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An activated variable parameter gradient‐based neural network for time‐variant constrained quadratic programming and its applications

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CAAI Transactions on Intelligence technology 2023年第3期8卷 670-679页

作者： Guancheng Wang Zhihao Hao Haisheng Li Bob Zhang PAMI Research Group Department of Computer and Information ScienceUniversity of MacaoTaipaMacaoChina China Industrial Control Systems Cyber Emergency Response Team BeijingChina Beijing Key Laboratory of Big Data Technology for Food Safety Beijing Technology and Business UniversityBeijingChina

This study proposes a novel gradient‐based neural network model with an activated variable parameter,named as the activated variable parameter gradient‐based neural network(AVPGNN)model,to solve time‐varying constrained quadratic programming(TVCQP)*** with the existing models,the AVPGNN model has the following advantages:(1)avoids the matrix inverse,which can significantly reduce the computing complexity;(2)introduces the time‐derivative of the time‐varying param-eters in the TVCQP problem by adding an activated variable parameter,enabling the AVPGNN model to achieve a predictive calculation that achieves zero residual error in theory;(3)adopts the activation function to accelerate the convergence *** solve the TVCQP problem with the AVPGNN model,the TVCQP problem is transformed into a non‐linear equation with a non‐linear compensation problem function based on the Karush Kuhn Tucker ***,a variable parameter with an activation function is employed to design the AVPGNN *** accuracy and convergence rate of the AVPGNN model are rigorously analysed in ***,numerical experiments are also executed to demonstrate the effectiveness and superiority of the proposed ***,to explore the feasibility of the AVPGNN model,appli-cations to the motion planning of a robotic manipulator and the portfolio selection of marketed securities are illustrated.

关键词： computational intelligence mathematics computing optimisation

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Efficient learning of robust quadruped bounding usingpretrained neural networks

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IET cyber-systems and Robotics 2022年第4期4卷 331-338页

作者： Zhicheng Wang Anqiao Li Yixiao Zheng Anhuan Xie Zhibin Li Jun Wu Qiuguo Zhu Institute of Cyber-Systems and Control Zhejiang UniversityZhejiangChina Intelligent Robot Research Center Zhejiang LabZhejiangChina Department of Computer Science University College LondonLondonUK State Key Laboratory of Industrial Control Technology Zhejiang UniversityZhejiangChina

Bounding is one of the important gaits in quadrupedal locomotion for negotiating *** authors proposed an effective approach that can learn robust bounding gaits more efficiently despite its large variation in dynamic body *** authors first pretrained the neural network(NN)based on data from a robot operated by conventional model-based controllers,and then further optimised the pretrained NN via deep reinforcement learning(DRL).In particular,the authors designed a reward function considering contact points and phases to enforce the gait symmetry and periodicity,which improved the bounding *** NN-based feedback controller was learned in the simulation and directly deployed on the real quadruped robot Jueying Mini successfully.A variety of environments are presented both indoors and outdoors with the authors’*** authors’approach shows efficient computing and good locomotion results by the Jueying Mini quadrupedal robot bounding over uneven *** cover image is based on the Research Article Efficient learning of robust quadruped bounding using pretrained neural networks by Zhicheng Wang et al.,https://***/10.1049/csy2.12062.

关键词： legged locomotion reinforcement learning robot learning

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The TAO of Blockchain Intelligence for Intelligent Web 3.0

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IEEE/CAA Journal of Automatica Sinica 2023年第12期10卷 2183-2186页

作者： Juanjuan Li Fei-Yue Wang IEEE the State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China the State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190China Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China

WE are in an exciting new intelligent era where various Web 3.0 systems emerge and flourish.[1]–[3].In this new epoch,the collaboration of data and knowledge,humans and machines,actual and virtual worlds is undergoing an unprecedented diversification and community-driven transformation,unveiling an open future full of boundless ***,the value of dispersed data extends far beyond passive storage and application.

关键词： Web TAO knowledge

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A Novel Evaluation Strategy to Artificial Neural Network Model Based on Bionics

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Journal of Bionic Engineering 2022年第1期19卷 224-239页

作者： Sen Tian Jin Zhang Xuanyu Shu Lingyu Chen Xin Niu You Wang School of Mathematics and Statistics Hunan Normal UniversityChangsha410081China College of Information Science and Engineering Hunan Normal UniversityChangsha410081China School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China Key Laboratory of Industrial Control Technology Zhejiang UniversityHangzhou310058China Science and Technology on Parallel and Distributed Laboratory College of ComputerNational University of Defense TechnologyChangsha410199China Key Laboratory of Industrial Control Technology Institute of Cyber Systems and ControlZhejiang UniversityHangzhou310027China

