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Saturation Functions Based Control for Series Elastic Actuator with Feedback Linearization

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Saturation Functions Based Control for Series Elastic Actuat...

第40届中国控制会议

作者： Tianjun Zha Peiwen Li Yucheng Xu Xinru Gong Lei Sun institute of robotics and automatic information system Nankai University Tianjin Key Laboratory of Intelligent robotics

In this paper, a saturation functions based control law with feedback linearization（FL） is proposed for the trajectory tracking control of a series elastic actuator（SEA） system. A feedback-based stabilisation-bounded control action shaped by saturation functions is designed. The controller consists of two parts, one is a feedforward controller which guarantees high order tracking errors converge to a small region, the other is a feedback controller which brings the entire state errors of the SEA system asymptotically converge to zero. In the second part of controller, the saturation function is not unique. Closed-loop stability is established by the Lyapunov method and La Salle s invariance principle. The effectiveness of the proposed control strategy is verified by simulations and experiments based on a SEA-driven robot arm.

关键词： Series Elastic Actuator Feedback Linearization Saturation Functions Tracking Control

Autonomous driving: cognitive construction and situation understanding

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Science China(information Sciences) 2019年第8期62卷 75-101页

作者： Shitao CHEN Zhiqiang JIAN Yuhao HUANG Yu CHEN Zhuoli ZHOU Nanning ZHENG institute of Artificial Intelligence and robotics Xi'an Jiaotong University National Engineering Laboratory for Visual information Processing and Applications

Autonomous vehicle is a kind of typical complex artificial intelligence system. In current research of autonomous driving, the most widely adopted technique is to use a basic framework of serial information processing and computations, which consists of four modules: perception, planning, decision-making, and control. However, this framework based on data-driven computing performs low computational efficiency,poor environmental understanding and self-learning ability. A neglected problem has long been how to understand and process environmental perception data from the sensors referring to the cognitive psychology level of the human driving process. The key to solving this problem is to construct a computing model with selective attention and self-learning ability for autonomous driving, which is supposed to possess the mechanism of memorizing, inferring and experiential updating, enabling it to cope with traffic scenarios with high noise, dynamic, and randomness. In addition, for the process of understanding traffic scenes,the efficiency of event-related mechanism is more significant than single-attribute scenario perception ***, an effective self-driving method should not be confined to the traditional computing framework of‘perception, planning, decision-making, and control’. It is necessary to explore a basic computing framework that conforms to human driver’s attention, reasoning, learning, and decision-making mechanism with regard to traffic scenarios and build an autonomous system inspired by biological intelligence. In this article, we review the basic methods and main progress in current data-driven autonomous driving technologies, deeply analyze the limitations and major problems faced by related algorithms. Then, combined with authors’ research, this study discusses how to implement a basic cognitive computing framework of self-driving with selective attention and an event-driven mechanism from the basic viewpoint of cognitive science. It furth

关键词： autonomous driving event-driven mechanism cognitive construction situation understanding intuitive reasoning

A Direct Swing Constraint-based Trajectory Planning Method for Underactuated Overhead Cranes

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自动化学报 2014年第11期40卷 2414-2419页

作者： WANG Peng-Cheng FANG Yong-Chun JIANG Zi-Ya institute of robotics and automatic information system (IRAIS) Nankai University Tianjin 300071 China Tianjin Key Laboratory of Intelligent robotics Nankai University Tianjin 300071 China State Key Laboratory of robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang 110016. China

This paper proposes a novel swing constraint-based trajectory planning method for nonlinear overhead crane systems. To enhance the efficiency and security of the transportation process, some desired trajectories are designed to achieve the following merits： 1） leading the trolley to reach the destination sufficiently fast; 2） keeping the payload swing in an acceptable domain; 3） eliminating the residue swing when the trolley stops at the desired position. Specifically, the trajectories are divided into three stages. For each stage, the desired curve of the swing angle is directly constructed in accordance with anti-swing and zero-residual swing requirements, based on which the trolley trajectory is then obtained by analyzing the nonlinear kinematics of the crane system. An optimization mechanism is introduced to make intelligent compromises among the indices of transportation time, maximal swing angle, and so on. Both simulation and experimental results are provided to demonstrate the performance of the proposed direct swing constraint-based trajectory planning method.

