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21st International Multi-Conference on systems, Signals and Devices, SSD 2024

作者： Abubakar, Abdulrazaq Nafiu Yahaya, Jamilu Umar Ma'Aruf, Muhammad Mysorewala, Muhammad Faizan Saif, Abdul-Wahid A. King Fahd University of Petroleum and Minerals Control and Instrumentation Engineering Department Dhahran31261 Saudi Arabia Interdisciplinary Research Center for Smart Mobility and Logistics King Fahd University of Petroleum and Minerals Dhahran31261 Saudi Arabia Interdisciplinary Research Center for Intelligent Manufacturing and Robotics King Fahd University of Petroleum and Minerals Dhahran31261 Saudi Arabia

ISBN: (纸本)9798350374131

In this paper, we studied the finite-time stability of robotic arm systems against Denial of Service attacks, when the link between the plant and the controller is often interrupted. In order to do this, a method is suggested that relies on an event-triggered mechanism which is based on the rate of change of the Lyapunov function, that keeps the system finite-time stable while simultaneously reducing the quantity of data interchange in the network. The suggested event-triggered finite-time controller's vulnerability to Denial of Service attacks is then assessed, and sufficient limitations on the length of a Denial of Service are identified to ensure the system's stability within a specified time frame. Lastly, a simulation study is used to show how effective the suggested strategy is. © 2024 IEEE.

关键词： system stability

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Model based reinforcement learning for robot grasping trajectory generation 4

Model based reinforcement learning for robot grasping trajec...

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4th IEEE International Conference on Advanced robotics and Mechatronics, ICARM 2019

作者： Xue, Hongxiang Wen, Shuhuan Yang, Chao Liu, Huaping Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao066004 China Department of Computer Science and Technology State Key Laboratory of Intelligent Technology and System Tsinghua National Laboratory for Information Science and Technology Tsinghua University Beijing100084 China

ISBN: (纸本)9781728100647

Trajectory generation is a fundamental problem for successful robotic grasping. However, most of the existing work dealt with this problem using supervised learning with a prescribed model. It prevents the developed grasp strategies from being used for new unknown scenarios. In this paper, inspired by the success of the reinforcement learning method, we developed a new method which optimizes grasp trajectories using a policy search method. An important advantage of the proposed method is that the complicated 3D model could be used to generate the multi-dimensional grasp configurations. The developed algorithm has been validated on some typical robotic hand. The experimental results show that the proposed method achieves excellent performance. © 2019 IEEE.

关键词： Trajectories

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Practical fractional-order variable-gain super-twisting control with application to wafer stages of photolithography systems

arXiv

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arXiv 2021年

作者： Kuang, Zhian Sun, Liting Gao, Huijun Tomizuka, Masayoshi Research Institute of Intelligent Control and Systems Harbin Institute of Technology Harbin150001 China State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin150001 China Mechanical Control System Lab Mechanical Engineering Department University of California BerkeleyCA94720 United States

In this paper, a practical fractional-order variable-gain super-twisting algorithm (PFVSTA) is proposed to improve the tracking performance of wafer stages for semiconductor manufacturing. Based on the sliding mode control (SMC), the proposed PFVSTA enhances the tracking performance from three aspects: 1) alleviating the chattering phenomenon via super-twisting algorithm and a novel fractional-order sliding surface (FSS) design, 2) improving the dynamics of states on the sliding surface with fast response and small overshoots via the designed novel FSS and 3) compensating for disturbances via variable-gain control law. Based on practical conditions, this paper analyzes the stability of the controller and illustrates the theoretical principle to compensate for the uncertainties caused by accelerations. Moreover, numerical simulations prove the effectiveness of the proposed sliding surface and control scheme, and they are in agreement with the theoretical analysis. Finally, practice-based comparative experiments are conducted. The results show that the proposed PFVSTA can achieve much better tracking performance than the conventional methods from various perspectives. Copyright © 2021, The Authors. All rights reserved.

关键词： Sliding mode control

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A Variable Power Surface Error Function backstepping based Dynamic Surface control of Non-Lower Triangular Nonlinear systems

arXiv

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arXiv 2024年

作者： Abubakar, Abdulrazaq Nafiu Nasir, Ali Quamar, Md Muzakkir Control and Instrumentation Engineering Department KFUPM Dhahran31261 Saudi Arabia Interdisciplinary Research Center for Intelligent Manufacturing and Robotics KFUPM Dhahran31261 Saudi Arabia KFUPM Dhahran31261 Saudi Arabia

A control design for error reduction in the tracking control for a class of nonlower triangular nonlinear systems is presented by combining techniques of Variable Power Surface Error Function (VPSEF), backstepping, and dynamic surface control. At each step of design, a surface error is obtained, and based on its magnitude, the VPSEF technique decides the surface error to be used. Thus, the backstepping-based virtual and actual control law is designed to stabilize the corresponding subsystem. To address the issue of circular structure, a first-order low-pass filter is used to handle the virtual control signal at each intermediate stage of the recursive design. The stability analysis of the closed-loop system demonstrates that all signals indicate semi-global uniform ultimate boundedness. Moreover, by using the switching strategy of the control input using the VPSEF technique suitably, it is possible to ensure that the steady-state tracking error converges to a neighborhood of zero with an arbitrarily very small size. The effectiveness of the proposed concept has been verified using two different simulated demonstrations. © 2024, CC BY.