With the continuous deepening of Artificial Neural Network(ANN)research,ANN model structure and function are improving towards diversification and ***,the model is more evaluated from the pros and cons of the problem-solving results and the lack of evaluation from the biomimetic aspect of imitating neural networks is not inclusive ***,a new ANN models evaluation strategy is proposed from the perspective of bionics in response to this problem in the ***,four classical neural network models are illustrated:Back Propagation(BP)network,Deep Belief Network(DBN),LeNet5 network,and olfactory bionic model(KIII model),and the neuron transmission mode and equation,network structure,and weight updating principle of the models are analyzed *** analysis results show that the KIII model comes closer to the actual biological nervous system compared with other models,and the LeNet5 network simulates the nervous system in ***,evaluation indexes of ANN are constructed from the perspective of bionics in this paper:small-world,synchronous,and chaotic ***,the network model is quantitatively analyzed by evaluation indexes from the perspective of *** experimental results show that the DBN network,LeNet5 network,and BP network have synchronous *** the DBN network and LeNet5 network have certain chaotic characteristics,but there is still a certain distance between the three classical neural networks and actual biological neural *** KIII model has certain small-world characteristics in structure,and its network also exhibits synchronization characteristics and chaotic *** with the DBN network,LeNet5 network,and the BP network,the KIII model is closer to the real biological neural network.

关键词： Artificial neural network(ANN) Back Propagation(BP)network Deep Belief Network(DBN) LeNet5 network Olfactory bionic model(KIII model) Small world Chaos Synchronous

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Meter-Scale Thin-Walled Structure with Lattice Infill for Fuel Tank Supporting Component of Satellite:Multiscale Design and Experimental Verification

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Computer Modeling in Engineering & Sciences 2024年第1期138卷 201-220页

作者： Xiaoyu Zhang Huizhong Zeng Shaohui Zhang Yan Zhang Mi Xiao Liping Liu Hao Zhou Hongyou Chai Liang Gao Beijing Key Laboratory of Lightweight Multi-Functional Composite Materials and Structures Beijing Institute of TechnologyBeijing100081China Beijing Key Laboratory of Intelligent Space Robotic Systems Technology and Applications Beijing Institute of Spacecraft System EngineeringChina Academy of Space TechnologyBeijing100094China Key Laboratory of Metallurgical Equipment and Control Technology Ministry of EducationWuhan University of Science and TechnologyWuhan430081China State Key Laboratory of Digital Manufacturing Equipment and Technology Huazhong University of Science and TechnologyWuhan430074China

Lightweight thin-walled structures with lattice infill are widely desired in satellite for their high stiffness-to-weight ratio and superior buckling strength resulting fromthe sandwich *** structures can be fabricated bymetallic additive manufacturing technique,such as selective laser melting(SLM).However,the maximum dimensions of actual structures are usually in a sub-meter scale,which results in restrictions on their appliance in aerospace and other *** this work,a meter-scale thin-walled structure with lattice infill is designed for the fuel tank supporting component of the satellite by integrating a self-supporting lattice into the thickness optimization of the *** designed structure is fabricated by SLM of AlSi10Mg and cold metal transfer welding ***-static mechanical tests and vibration tests are both conducted to verify the mechanical strength of the designed large-scale lattice thin-walled *** experimental results indicate that themeter-scale thin-walled structure with lattice infill could meet the dimension and lightweight requirements of most spacecrafts.

关键词： Thin-walled structure lattice infill supporting component selective laser melting satellite

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BEV-ODOM: Reducing Scale Drift in Monocular Visual Odometry with BEV Representation

BEV-ODOM: Reducing Scale Drift in Monocular Visual Odometry ...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Yufei Wei Sha Lu Fuzhang Han Rong Xiong Yue Wang Laboratory of Industrial Control and Technology and the Institute of Cyber-Systems and Control Zhejiang University Hangzhou China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Monocular visual odometry (MVO) is vital in autonomous navigation and robotics, providing a cost-effective and flexible motion tracking solution, but the inherent scale ambiguity in monocular setups often leads to cumulative errors over time. In this paper, we present BEV-ODOM, a novel MVO framework leveraging the Bird’s Eye View (BEV) Representation to address scale drift. Unlike existing approaches, BEV-ODOM integrates a depth-based perspective-view (PV) to BEV encoder, a correlation feature extraction neck, and a CNN-MLP-based decoder, enabling it to estimate motion across three degrees of freedom without the need for depth supervision or complex optimization techniques. Our framework reduces scale drift in long-term sequences and achieves accurate motion estimation across various datasets, including NCLT, Oxford, and KITTI. The results indicate that BEV-ODOM outperforms current MVO methods, demonstrating reduced scale drift and higher accuracy.

