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arXiv 2021年

作者： Ahsan, Muhammad Naqvi, Syed Abbas Zilqurnain Anwar, Haider Department of Mechatronics and Control Engineering University of Engineering and Technology Lahore Pakistan Department of Computer Science Namal Institute

Crosstalk and several sources of operational interference are invisible when qubit or a gate is calibrated or benchmarked in isolation. These are unlocked during the execution of full quantum circuit applying entangling gates to several qubits simultaneously. Unwanted Z-Z coupling on superconducting cross-resonance CNOT gates, is a commonly occurring unitary crosstalk noise that severely limits the state fidelity. This work presents (1) method of tracing unitary errors, which exploits their sensitivity to the arrangement of CNOT gates in the circuit and (2) correction scheme that modifies original circuit by inserting carefully chosen compensating gates (single- or two-qubit) to possibly undo unitary errors. On two vastly different types of IBMQ processors offering quantum volume 8 and 32, our experimental results show up to 25% reduction in the infidelity of [[7, 1, 3]] code |+i state. Our experiments aggressively deploy forced commutation of CNOT gates to obtain low noise state-preparation circuits. Encoded state initialized with fewer unitary errors marks an important step towards successful demonstration of fault-tolerant quantum computers. Copyright © 2021, The Authors. All rights reserved.

关键词： Crosstalk

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Interaction-Aware Cut-In Trajectory Prediction and Risk Assessment in Mixed Traffic

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IEEE/CAA Journal of Automatica Sinica 2022年第10期9卷 1752-1762页

作者： Xianglei Zhu Wen Hu Zejian Deng Jinwei Zhang Fengqing Hu Rui Zhou Keqiu Li Fei-Yue Wang the College of Intelligence and Computing Tianjin UniversityTianjin 300350 the China Automotive Technology and Research Center Co.Ltd. Tianjin 300300China the State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body College of Mechanical and Vehicle EngineeringHunan UniversityChangsha 410082China the Department of Mechanical and Mechatronics Engineering University of WaterlooWaterlooON N2L3G1Canada the School of Mechanical Engineering Beijing Institute of TechnologyBeijing 100081China the Macao University of Science and Technology MacaoChina the Waytous Inc. Qingdao 266000China IEEE the State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China

Accurately predicting the trajectories of surrounding vehicles and assessing the collision risks are essential to avoid side and rear-end collisions caused by *** improve the safety of autonomous vehicles in the mixed traffic,this study proposes a cut-in prediction and risk assessment method with considering the interactions of multiple traffic *** integration of the support vector machine and Gaussian mixture model(SVM-GMM)is developed to simultaneously predict cut-in behavior and *** dimension of the input features is reduced through Chebyshev fitting to improve the training efficiency as well as the online inference *** on the predicted trajectory of the cut-in vehicle and the responsive actions of the autonomous vehicles,two risk measurements are introduced to formulate the comprehensive interaction risk through the combination of Sigmoid function and Softmax ***,the comparative analysis is performed to validate the proposed method using the naturalistic driving *** results show that the proposed method can predict the trajectory with higher precision and effectively evaluate the risk level of a cut-in maneuver compared to the methods without considering interaction.

关键词： Cut-in behavior interaction-aware mixed traffic risk assessment trajectory prediction

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Machine Learning Integrated with Model Predictive control for Imitative Optimal control of Compression Ignition Engines

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IFAC-PapersOnLine 2022年第24期55卷 19-26页

作者： Armin Norouzi Saeid Shahpouri David Gordon Alexander Winkler Eugen Nuss Dirk Abel Jakob Andert Mahdi Shahbakhti Charles Robert Koch Department of Mechanical Engineering University of Alberta Edmonton Canada Teaching and Research Area Mechatronics in Mobile Propulsion RWTH Aachen University Aachen Germany Institute of Automatic Control RWTH Aachen University Germany

The high thermal efciency and reliability of the compression-ignition engine makes it the first choice for many applications. For this to continue, a reduction of the pollutant emissions is needed. One solution is the use of Machine Learning (ML) and Model Predictive control (MPC) to minimize emissions and fuel consumption, without adding substantial computational cost to the engine controller. ML is developed in this paper for both modeling engine performance and emissions and for imitating the behaviour of a Linear Parameter Varying (LPV) MPC. Using a support vector machine-based linear parameter varying model of the engine performance and emissions, a model predictive controller is implemented for a 4.5 L Cummins diesel engine. This online optimized MPC solution offers advantages in minimizing the NO x emissions and fuel consumption compared to the baseline feedforward production controller. To reduce the computational cost of this MPC, a deep learning scheme is designed to mimic the behavior of the developed controller. The performance in reducing NO x emissions at a constant load by the imitative controller is similar to that of the online optimized MPC, however, the imitative controller requires 50 times less computation time when compared to that of the online MPC optimization.

