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A New Recursive Composite Adaptive controller for Robot Manipulators

Space(Science & Technology) 2021年第1期2021卷 77-83页

作者： Jianfei Li Yaobing Wang Zhiyong Liu Xin Jing Chengwei Hu The State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Beijing Key Laboratory of Intelligent Space Robotic System Technology and Applications Beijing Institute of Spacecraft System EngineeringBeijing 100094China State Key Laboratory of Robotics and System Harbin Institute of TechnologyHarbin 150001China

In this paper,a new recursive implementation of composite adaptive control for robot manipulators is *** investigate the recursive composite adaptive algorithm and prove the stability directly based on the Newton-Euler equations in matrix form,which,to our knowledge,is the first result on this point in the *** proposed algorithm has an amount of computation O(n),which is less than any existing similar algorithms and can satisfy the computation need of the complicated multidegree *** manipulator of the Chinese Space Station is employed as a simulation example,and the results verify the effectiveness of this proposed recursive algorithm.

关键词： Robot manipulators robot

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Coordinated Motion control for a Six-wheel-drive Skid-steering Mobile Robot

Coordinated Motion Control for a Six-wheel-drive Skid-steeri...

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2021 China Automation Congress, CAC 2021

作者： Xu, Yongkang Wang, Shoukun Zhao, Hongqiang Chen, Zhihua State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing China State Key Laboratory of High Performance and Complex Manufacturing Central South University Changsha China

ISBN: (纸本)9781665426473

A speed consensus control strategy for a six-wheel-drive skid-steering mobile robot was proposed based on a hierarchical controller in a complex environment with unknown disturbances. The effective method of reliable speed consensus control strategy should consider some disturbances, such as the internal-robot assembly error, which including instability of motor control system and external physical interference in robot dynamic system. In this paper, a driving anti-slip control system with PID and the stability control system with sliding mode by variable structure is designed and integrated to elevate the stability of the robot. And, a G-vector control algorithm based on sliding mode control is proposed to reduce the yaw angle as much as possible during the process of differential steering of robots. The effectiveness of the proposed control strategy is verified by an example of a *** results show that the method has good control performance in control accuracy and stability. © 2021 IEEE

关键词： Motion control

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Interactive multiobjective evolutionary algorithm based on decomposition and compression

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Science China(Information Sciences) 2021年第10期64卷 166-181页

作者： Lu CHEN Bin XIN Jie CHEN School of Automation Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems

Many real-world optimization problems involve multiple conflicting objectives. Such problems are called multiobjective optimization problems(MOPs). Typically, MOPs have a set of so-called Pareto optimal solutions rather than one unique optimal solution. To assist the decision maker(DM) in finding his/her most preferred solution, we propose an interactive multiobjective evolutionary algorithm(MOEA)called iDMOEA-εC, which utilizes the DM's preferences to compress the objective space directly and progressively for identifying the DM's preferred region. The proposed algorithm employs a state-of-the-art decomposition-based MOEA called DMOEA-εC as the search engine to search for solutions. DMOEA-εC decomposes an MOP into a series of scalar constrained subproblems using a set of evenly distributed upper bound vectors to approximate the entire Pareto front. To guide the population toward only the DM's preferred part on the Pareto front, an adaptive adjustment mechanism of the upper bound vectors and two-level feasibility rules are proposed and integrated into DMOEA-εC to control the spread of the population. To ease the DM's burden, only a small set of representative solutions is presented in each interaction to the DM,who is expected to specify a preferred one from the set. Furthermore, the proposed algorithm includes a two-stage selection procedure, allowing to elicit the DM's preferences as accurately as possible. To evaluate the performance of the proposed algorithm, it was compared with other interactive MOEAs in a series of experiments. The experimental results demonstrated the superiority of iDMOEA-εC over its competitors.

