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第43届中国控制会议

作者： Xiang Li Weiwei Shang Shuang Cong Department of Automation University of Science and Technology of China

Reinforcement learning(RL) algorithms have emerged as potent tools for solving sequential decision-making problems. These model-free methods have garnered significant achievements. However, their high sample complexity poses a substantial obstacle, particularly in real-world robotic applications. Consequently, model-based approaches are often preferred to mitigate this complexity, despite their limitations due to inherent model biases in asymptotic performance. Traditional dynamic control methodologies enable the design of precise, compliant, and energy-efficient robotic control. Their primary limitation lies in the requirement for accurate analytical dynamic models. In this research, we address this gap by employing an ensemble of probabilistic neural networks(EPNNs) to learn dynamic models of robots directly from sensor data, integrating system uncertainty considerations. Drawing inspiration from the traditional augmented PD(APD) control method, we propose a novel model-based reinforcement learning(MBRL) algorithm, which employs residual learning for both offline optimization and online planning of the policy. Trajectory tracking experiments are conducted using a Baxter robot in both Pybullet simulation and realworld environments, which demonstrate significant improvements in tracking accuracy with our MBRL method. Furthermore,the robustness of this approach is also validated through various load experiments.

关键词： Model-based reinforcement learning Augmented PD Probabilistic neural networks robotic control

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Robust control of uncertain robotic systems:An adaptive friction compensation approach

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Science China(Technological Sciences) 2021年第6期64卷 1228-1237页

作者： WANG QiShao ZHUANG Han DUAN ZhiSheng WANG QingYun Department of Dynamics and control Beihang UniversityBeijing 100191China College of Engineering Peking UniversityBeijing 100871China

This paper solves the robust control problem of robotic manipulator systems with uncertain dynamics by friction compensation approach. A weighting factor is introduced to distinguish the role of friction in control process by comparing the directions of sliding vector and friction. Utilizing the weighting factor, model-based and model-free adaptive friction compensation controllers are designed to achieve asymptotical tracking of the desired joint-space trajectory according to the knowledge of friction. The damping property of friction is fully used to improve the control performance by compensating the friction harmful for the stability, and on the other hand, utilizing the beneficial friction. Numerical simulations are given to demonstrate the control performance of the proposed approach.

关键词： robotic control friction compensation adaptive control robust control

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维普期刊数据库

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Tuning Synaptic Connections Instead of Weights by Genetic Algorithm in Spiking Policy Network

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Machine Intelligence Research 2024年第5期21卷 906-918页

作者： Duzhen Zhang Tielin Zhang Shuncheng Jia Qingyu Wang Bo Xu School of Artificial Intelligence University of Chinese Academy of SciencesBeijing100049China Institute of Automation Chinese Academy of Sciences(CAS)Beijing100190China Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences(CAS)Shanghai200031China

Learning from interaction is the primary way that biological agents acquire knowledge about their environment and *** deep reinforcement learning(DRL)explores a computational approach to learning from interaction and has made significant progress in solving various ***,despite its power,DRL still falls short of biological agents in terms of energy *** the underlying mechanisms are not fully understood,we believe that the integration of spiking communication between neurons and biologically-plausible synaptic plasticity plays a prominent role in achieving greater energy *** this biological intuition,we optimized a spiking policy network(SPN)using a genetic algorithm as an energy-efficient alternative to *** SPN mimics the sensorimotor neuron pathway of insects and communicates through event-based *** by biological research showing that the brain forms memories by creating new synaptic connections and rewiring these connections based on new experiences,we tuned the synaptic connections instead of weights in the SPN to solve given *** results on several robotic control tasks demonstrate that our method can achieve the same level of performance as mainstream DRL methods while exhibiting significantly higher energy efficiency.

关键词： Spiking neural networks genetic evolution bio-inspired learning agent&cognitive architectures robotic control

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Software for Small-scale robotics: A Review

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International Journal of Automation and computing 2018年第5期15卷 515-524页

作者： Tobias Tiemerding Sergej Fatikow Department of Computing Science Division Microrobotics and Control Engineering (AMiR) University of Oldenburg OFFIS–Institute for Information Technology R&D Division Automation and Integration Technology (AIT)

In recent years, a large number of relatively advanced and often ready-to-use robotic hardware components and systems have been developed for small-scale use. As these tools are mature, there is now a shift towards advanced applications. These often require automation and demand reliability, efficiency and decisional autonomy. New software tools and algorithms for artificial intelligence（AI） and machine learning（ML） can help here. However, since there are many software-based control approaches for small-scale robotics, it is rather unclear how these can be integrated and which approach may be used as a starting point. Therefore, this paper attempts to shed light on existing approaches with their advantages and disadvantages compared to established requirements. For this purpose, a survey was conducted in the target group. The software categories presented include vendor-provided software, robotic software frameworks（RSF）, scientific software and in-house developed software（IHDS）. Typical representatives for each category are described in detail, including Smar Act precision tool commander, Math Works Matlab and national instruments Lab VIEW, as well as the robot operating system（ROS）. The identified software categories and their representatives are rated for end user satisfaction based on functional and non-functional requirements, recommendations and learning curves. The paper concludes with a recommendation of ROS as a basis for future work.

关键词： robotic control software engineering micro/nano robotics artificial intelligence (AI) machine learning (ML) open

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A Cooperative Autonomous Mechatronic System for Fault Tolerant control of Material Handling Operations

A Cooperative Autonomous Mechatronic System for Fault Tolera...

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第11届粤港机械电子工程技术与应用研讨会

作者： Raymond C.M.Chan Henry Y.K.Lau Department of Industrial and manufacturing Systems Engineering The University of Hong Kong Pokfulam Road Hong KongPRC

＜正＞Sustainable control of a cooperative autonomous mechatronic system is a challenging undertaking and it is one of the focuses which many researchers have been working on for *** this paper,we introduce the design of an immune-inspired decentralized sustainability control methodology for cooperative autonomous mechatronic system such as multi-agent systems （MAS） to optimize the overall performance in terns of longevity through the trade-off between maintenance activity and task *** resulting system will have favorable characteristics of good fault tolerant as well as task achievement.A design of a multi-robot system that is amenable for the experimentation of the proposed methodology is presented together with a simulation platform for investigating its performance.

关键词： multi-agent system artificial immune systems robotic control

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