关键词： Diesel engines Linear Parameter Variable Model Machine learning Support Vector Machine Model Predictive control Deep Learning

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Catch me if you can: A pursuit-evasion game with intelligent agents in the Unity 3D game environment 6

Catch me if you can: A pursuit-evasion game with intelligent...

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6th International Conference on Electrical engineering, ICEE 2020

作者： Sahin, Ihsan Kumbasar, Tufan Istanbul Technical University Mechatronics Engineering Department Istanbul Turkey Istanbul Technical University Control and Automation Engineering Department Istanbul Turkey

ISBN: (纸本)9781728182926

In this paper, we present a pursuit-evasion game with intelligent agents in the Unity 3D environment. The pursuit-evasion game consists of one evader and four pursuers which are the red, blue, orange and pink agents. In the game scenario, the goal of the evader, which is controlled by human user, is to collect papers without getting caught by the four pursuer agents. The pursuer agents are constructed such that they can move autonomously in the game field via the deployment of a dynamic waypoint structure and the A∗ algorithm. The red, blue and orange pursuer agents share the information on location of the evader agent if the evader agent lies in one of their limited line of sight. They try to intercept the evader agent from different directions in collaborative manner. In this context, we designed a PD controller based pursuing system that tries to minimize the distance between the evader and pursuer agent. For the pink agent, we designed a fuzzy logic based pursuing system that is capable to generate reference interception points and force values to catch evader agent. The performance of the pursuing systems is examined by providing experimental results collected from the game-environment. The results show that the performance of the developed pursuing strategies is satisfactory and the fuzzy logic based pursuing system resulted with a better performance when compared to its conventional counterpart. © 2020 IEEE.

关键词： Fuzzy logic

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Video2Reward: Generating Reward Function from Videos for Legged Robot Behavior Learning

arXiv

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arXiv 2024年

作者： Zeng, Runhao Zhou, Dingjie Liang, Qiwei Liu, Junlin Li, Hui Huang, Changxin Li, Jianqiang Hu, Xiping Sun, Fuchun Artificial Intelligence Research Institute Shenzhen MSU-BIT University China College of Mechatronics and Control Engineering Shenzhen University China College of Computer Science and Software Engineering Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Department of Computer Science and Technology Tsinghua University China

Learning behavior in legged robots presents a significant challenge due to its inherent instability and complex constraints. Recent research has proposed the use of a large language model (LLM) to generate reward functions in reinforcement learning, thereby replacing the need for manually designed rewards by experts. However, this approach, which relies on textual descriptions to define learning objectives, fails to achieve controllable and precise behavior learning with clear directionality. In this paper, we introduce a new video2reward method, which directly generates reward functions from videos depicting the behaviors to be mimicked and learned. Specifically, we first process videos containing the target behaviors, converting the motion information of individuals in the videos into keypoint trajectories represented as coordinates through a video2text transforming module. These trajectories are then fed into an LLM to generate the reward function, which in turn is used to train the policy. To enhance the quality of the reward function, we develop a video-assisted iterative reward refinement scheme that visually assesses the learned behaviors and provides textual feedback to the LLM. This feedback guides the LLM to continually refine the reward function, ultimately facilitating more efficient behavior learning. Experimental results on tasks involving bipedal and quadrupedal robot motion control demonstrate that our method surpasses the performance of state-of-the-art LLM-based reward generation methods by over 37.6% in terms of human normalized score. More importantly, by switching video inputs, we find our method can rapidly learn diverse motion behaviors such as walking and running. Copyright © 2024, The Authors. All rights reserved.