关键词： multiobjective optimization interactive decision making preference incorporation decomposition compression

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Electric Dual Variable Pump controlled Hydraulic Motor

Electric Dual Variable Pump Controlled Hydraulic Motor

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Chinese control Conference (CCC)

作者： Jianmin Yang Junzheng Wang Shanshuai Niu State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing P. R. China

The combined control of variable speed and variable displacement is a new type of volume control with high efficiency and fast response. However, due to the inherent nonlinearity of multiplication, it brings certain difficulties to the control. The electric double-variable pump[1] is a dual-input single-output system, and it is a nonlinear system[2]. It is necessary to linearize the system or use a nonlinear control method to control and solve the control problem of the system. In this paper, an intelligent control rule is proposed for the nonlinear problem of double input and single output. Through backstepping design[3], the nonlinear system is transformed into multiple linear subsystems. Then the original system is turned into two independent subsystems with single input and single output, which are controlled separately. The co-simulation platform based on AMESIM and Simulink[4] has been verified and compared with a single PID control algorithm to simulate the step response and sinusoidal tracking performance of the system. The results show that the response speed of the system has been greatly improved.

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An Improved Task Planning Method for Unmanned Swarms with Coupled Physical and Logical Constraints

An Improved Task Planning Method for Unmanned Swarms with Co...

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2024 IEEE International Conference on Unmanned systems, ICUS 2024

作者： Chen, Xiubin Zhang, Lei Xu, Fang Yao, Weiran School of Astronautics Harbin Institude of Technology Harbin China Wuhan 2nd Ship Design and Research Institute Wuhan China Marine Military Intelligence Innovation Institute CSSC Systems Engineering Research Institute Beijing China National Key Laboratory of Complex System Control and Intelligent Agent Cooperation Beijing China

ISBN: (纸本)9798350384185

Traditional task allocation methods for unmanned swarm systems ignore the effects of actual paths, resulting in estimation accuracy *** paper formulates task planning problem by incorporating physical and logical constraints, and establishes an integrated framework of task allocation and path ***-based search method is used to address path planning with physical constraints.A genetic algorithm is employed to solve multi-traveling salesman allocation problem.A bounded suboptimal optimization, a data dictionary, and an island model are introduced to accelerate the convergence speed of the genetic *** experiments verify that compared to the decoupled task planning methods, the proposed method improves task execution efficiency and remains adaptability to various complex spatial maps. © 2024 IEEE.

关键词： Genetic algorithms

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Adaptive SOM neural network algorithm 3

Adaptive SOM neural network algorithm

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3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing, AIAHPC 2023

作者： Wang, Xin Yan, Taishan School of Information Science and Engineering Hunan Institute of Science and Technology Hunan Yueyang China Hunan Institute of Science and Technology School of Information Science and Engineering China Key Laboratory of Hunan Province on Intelligent Control and Optimization of Complex Industrial Logistics System Hunan Yueyang China

ISBN: (数字)9781510666580

ISBN: (纸本)9781510666573

In order to improve the performance of traditional SOM model, an adaptive SOM neural network algorithm is proposed. By introducing the error square sum solution formula, the clustering error of the model sub-data set is calculated by using the method of cutting the data set, and the change trend of the clustering error is quickly obtained. Then the learning rate of the SOM network is adaptively adjusted according to this, and the convergence speed is accelerated on the basis of ensuring that the network is fully trained, thereby improving the performance of the SOM network. © 2023 SPIE.

关键词： Errors

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Full Attention Tracker: A Good Combination of Pixel-Level and Region-Level Cross-Correlation

Full Attention Tracker: A Good Combination of Pixel-Level an...

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第42届中国控制会议

作者： Yuxuan Wang Liping Yan Zihang Feng Yuanqing Xia Bo Xiao Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology School of Artificial Intelligence Beijing University of Posts and Telecommunications

The tracker based on Siamese neural network is currently a technical method with high accuracy in the tracking *** the introduction of transformer in the visual tracking field, the attention mechanism has gradually emerged in tracking ***, due to the characteristics of attention operation, Transformer usually has slow convergence speed, and its pixel-level correlation discrimination in tracking is more likely to lead to overfitting, which is not conducive to long-term tracking. A brand new framework FAT was designed, which is the improvement of MixFormer. The operation for simultaneous feature extraction and target information integration in MixFormer is retained, and the Mixing block is introduced to suppress the background as much as possible before the information interaction. In addition, a new operation is designed: the result of region-level crosscorrelation is used as a guidance to help the learning of pixel-level cross-correlation in attention, thereby accelerating the model convergence speed and enhancing the model generalization. Finally, a joint loss function is designed to further improve the accuracy of the model. Experiments show that the presented tracker achieves excellent performance on five benchmark datasets.