关键词：桥式起重机基于约束 Swing 欠驱动规划法非线性运动学载荷摆动轨迹规划

Soft robotics:Morphology and Morphology-inspired Motion Strategy

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IEEE/CAA Journal of automatica Sinica 2021年第9期8卷 1500-1522页

作者： Fan Xu Hesheng Wang Department of Automation Institute of Medical RoboticsKey Laboratory of System Control and Information Processing of Ministry of EducationShanghai Jiao Tong UniversityShanghai 200240China Key Laboratory of Marine Intelligent Equipment and system of Ministry of Education Shanghai Jiao Tong UniversityShanghai 200240China

robotics has aroused huge attention since the *** of the uniqueness that industrial applications exhibit,conventional rigid robots have displayed noticeable limitations,particularly in safe cooperation as well as with environmental ***,scientists have shifted their focus on soft robotics to apply this type of robots more effectively in unstructured *** decades,they have been committed to exploring sub-fields of soft robotics(e.g.,cutting-edge techniques in design and fabrication,accurate modeling,as well as advanced control algorithms).Although scientists have made many different efforts,they share the common goal of enhancing *** presented paper aims to brief the progress of soft robotic research for readers interested in this field,and clarify how an appropriate control algorithm can be produced for soft robots with specific *** paper,instead of enumerating existing modeling or control methods of a certain soft robot prototype,interprets for the relationship between morphology and morphology-dependent motion strategy,attempts to delve into the common issues in a particular class of soft robots,and elucidates a generic solution to enhance their performance.

关键词： Soft continuum manipulator soft gripper soft mobile robot soft robot control method soft robot modeling method soft robotics

Application of Improved Genetic Algorithm in Network Fault Diagnosis Expert system

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Journal of Beijing institute of Technology 2003年第3期12卷 225-229页

作者：苏利敏侯朝桢戴忠健张雅静 Department of automatic Control School of Information Science and TechnologyBeijing Institute of Technology

Knowledge acquisition is the “bottleneck” of building an expert system. Based on the optimization model, an improved genetic algorithm applied to knowledge acquisition of a network fault diagnostic expert system is proposed. The algorithm applies operators such as selection, crossover and mutation to evolve an initial population of diagnostic rules. Especially, a self adaptive method is put forward to regulate the crossover rate and mutation rate. In the end, a knowledge acquisition problem of a simple network fault diagnostic system is simulated, the results of simulation show that the improved approach can solve the problem of convergence better.

关键词： expert system knowledge acquisition fault diagnosis genetic algorithm

Optimization-inspired manual architecture design and neural architecture search

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Science China(information Sciences) 2023年第11期66卷 100-112页

作者： Yibo YANG Zhengyang SHEN Huan LI Zhouchen LIN JD Explore Academy School of Mathematical Sciences Peking University institute of robotics and automatic information systems College of Artificial Intelligence Nankai University Key Lab of General Artificial Intelligence School of Intelligence Science and Technology Peking University institute for Artificial Intelligence Peking University Pazhou Lab

Neural architecture has been a research focus in recent years due to its importance in deciding the performance of deep networks. Representative ones include a residual network(Res Net) with skip connections and a dense network(Dense Net) with dense connections. However, a theoretical guidance for manual architecture design and neural architecture search(NAS) is still lacking. In this paper, we propose a manual architecture design framework, which is inspired by optimization algorithms. It is based on the conjecture that an optimization algorithm with a good convergence rate may imply a neural architecture with good performance. Concretely, we prove under certain conditions that forward propagation in a deep neural network is equivalent to the iterative optimization procedure of the gradient descent algorithm minimizing a cost function. Inspired by this correspondence, we derive neural architectures from fast optimization algorithms,including the heavy ball algorithm and Nesterov’s accelerated gradient descent algorithm. Surprisingly, we find that we can deem the Res Net and Dense Net as special cases of the optimization-inspired *** architectures offer not only theoretical guidance, but also good performances in image recognition on multiple datasets, including CIFAR-10, CIFAR-100, and Image Net. Moreover, we show that our method is also useful for NAS by offering a good initial search point or guiding the search space.

关键词： deep neural network manual architecture design neural architecture search image recognition optimization algorithms learning-based optimization

Nonlinear Online Trajectory Design for Double-Pendulum Overhead Cranes with Time-Varying Rope Lengths

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Nonlinear Online Trajectory Design for Double-Pendulum Overh...