关键词： Backstepping

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Fuzzy modeling and control of a nonlinear magnetic bearing system

Fuzzy modeling and control of a nonlinear magnetic bearing s...

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IEEE Conference on control Technology and Applications (CCTA)

作者： Sung-Kyung Hong R. Langari Joongseon Joh Department of Mechanical Engineering and Center for Fuzzy Logic Roboticsand Intelligent Systems Research Texas A and M University College Station TX USA Department of Control and Instrumentation engineering Changwon NatE28099l University South Korea

This paper presents an approach, called fuzzy model based nonlinear fuzzy control, that overcomes position dependent nonlinearity of magnetic bearing systems. We represent the nonlinear magnetic bearing by the Takagi-Sugeno-Kang fuzzy model. Based on such model, nonlinear fuzzy controllers can be derived by means of a systematic synthesis approach. Moreover, the stability analysis of the fuzzy control system can be done efficiently by using linear matrix inequality method. Simulation results demonstrate that the proposed fuzzy controller yields not only maximized stability boundary but also better performance than a single operating point linear controller.

关键词： Fuzzy control Fuzzy systems Nonlinear control systems Magnetic levitation Fuzzy logic control systems Nonlinear dynamical systems Nonlinear magnetics Rotors Magnetic forces

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Hazard Analysis of Collaborative Automation systems: A Two-layer Approach based on Supervisory control and Simulation

Hazard Analysis of Collaborative Automation Systems: A Two-l...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Tom P. Huck Yuvaraj Selvaraj Constantin Cronrath Christoph Ledermann Martin Fabian Bengt Lennartson Torsten Kröger Intelligent Process Automation and Robotics Lab Institute of Anthropomatics and Robotics (IAR-IPR) Karlsruhe Institute of Technology India Department of Electrical Engineering Division of Systems and Control Chalmers University of Technology Gothenburg Sweden

Safety critical systems are typically subjected to hazard analysis before commissioning to identify and analyse potentially hazardous system states that may arise during operation. Currently, hazard analysis is mainly based on human reasoning, past experiences, and simple tools such as checklists and spreadsheets. Increasing system complexity makes such approaches decreasingly suitable. Furthermore, testing-based hazard analysis is often not suitable due to high costs or dangers of physical faults. A remedy for this are model-based hazard analysis methods, which either rely on formal models or on simulation models, each with their own benefits and drawbacks. This paper proposes a two-layer approach that combines the benefits of exhaustive analysis using formal methods with detailed analysis using simulation. Unsafe behaviours that lead to unsafe states are first synthesised from a formal model of the system using Supervisory control Theory. The result is then input to the simulation where detailed analyses using domain-specific risk metrics are performed. Though the presented approach is generally applicable, this paper demonstrates the benefits of the approach on an industrial human-robot collaboration system.

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Self-supervised Multi-frame Monocular Depth Estimation with Pseudo-LiDAR Pose Enhancement

Self-supervised Multi-frame Monocular Depth Estimation with ...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Wenhua Wu Guangming Wang Jiquan Zhong Hesheng Wang Zhe Liu MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai China Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Insititute of Medical Robotics Shanghai Jiao Tong University Shanghai China

Depth estimation is one of the most important tasks in scene understanding. In the existing joint self-supervised learning approaches of depth-pose estimation, depth estimation and pose estimation networks are independent of each other. They only use the adjacent image frames for pose estimation and lack the use of the estimated geometric information. To enhance the depth-pose association, we propose a monocular multi-frame unsupervised depth estimation framework, named PLPE-Depth. There are a depth estimation network and two pose estimation networks with image input and pseudo-LiDAR input. The main idea of our approach is to use the pseudo-LiDAR reconstructed from the depth map to estimate the pose of adjacent frames. We propose depth re-estimation with a better pose between the image pose and the pseudo-LiDAR pose to improve the accuracy of estimation. Besides, we improve the reconstruction loss and design a pseudo-LiDAR pose enhancement loss to facilitate the joint learning. Our approach enhances the use of the estimated depth information and strengthens the coupling between depth estimation and pose estimation. Experiments on the KITTI dataset show that our depth estimation achieves state-of-the-art performance at low resolution. Our source codes will be released on https://***/IRMVlabIPLPE-Depth.