关键词： Robots

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Experimental Validation of Functional Iterative Learning control on a One-Link Flexible Arm

Experimental Validation of Functional Iterative Learning Con...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Sjoerd Drost Pietro Pustina Franco Angelini Alessandro De Luca Gerwin Smit Cosimo Della Santina Department of Cognitive Robotics Delft University of Technology Delft Netherlands Department of Computer Control and Management Engineering Sapienza University of Rome Rome Italy Department of Information Engineering Research Center “Enrico Piaggio” University of Pisa Pisa Italy Institute of Robotics and Mechatronics German Aerospace Center (DLR) Oberpfaffenhofen Germany

Performing precise, repetitive motions is essential in many robotic and automation systems. Iterative learning control (ILC) allows determining the necessary control command by using a very rough system model to speed up the process. Functional iterative learning control is a novel technique that promises to solve several limitations of classic ILC. It operates by merging the input space into a large functional space, resulting in an over-determined control task in the iteration domain. In this way, it can deal with systems having more outputs than inputs and accelerate the learning process without resorting to model discretizations. However, the framework lacks so far a validation in experiments. This paper aims to provide such experimental validation in the context of robotics. To this end, we designed and built a one-link flexible arm that is actuated by a stepper motor, which makes the development of an accurate model more challenging and the validation closer to the industrial practice. We provide multiple experimental results across several conditions, proving the feasibility of the method in practice.

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control System for Electric Drives of Plate Straightening Machines

Control System for Electric Drives of Plate Straightening Ma...

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International Russian Automation Conference (RusAutoCon)

作者： Andrey A. Radionov Andrey Yu. Semitko Boris M. Loginov Vadim R. Khramshin Aleksandra A. Filimonova Department of Automation and Control Moscow Polytechnic University Moscow Russia Department of Mechatronics and Automation South Ural State University Chelyabinsk Russia Magnitogorsk Iron and Steel Works Magnitogorsk Russia Power Engineering and Automated Systems Institute Nosov Magnitogorsk State Technical University Magnitogorsk Russia Department of Electric Drive Mechatronics and Electromechanics South Ural State University Chelyabinsk Russia

The essential quality parameter of rolled products is the flatness along the rolled stock length. The profile defects of the rolled stock, produced at the modern plate mill 5000, frequently occur in the course of intense cooling implied by the thermomechanical rolling technology. To eliminate them, the mill lines include cold straightening machines. The authors show typical flatness defects caused by the occurrence of longitudinal, central and edge waves. Further, the paper characterizes a cold straightening machine installed at the mill 5000 and provides the parameters of electric engines of straightening rollers. The key drawback of the design algorithm for electric drives’ control is setting engine constant moments regardless of the thickness and character of the defects in the rolled stock processed. This causes overloading of the electric and mechanical systems of certain rollers and unsatisfactory product quality. To eliminate the drawback, the authors developed a method for individual setting of engine moments closely coupled in the course of sheet processing. The authors present a functional block diagram for the control system to implement the method. The rationale is provided for the digital setting of moment controllers switched on the inputs of speed controllers. The authors conducted simulation modelling and comparative analysis of engine speeds and moments at the design and proposed settings. The paper provides the analysis of oscillograms obtained before and after the introduction of the control system developed. Its technical and economic feasibility was confirmed during operation.

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In-Hand Manipulation with Synergistic Actuated Robotic Hands: An MPC-Based Approach

In-Hand Manipulation with Synergistic Actuated Robotic Hands...

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IEEE-RAS International Conference on Humanoid Robots

作者： Martin Rohrmüller Philipp Beckerle Knut Graichen Monica Malvezzi Maria Pozzi Chair of Autonomous Systems and Mechatronics Friedrich-Alexander-Universität Erlangen Germany Chair of Automatic Control Friedrich-Alexander-Universität Erlangen Germany Department of Information Engineering and Mathematics University of Siena Siena Italy Humanoids & Human Centered Mechatronics Research Line Istituto Italiano di Tecnologia Genoa Italy

In-hand manipulation, or dexterous manipulation, is one of the most complex challenges in robotics as it requires the accurate coordination of multiple degrees of freedom. While several solutions have been presented for fully actuated hands, less work has focused on underactuated grippers. Synergies can be interpreted as a method for coupling joint motions, constraining the hand's degrees of freedom, and thereby reducing the number of control inputs. In this paper, we propose a model predictive control scheme (MPC) that integrates synergies to implement dexterous in-hand manipulation with robotic hands. In the MPC formulation, synergies can either be considered as constraints on the joint variables or are directly inserted in the system function with a reallocation of the input variables acting on the joints. Through several sets of simulations we compare these two approaches and show their main features.

