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2023 International Conference on Ocean Studies, ICOS 2023

作者： Leng, Jiajun Fu, Li Wang, Lingling Tang, Ning Laboratory of Science and Technology on Marine Navigation and Control China State Shipbuilding Corporation Beijing100036 China Tianjin Navigation Instruments Research Institute Tianjin300131 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Bauman Moscow State Technical University Moscow Department of Automatic Control Systems 105005 Russia

ISBN: (纸本)9798350382457

Path planning algorithms are current research hotspots. Heuristic algorithms that can solve dynamic environment problems are gradually becoming the mainstream research direction. The D∗ algorithm, as one of the new heuristic algorithms, has the advantages of being more computationally efficient than the A ∗ algorithm and being able to solve dynamic problems, and has played an important role in two-dimensional path planning projects such as the NASA Mars rover. But its theoretical research and practical application in 3D environment are very rare. In this context, this paper carries out an in-depth study of the path planning problem based on the D∗ algorithm, and completes the theoretical derivation and simulation process in 2D and 3D environments. In this paper, a complex random 2D map is established by random generation, and the simulation of D∗ algorithm is realized on the map with the help of Python language, which realizes the finding of the relative optimal path. Then, in order to study the feasibility of D∗ solving in 3D environment, the simulation of D∗ algorithm under 3D map is realized with the help of Python and Unity3d. The research results show that the simulation results are good, which is valuable for the theoretical research and practical application of heuristic algorithms, and plays a role in the application of robot path planning algorithms. © 2023 IEEE.

关键词： Python

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GPIO Based Iterative Learning control for Manipulators with Time-Varying Disturbances 42

GPIO Based Iterative Learning Control for Manipulators with ...

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42nd Chinese control Conference, CCC 2023

作者： Lu, Huangyuwei Wang, Zuo Sun, Jiankun Xiao, Xi Li, Shihua School of Automation Southeast University Nanjing210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China Shenzhen Research Institute Southeast University Shenzhen518063 China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Aviation Key Laboratory of Science and Technology on Aero Electromechanical System Integration Nanjing211106 China

ISBN: (纸本)9789887581543

To improve the disturbance rejection ability and repetitive tracking accuracy of manipulators, a composite iterative learning control (ILC) scheme via generalized proportional integral observer (GPIO) is proposed. A high-order polynomial is first employed to model the time-varying disturbances that include unmodeled dynamics, parameter uncertainties and load fluctuations. A GPIO is then designed to estimate the disturbances and high-order time derivatives. By introducing estimations into a PD-type ILC at each iteration, a composite control scheme is obtained, such that the undesirable influence of time-varying disturbances can be effectively attenuated in the repetitive tracking process of manipulators. In this paper, the rigorous stability analysis of the closed-loop system is presented to guarantee that the tracking error exponentially converges to zero as the iteration number tends to infinity. The effectiveness of the proposed control scheme is finally verified by simulation results. © 2023 Technical Committee on control Theory, Chinese Association of automation.

关键词： Disturbance rejection

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GPR and SPSO-CG based gait pattern generation for subject-specific training

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Science China(Information Sciences) 2021年第8期64卷 244-246页

作者： Weiqun WANG Weiguo SHI Shixin REN Zeng-Guang HOU Xu LIANG Jiaxin WANG Liang PENG The State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences CAS Center for Excellence in Brain Science and Intelligence Technology

Dear editor,Gait training has been proved effective for recovery of walking ability for nerve injury patients caused by stroke, spinal cord injury(SCI), traumatic brain injury(TBI), etc. The traditional gait training methods, usually carried out by hands, are laborious and time-consuming, and hence, the training intensity is difficult to be maintained and the rehabilitation results are not satisfactory.

关键词： GPR and SPSO-CG based gait pattern generation for subject-specific training

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Predictive linear online tracking for unknown targets 24

Predictive linear online tracking for unknown targets

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Proceedings of the 41st International Conference on Machine Learning

作者： Anastasios Tsiamis Aren Karapetyan Yueshan Li Efe C. Balta John Lygeros Automatic Control Laboratory ETH Zürich Zürich Switzerland Institute for Dynamic Systems and Control ETH Zürich Zürich Switzerland Automatic Control Laboratory ETH Zürich Zürich Switzerland and Control and Automation Group inspire AG Zürich Switzerland

In this paper, we study the problem of online tracking in linear control systems, where the objective is to follow a moving target. Unlike classical tracking control, the target is unknown, non-stationary, and its state is revealed sequentially, thus, fitting the framework of online non-stochastic control. We consider the case of quadratic costs and propose a new algorithm, called predictive linear online tracking (PLOT). The algorithm uses recursive least squares with exponential forgetting to learn a time-varying dynamic model of the target. The learned model is used in the optimal policy under the framework of receding horizon control. We show the dynamic regret of PLOT scales with O(√TVT), where VT is the total variation of the target dynamics and T is the time horizon. Unlike prior work, our theoretical results hold for non-stationary targets. We implement PLOT on a real quadrotor and provide open-source software, thus, showcasing one of the first successful applications of online control methods on real hardware.