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A Modified RC and Diode Co-Triggered ESD Clamp Circuit 1

A Modified RC and Diode Co-Triggered ESD Clamp Circuit

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1st Annual International EOS/ESD Symposium on Design and system, IEDS 2020

作者： Yang, Zhaonian Zhang, Yibo Mao, Pan Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an710048 China

ISBN: (纸本)9781585373284

This paper presents a new ESD clamp circuit which is co-triggered by RC and diode. It is immune to the false triggering caused by fast power-up events due to the diode detection mechanism. With the addition of a common source amplifier, the detection signal is enhanced, and the clamp device is turned on more quickly. The proposed circuit is designed in a 0.18 μm 1.8-V CMOS process and verified by numerical simulation. The characteristics and improvements of the new circuit are discussed. © 2021 EOS/ESD ASSOCIATION INC.

关键词： Diodes

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decision-making and Planning Framework with Prediction-Guided Strategy Tree Search Algorithm for Uncontrolled Intersections 25

Decision-making and Planning Framework with Prediction-Guide...

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25th IEEE International Conference on intelligent Transportation systems, ITSC 2022

作者： Zhang, Ting Fu, Mengyin Song, Wenjie Yang, Yi Wang, Meiling Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing 100081 China Nanjing University of Science and Technology Nanjing 210014 China

ISBN: (数字)9781665468800

ISBN: (纸本)9781665468817

Uncontrolled intersections are important and challenging traffic scenarios for autonomous vehicles. Vehicles not only need to avoid collisions with dynamic vehicles instantaneously but also predict their behavior then make long-term decisions in reaction. To solve this problem, we propose a cooperative framework composed of a Primary Driver (PD) for motion planning and a Subordinate Driver (SD) for decision-making. SD is essentially the combination of a prediction module and a high-level behavior planner, which develops a prediction-guided strategy tree to determine the optimal action sequence. Especially, under the guidance of the prediction results, the tree branches are evaluated in security metrics, then get trimmed in action and observation space to reduce the dimensional complexity. With the assistance of SD, PD works as a collision checker and a low-level motion planner to generate a safe and smooth trajectory. We use the INTERACTION dataset to validate our method and achieve more than 90% success rate with efficiency improvement in various situations. © 2022 IEEE.

关键词： Uncertainty decision making Prediction algorithms Planning Trajectory complexity theory Behavioral sciences

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decision-Making and Path Planning for Highway Autonomous Driving Based on Spatio-Temporal Lane-Change Gaps

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IEEE systems Journal 2022年第2期16卷 3249-3259页

作者： Feng, Zhiqi Song, Wenjie Fu, Mengyin Yang, Yi Wang, Meiling Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing100081 China

Safe and efficient decision-making and path planning is a challenging problem for autonomous driving in highway because of numerous dynamic vehicles around the ego-vehicle. For ego-vehicle driving on structured roads, behavioral decisions tend to pay more attention to the relative distribution of its surrounding vehicles, while path planning based on the decision requires more detailed consideration of vehicle model and lane curvature. Therefore, these two modules are separated in our article and executed successively in different coordinate systems. Specifically, we create a decision topology for lane changing or following decision based on spatio-temporal lane-change gaps in relative moving coordinate, which consists of the node representing the gap and the edges representing the connectivity between two gaps. Combining the decision topology with the behaviors of surrounding dynamic vehicles, a local cost map is generated and the target decision gap is selected. Once the decision is done, a smooth and safe trajectory from the ego-vehicle to the target decision gap, that satisfies the constraints of acceleration and curvature, is obtained in the geodetic coordinate through polynomial trajectory generation. The real-time performance and effectiveness of this method were verified in the dynamic high-speed interaction scenarios. © 2007-2012 IEEE.

关键词： decision making

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