关键词： Correlation Accuracy Three-dimensional displays Tracking Motion estimation Feature extraction Decoding Neck Optimization Visual odometry

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GeoPro-VO: Dynamic Obstacle Avoidance with Geometric Projector Based on Velocity Obstacle

arXiv

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arXiv 2024年

作者： Huang, Jihao Chi, Xuemin Zeng, Jun Liu, Zhitao Su, Hongye The State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China Cruise LLC United States

Optimization-based approaches are widely employed to generate optimal robot motions while considering various constraints, such as robot dynamics, collision avoidance, and physical limitations. It is crucial to efficiently solve the optimization problems in practice, yet achieving rapid computations remains a great challenge for optimization-based approaches with nonlinear constraints. In this paper, we propose a geometric projector for dynamic obstacle avoidance based on velocity obstacle (GeoPro-VO) by leveraging the projection feature of the velocity cone set represented by VO. Furthermore, with the proposed GeoPro-VO and the augmented Lagrangian spectral projected gradient descent (ALSPG) algorithm, we transform an initial mixed integer nonlinear programming problem (MINLP) in the form of constrained model predictive control (MPC) into a sub-optimization problem and solve it efficiently. Numerical simulations are conducted to validate the fast computing speed of our approach and its capability for reliable dynamic obstacle avoidance. Copyright © 2024, The Authors. All rights reserved.

关键词： Constrained optimization

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Adaptive Pre-Processing Linear Output Regulation with Non - Vanishing Measurements

Adaptive Pre-Processing Linear Output Regulation with Non - ...

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American control Conference (ACC)

作者： Qi Han Lei Wang Lorenzo Marconi Zhitao Liu Hongye Su State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University China CASY-DEI University of Bologna Italy

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

This paper studies adaptive pre-processing output regulation problem of linear systems in the presence of non-vanishing measurements and unknown linear exosystems. The proposed regulator is comprised of a washout filter, a pre-processing internal model and a stabilizer, all in continuous-time form and parameterized by parameters acting as the state of a discrete-time identifier. Under a persistency of excitation condition, it is shown that the resulting closed-loop signals are bounded and the regulation error asymptotically converges to zero. Finally, as an extension, we show through an example that the proposed regulator can be applied to a class of nonlinear systems.

关键词： Linear systems Adaptation models Regulators Adaptive systems Measurement uncertainty Regulation Steady-state

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Path Planning for Autonomous Driving with Curvature-considered Quadratic Optimization

Path Planning for Autonomous Driving with Curvature-consider...

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IEEE Symposium on Intelligent Vehicle

作者： Ziang Zhang Ziyi Zou Xiang Li Mingyi Wang Yixu Wang Xiaoqing Guan You Wang Guang Li State Key Laboratory of Industrial Control Technology Institute of Cyber Systems and Control Zhejiang University Hangzhou China

Path planning is a crucial module in motion planning for autonomous driving, aiming at generating kinematically feasible and collision-free paths. Furthermore, the smoothness of generated path is significant for passengers’ comfortable feelings. In this paper, we propose an improved quadratic programming approach that generates optimal paths in urban structure scenarios with the Frenét frame, taking the cost of the path curvature into consideration explicitly. The proposed second-order Taylor-expansion estimation of the path curvature with the lateral spatial parameters can reflect the actual change of path curvature. Various simulated scenarios verify the effectiveness of our proposed method and the improvement of path quality by adding the curvature objective in the optimization procedure. The source code is released as an open-source package for the community.

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Learning Adaptive Manipulation of Objects with Revolute Joint: A case Study on Varied Cabinet Doors Opening

Learning Adaptive Manipulation of Objects with Revolute Join...

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Chinese control Conference (CCC)

作者： Hongxiang Yu Dashun Guo Zhongxiang Zhou Yue Wang Rong Xiong The State Key Laboratory of Industrial Control and Technology The Institute of Cyber-Systems and Control Zhejiang University Hangzhou China

This paper introduces a learning-based framework for robot adaptive manipulating the object with a revolute joint in unstructured environments. We concentrate our discussion on various cabinet door opening tasks. To improve the performance of Deep Reinforcement Learning in this scene, we analytically provide an efficient sampling manner utilizing the constraints of the objects. To open various kinds of doors, we add encoded environment parameters that define the various environments to the input of out policy. To transfer the policy into the real world, we train an adaptation module in simulation and fine-tune the adaptation module to cut down the impact of the policy-unaware environment parameters. We design a series of experiments to validate the efficacy of our framework. Additionally, we testify to the model's performance in the real world compared to the traditional door opening method.

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