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Guaranteed performances for a learning-based eco-cruise control using robust LPV method 4

Guaranteed performances for a learning-based eco-cruise cont...

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4th IFAC Workshop on Linear Parameter Varying systems, LPVS 2021

作者： Nemeth, Balazs Caspar, Peter Szabo, Zoltan Systems and Control Laboratory Institute for Computer Science and Control Kende u. 13-17 BudapestH-llll Hungary

In this paper the design of an eco-cruise control system with learning-based agent for automated vehicles is proposed. The control design is based on the robust Linear Parameter- Varying (LPV) framework, in which performance levels of the system can be guaranteed. The motivation of the learning-based agent is to reduce the required on-line computation of the eco- cruise control signal, in which several environmental factors are involved, e.g. the forthcoming terrain characteristics, speed limits. In the proposed method the design of the LPV controller and the selection of scheduling variables are performed in an iterative method. As a result, the proposed system is able to handle the degradation of the learning-based agent, while the performance of the system is guaranteed. Copyright © 2021 The Authors. This is an open access article under the CC BY-NC-ND license.

关键词： Iterative methods

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Two Degree of Freedom Active Disturbance Rejection control Strategy for Time Delay systems

Two Degree of Freedom Active Disturbance Rejection Control S...

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Chinese control Conference (CCC)

作者： Xiyuan Shan Tao Cai School of Automation Beijing Institute of Technology Beijing China State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing China

This paper is concerned with the controller design and the theoretical analysis for time-delay systems, a two degree of freedom (feedforward and feedback) control method is proposed, which combines advantages of the Smith predictor and the active disturbance rejection control (ADRC). The feedforward part of controller is used to track the set point, the feedback part of controller (ADRC) is used to suppress interferences and the Smith predictor is used to correct time delay. The proposed control design is easy to tune parameters and has been proved to effectively control systems with large time delay. The bounded input bounded output (BIBO) stability of closed-loop system is verified. Finally, numerical simulations show the effectiveness and practicality of the proposed design.

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Trajectory control of Multi-Axis Robotic Arms Based on Physics-Informed Neural Networks and Nonlinear Model Predictive control

Trajectory Control of Multi-Axis Robotic Arms Based on Physi...

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Chinese automation Congress (CAC)

作者： Yiqing Wang Changqing Xia Xi Jin Chi Xu Yanzhu Zhang School of Automation and Electrical Engineering Shenyang Ligong University Shenyang China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Multi-axis robotic arms are extensively utilized in intelligent manufacturing scenarios, with trajectory control in flexible scenarios constituting a primary challenge. Physics-Informed Neural Networks (PINNs) represent advanced methods that integrate physical laws with data-driven approaches. Despite their outstanding performance in integrating theory and data, research on their application for controlling robotic arms in complex scenarios remains limited. This paper establishes a nonlinear dynamic model based on the physical relationships between the axes of robotic arms. In the absence of prior data, Sobol sequence random sampling is employed to generate data, which are subsequently trained using PINNs. Considering system noise, the Extended Kalman Filter (EKF) is utilized to predict the next state in noisy environments, and Nonlinear Model Predictive control (NMPC) is implemented to control the robotic arm for trajectory tracking, achieving real-time control. Simulation results demonstrate that the proposed Discrete Physics-Informed Predictive control (DPIPC) method exhibits smaller position and velocity errors with less fluctuation compared to the method in the reference, indicating superior control capabilities.

