operators with a tireless, consistent machine intelligence.
{"title":"Rethinking Off-Road ADAS: A Perspective on the Generative Co-Pilot Paradigm","authors":"Ao Guo;Jingwei Ge;Daniel Horti;Dimitar Filev;Fei-Yue Wang","doi":"10.1109/JAS.2025.125891","DOIUrl":"https://doi.org/10.1109/JAS.2025.125891","url":null,"abstract":"Driven by advancements in artificial intelligence technologies such as deep learning, core intelligent driving technologies like advanced driver assistance systems (ADAS) have made significant advances. Some advanced ADAS systems, particularly in highway scenarios, have reached or even surpassed human drivers in terms of precision and reliability [1]. This mainstream development path is based on a replacement paradigm, whose central goal is to relieve human drivers of monotonous, repetitive tasks such as highway commuting, maximizing traffic efficiency and safety [2]. This paradigm aims to replace error-prone human operators with a tireless, consistent machine intelligence.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 10","pages":"1959-1962"},"PeriodicalIF":19.2,"publicationDate":"2025-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145428892","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}
Chao Cun;Qunting Yang;Zhijun Li;MengChu Zhou;Jianxin Pang
In this paper, a framework of model predictive optimization and control for quadruped whole-body locomotion is presented, which enables dynamic balance and minimizes the control effort. First, we propose a hierarchical control scheme consisting of two modules. The first layer is to find an optimal ground reaction force (GRF) by employing inner model predictive control (MPC) along a full motor gait cycle, ensuring the minimal energy consumption of the system. Based on the output GRF of inner layer, the second layer is designed to prioritize tasks for motor execution sequentially using an outer model predictive control. In inner MPC, an objective function about GRF is designed by using a model with relatively long time horizons. Then a neural network solver is used to obtain the optimal GRF by minimizing the objective function. By using a two-layered MPC architecture, we design a hybrid motion/force controller to handle the impedance of leg joints and robotic uncertainties including external perturbation. Finally, we perform extensive experiments with a quadruped robot, including the crawl and trotting gaits, to verify the proposed control framework.
{"title":"Model Predictive Optimization and Control of Quadruped Whole-Body Locomotion","authors":"Chao Cun;Qunting Yang;Zhijun Li;MengChu Zhou;Jianxin Pang","doi":"10.1109/JAS.2024.125073","DOIUrl":"https://doi.org/10.1109/JAS.2024.125073","url":null,"abstract":"In this paper, a framework of model predictive optimization and control for quadruped whole-body locomotion is presented, which enables dynamic balance and minimizes the control effort. First, we propose a hierarchical control scheme consisting of two modules. The first layer is to find an optimal ground reaction force (GRF) by employing inner model predictive control (MPC) along a full motor gait cycle, ensuring the minimal energy consumption of the system. Based on the output GRF of inner layer, the second layer is designed to prioritize tasks for motor execution sequentially using an outer model predictive control. In inner MPC, an objective function about GRF is designed by using a model with relatively long time horizons. Then a neural network solver is used to obtain the optimal GRF by minimizing the objective function. By using a two-layered MPC architecture, we design a hybrid motion/force controller to handle the impedance of leg joints and robotic uncertainties including external perturbation. Finally, we perform extensive experiments with a quadruped robot, including the crawl and trotting gaits, to verify the proposed control framework.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 10","pages":"2103-2114"},"PeriodicalIF":19.2,"publicationDate":"2025-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145428896","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：