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Multi-microgrid Energy Management Systems: Architecture, Communication, and Scheduling Strategies

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Journal of Modern Power Systems and Clean Energy 2021年第3期9卷 463-476页

作者： Bin Zhou Jianting Zou Chi Yung Chung Huaizhi Wang Nian Liu Nikolai Voropai Daosen Xu the College of Electrical and Information Engineering Hunan UniversityChangsha 410082China the Department of Electrical and Computer Engineering University of SaskatchewanSaskatoonSK S7N5A9Canada the College of Mechatronics and Control Engineering Shenzhen UniversityShenzhen 518060China the State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power UniversityBeijing 102206China the Melentiev Energy Systems Institute of Siberian Branch of the Russian Academy of Sciences Irkutsk 664033Russia

The increasing penetration of various distributed and renewable energy resources at the consumption premises,along with the advanced metering,control and communication technologies,promotes a transition on the structure of traditional distribution systems towards cyber-physical multi-microgrids(MMGs).The networked MMG system is an interconnected cluster of distributed generators,energy storage as well as controllable loads in a distribution *** its operation complexity can be decomposed to decrease the burdens of communi-cation and control with a decentralized ***,the multi-microgrid energy management system(MIVIGEIV1S)plays a significant role in improving energy efficiency,power quality and reliability of distribution systems,especially in enhancing system resiliency during contingencies.A comprehensive overview on typical functionalities and architectures of MMGEMS is ***,the emerging communication technologies for information monitoring and interaction among MMG clusters are ***,various energy scheduling and control strategies of MMGs for interactive energy trading,multi-energy management,and resilient operations are thoroughly analyzed and ***,some challenges with great importance in the future research are presented.

关键词： Energy management system(EMS) microgrid communication renewable energy multi-energy system

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Design and Validation of Tunable Stiffness Actuator using Soft-Rigid Combined Layer Jamming Mechanism

Design and Validation of Tunable Stiffness Actuator using So...

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IEEE International Conference on Soft Robotics (RoboSoft)

作者： Seoyeon Ham Brian Byunghyun Kang Kevin Abishek Hyunglae Lee Wansoo Kim Department of Interdisciplinary Robot Engineering Systems Hanyang University Gyeonggi-do Republic of Korea Neuromuscular Control and Human Robotics Lab. Arizona States University AZ USA Department of Intelligent Mechatronics Engineering Sejong University Seoul Republic of Korea Robotics Department Ansan-si Gyeonggi-do Republic of Korea

This paper presents a tunable stiffness actuator with a soft-rigid combined layer jamming mechanism. The tunable stiffness actuator is aimed to be integrated into an exosuit to prevent ankle sprain and avoid or mitigate the development of chronic ankle instability. The main purpose of the soft-rigid layer jamming mechanism is to produce large stiffness with a small volume and achieve a linear stiffness characteristic. To this end, the actuator is designed to include rigid retainer pieces within the soft silicone layers, and each soft-rigid layer is jammed to induce stiffness changes. To validate the stiffness characteristics of the proposed soft-rigid actuator, a series of experiments were performed and stiffness changes were investigated for varying jamming states from unjammed to fully jammed states. Increasing the number of jamming layer effectively increased the actuator stiffness, which was consistent with expected results from the analytical model. Soft-rigid actuator's stiffness at the fully jammed state was 212.1% and 123.1% higher than the unjammed state for one-side and both sides anchored conditions, respectively. Compared to the soft actuator without the rigid retainer, the soft-rigid actuator exhibited a more linear characteristic (Pearson correlation coefficient = 0.990 and 0.997 for one-side and both sides anchored conditions, respectively). Moreover, the soft-rigid actuator achieved significantly higher stiffness than the soft actuator in all jamming states (at least 41.3% increase in each jamming state). The results suggest a potential use of the tunable stiffness actuator to develop a soft ankle exosuit with highly variable but linear stiffness characteristics.

关键词： Correlation coefficient Actuators Analytical models Composite materials Mechanical cables Soft robotics Jamming

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