第43届中国控制会议

作者： Yiming Zhao Biao Lu Yongchun Fang institute of robotics and automatic information system Nankai University Shenzhen Research institute of Nankai University

The inputs of practical crane systems are usually the speed signals,which motivates the trajectory planning for overhead cranes. This paper develops a nonlinear online trajectory for the double-pendulum overhead cranes with time-varying rope lengths without considering any linearization or simplification for the model. Output feedback is introduced into the planned trajectory to enhance the antiswing ability. With the proposed trajectory,the trolley displacement and the rope length can converge to the desired values asymptotically while the double-pendulum effect is eliminated,which is proven by Lyapunov techniques and LaSalle's invariance theorem. Numerical simulation is carried out to demonstrate the antiswing capability of the proposed method,especially in the cases of parameter variations and disturbed feedback of the swing angles.

关键词： Double-pendulum overhead crane Time-varying rope length Trajectory planning Antiswing control

Robust hand posture recognition based on RGBD images

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Robust hand posture recognition based on RGBD images

第26届中国控制与决策会议

作者： Liang Dong Hongpeng Wang Ziyi Hao Jingtai Liu institute of robotics and automatic information system Nankai University Tianjin Key Laboratory of Intelligent robotics Nankai University

ISBN: (纸本)9781479937097

This paper proposes a robust hand posture recognition system based on RGBD images. While much research has focused on human body posture recognition, this work investigates skeleton-free hand detection, tracking and posture recognition. This work consists of two different parts. In the first part, we utilize random forest to get pixel detection of hand and mean-shift to track hand based on RGBD images. In the second part, we implemented extraction of different features and RBF support vector machine to recognize multiple hand postures. This system has two advantages: it is skeleton-free and works in wider area;it is more robust by combining depth and color features. At last, we use the posture recognition system to control robots in virtual reality platform.

关键词： Hand posture recognition RGBD images Random forest Support vector machine

基于细胞内应变评估的卵母细胞去核方法及在机器人化克隆中的应用

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Engineering 2023年第5期24卷 73-83,I0002页

作者： Ming-Zhu Sun Yao-Wei Liu Mao-Sheng Cui Qi-Li Zhao Xiang-Fei Zhao Yi-Di Zhang Jing-Jing Huang Gui-Zhang Lu Xin Zhao institute of robotics and automatic information system Tianjin Key Laboratory of Intelligent RoboticsNankai UniversityTianjin 300071China Tianjin Animal Science and Veterinary Research institute Tianjin 300201China institute of Intelligence Technology and Robotic systems Shenzhen Research Institute of Nankai UniversityShenzhen 518063China

自1996年第一只克隆羊诞生,克隆技术就由于其在动物育种方面的巨大潜力而得到了大量关注。体细胞核移植技术是动物克隆的核心,其操作过程非常复杂,不可避免地造成细胞内损伤。通常来说,只有不到1%的重构胚胎能够发育成克隆动物,克隆的... 详细信息

自1996年第一只克隆羊诞生,克隆技术就由于其在动物育种方面的巨大潜力而得到了大量关注。体细胞核移植技术是动物克隆的核心,其操作过程非常复杂,不可避免地造成细胞内损伤。通常来说,只有不到1%的重构胚胎能够发育成克隆动物,克隆的低成功率是制约其广泛应用的主要原因之一。为此,本文提出了一种基于细胞内应变评估的卵母细胞去核方法,来减少体细胞核移植中的潜在细胞内损伤,从而提高克隆成功率。首先,论文根据细胞内速度场计算细胞内应变,进而将内应变作为评价标准来优化去核操作;在此基础上,开发了一套机器人批量体细胞核移植系统,用于实现机器人化克隆。实验结果表明,本文方法将体细胞核移植后的胚胎囊胚率从10.0%提高到20.8%,并成功获得17头克隆小猪,这是世界首批由机器人完成克隆操作获得的克隆动物。对比手动核移植操作,本文将克隆成功率从平均0.73%提升到了2.50%,大幅提升了克隆效率。与此同时,基于细胞内应变的去核方法有望应用于其他生物操作,并建立一种通用的减少细胞内损伤的细胞操作规范。

关键词：动物育种去核方法体细胞核移植克隆动物卵母细胞机器人化动物克隆克隆技术