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Terrain identification on a one-legged hopping robot using high-resolution pressure images

Terrain identification on a one-legged hopping robot using h...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Jacob J. Shill Emmanuel G. Collins Eric Coyle Jonathan Clark Department of Mechanical Engineering Center for Intelligent Systems Control. and Robotics (CISCOR) Tallahassee FL USA Department of Mechanical Engineering Embry-Riddle Aeron University Daytona Beach FL USA Department of Mechanical Engineering Scansorial and Terrestrial Robotics and Integrated Design Lab (STRIDe) Tallahassee FL USA

For efficient and safe locomotion the gaits of legged robots should vary with the type of terrain. Hence, terrain surface classification is an important problem for this class of mobile robots. Prior research has developed approaches to proprioceptive terrain classification for both wheeled and limbed robots that use sensor measurements dependent upon the dynamics of the robot, which ultimately requires the classification system to be trained at a large number of operating conditions (e.g., vehicle speeds and loads). This research develops an approach to terrain identification based on pressure images generated through direct surface contact using a robot skin constructed around a high-resolution pressure sensing array. Terrain signatures for classification are formulated from the magnitude frequency responses of the pressure images. The methodology is used to train and test a classifier using dynamically measured pressure images from a one-legged hopping robot. Experimental tests yield high classification accuracies, which are independent with respect to changing robot dynamics (i.e., different leg gaits). The findings of this paper suggest the methodology can be extended to autonomous field robots, providing the robot with crucial information about the environment that can be used to aid stability over rough terrains and enhance motion planning over varying terrains.

关键词： Robot sensing systems Skin Legged locomotion Vectors Foot Accuracy

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Renewable Power Generation Maximization for UAVs with Adaptive Sliding Mode control

Renewable Power Generation Maximization for UAVs with Adapti...

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International Conference on Renewable Energy Research and Applications (ICRERA)

作者： Khalid Alfuwail Ali Nasir Md Shafiullah Ayman Abdallah Control & Instrumentation Engineering Department King Fahd University of Petroleum & Minerals (KFUPM) Dhahran Eastern Province Saudi Arabia Control & Instrumentation Engineering Department IRC Intelligent Manufacturing and Robotics (IRC-IMR) King Fahd University of Petroleum & Minerals (KFUPM) Dhahran Eastern Province Saudi Arabia Control & Instrumentation Engineering Department IRC for Sustainable Energy Systems (IRC-SES) King Fahd University of Petroleum & Minerals (KFUPM) Dhahran Eastern Province Saudi Arabia Aerospace Engineering Department IRC for Aviation & Space Exploration (IRC-ASE) King Fahd University of Petroleum & Minerals (KFUPM) Dhahran Eastern Province Saudi Arabia

ISBN: (数字)9798350375589

ISBN: (纸本)9798350375596

The demand for efficient and Fuel consumption alternative renewable energy sources in the aviation sector has led to the exploration of advanced technologies such as Ram Air Turbines (RATs). RATs are critical in applications requiring power generation in emergency scenarios that may happen to un-manned aerial vehicles (UAVs). However, the primary challenge lies in optimizing the performance of the RATs, such as power output and operational stability, under varying and unpredictable wind conditions. Traditional control methods often fail to adapt to these dynamic environments. This paper underscores the necessity of utilizing a recurrent neural network (RNN) to detect uncertain wind turbine dynamics; a sliding mode control rule is created in the suggested controller to monitor the optimal turbine rotation speed. Next, an online update technique is developed to provide real-time weight updates for the RNN, enabling control over maximum power extraction. According to simulation results, the suggested controller outperforms a traditional control approach in tracking the optimal turbine rotation speed and extracting the most wind power from RATs by 13 times, even in severe nonlinearities and system uncertainties.

关键词： Renewable energy sources Uncertainty Recurrent neural networks Stability analysis Real-time systems Hardware Wind turbines Vehicle dynamics Sliding mode control Wind energy conversion

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Human intention estimation based on hidden Markov model motion validation for safe flexible robotized warehouses

arXiv

引用

arXiv 2018年

作者： Petković, Tomislav Puljiz, David Marković, Ivan Hein, Björn University of Zagreb Faculty of Electrical Engineering and Computing Department of Control and Computer Engineering Laboratory for Autonomous Systems and Mobile Robotics Unska 3 ZagrebHR-10000 Croatia Karlsruhe Institute of Technology Institute for Anthropomatics and Robotics Intelligent Process Automation and Robotics Lab Kaiserstraße 12 Deutschland KarlsruheDE-76131

With the substantial growth of logistics businesses the need for larger warehouses and their automation arises, thus using robots as assistants to human workers is becoming a priority. In order to operate efficiently and safely, robot assistants or the supervising system should recognize human intentions in real-time. Theory of mind (ToM) is an intuitive human conception of other humans' mental state, i.e., beliefs and desires, and how they cause behavior. In this paper we propose a ToM based human intention estimation algorithm for flexible robotized warehouses. We observe human's, i.e., worker's motion and validate it with respect to the goal locations using generalized Voronoi diagram based path planning. These observations are then processed by the proposed hidden Markov model framework which estimates worker intentions in an online manner, capable of handling changing environments. To test the proposed intention estimation we ran experiments in a real-world laboratory warehouse with a worker wearing Microsoft Hololens augmented reality glasses. Furthermore, in order to demonstrate the scalability of the approach to larger warehouses, we propose to use virtual reality digital warehouse twins in order to realistically simulate worker behavior. We conducted intention estimation experiments in the larger warehouse digital twin with up to 24 running robots. We demonstrate that the proposed framework estimates warehouse worker intentions precisely and in the end we discuss the experimental results. Copyright © 2018, The Authors. All rights reserved.

关键词： Virtual reality

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