关键词： Fluctuations Trajectory tracking Simulation Noise Neural networks Manipulators Real-time systems Trajectory Manufacturing Predictive control

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GACL-Net:Hybrid Deep Learning Framework for Accurate Motor Imagery Classification in Stroke Rehabilitation

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computers, Materials & Continua 2025年第4期83卷 517-536页

作者： Chayut Bunterngchit Laith H.Baniata Mohammad H.Baniata Ashraf ALDabbas Mohannad A.Khair Thanaphon Chearanai Sangwoo Kang State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing100190China School of Computing Gachon UniversitySeongnam13120Republic of Korea Computer Science Department Faculty of Information TechnologyThe World Islamic Sciences and Education UniversityAmman11947Jordan Intelligent Systems Department Faculty of Artificial IntelligenceAl-Balqa Applied UniversityAl-Salt19117Jordan IT Infrastructure Department Qatrana Cement CompanyAmman11821Jordan Division of Industrial and Logistics Engineering Technology Faculty of Engineering and TechnologyKing Mongkut’s University of Technology North BangkokRayong CampusRayong21120Thailand

Stroke is a leading cause of death and disability worldwide,significantly impairing motor and cognitive *** rehabilitation is often hindered by the heterogeneity of stroke lesions,variability in recovery patterns,and the complexity of electroencephalography(EEG)signals,which are often contaminated by *** classification of motor imagery(MI)tasks,involving the mental simulation of movements,is crucial for assessing rehabilitation strategies but is challenged by overlapping neural signatures and patient-specific *** address these challenges,this study introduces a graph-attentive convolutional long short-term memory(LSTM)network(GACL-Net),a novel hybrid deep learning model designed to improve MI classification accuracy and ***-Net incorporates multi-scale convolutional blocks for spatial feature extraction,attention fusion layers for adaptive feature prioritization,graph convolutional layers to model inter-channel dependencies,and bidi-rectional LSTM layers with attention to capture temporal *** on an open-source EEG dataset of 50 acute stroke patients performing left and right MI tasks,GACL-Net achieved 99.52%classification accuracy and 97.43%generalization accuracy under leave-one-subject-out cross-validation,outperforming existing state-of-the-art ***,its real-time processing capability,with prediction times of 33–56 ms on a T4 GPU,underscores its clinical potential for real-time neurofeedback and adaptive *** findings highlight the model’s potential for clinical applications in assessing rehabilitation effectiveness and optimizing therapy plans through precise MI classification.

关键词： Motor imagery EEG stroke rehabilitation deep learning brain-computer interface

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Nonlinear control Method for Backflipping with Miniature Quadcopters

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IFAC-PapersOnLine 2022年第14期55卷 133-138页

作者： P. Antal T. Péni R. Tóth Systems and Control Laboratory Institute for Computer Science and Control H-1111 Bp. Kende u. 13-17

The paper proposes a nonlinear control method for performing a backflip maneuver with a nano quadcopter. To perform the maneuver, first a feasible reference trajectory is designed that describes the intended state evolution. Then, the designed trajectory is precisely tracked by a nonlinear geometric controller that is able to track even highly challenging reference trajectories. The performance of the proposed method is evaluated and compared to a simple adaptive feedforward control strategy based on simulations and real-world experiments using Bitcraze Crazyflie nano quadcopters.

关键词： aerial robotics aerobatics trajectory planning geometric control optimization

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Modified Implicit Discretization of the Super-Twisting controller

arXiv

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arXiv 2023年

作者： Andritsch, Benedikt Watermann, Lars Koch, Stefan Reichhartinger, Markus Reger, Johann Horn, Martin The Institute of Automation and Control Graz University of Technology Graz Austria The Christian Doppler Laboratory for Model-Based Control of Complex Test Bed Systems Institute of Automation and Control Graz University of Technology Graz Austria The Control Engineering Group at Technische Universität Ilmenau Ilmenau Germany

In this paper a novel discrete-time realization of the super-twisting controller is proposed. The closed-loop system is proven to be globally asymptotically stable in the absence of a disturbance by means of Lyapunov theory. Furthermore, the steady-state error in the disturbed case is computed analytically and shown to be independent of the parameters. The steady-state error only depends on the sampling time and the unknown disturbance. The proposed discrete-time controller is compared to previously published discrete-time super-twisting controllers by means of the controller structure. In extensive simulation studies the proposed controller is evaluated comparative to known controllers. The continuous-time super-twisting controller is capable of rejecting any unknown Lipschitz-continuous perturbation. Furthermore, the convergence time decreases, if any of the gains is increased. The simulations demonstrate that the systems closed in the loop with each of the known controllers lose one of these properties, introduce discretization-chattering effects, or do not yield the same accuracy level as with the proposed controller. The proposed controller, in contrast, is beneficial in terms of the above described properties of the continuous-time super-twisting controller. © 2023, CC BY.

关键词： controllers

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