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10th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2021

作者： Zhu, Xinjian Li, Feng Li, Chenghao Jia, Li Cao, Qingfeng College of Electrical and Information Engineering Jiangsu University of Technology Changzhou213001 China University of Cincinnati Joint Co-op Institute Chongqing University Chongqing400044 China College of Mechatronics Engineering and Automation Shanghai University Shanghai200072 China College of Electrical Energy and Power Engineering Yangzhou University Yangzhou225127 China

ISBN: (纸本)9781665424233

A parameter estimation method based on multi-signal processing is developed that aims at the Hammerstein output error model in this paper. The multi-signal processing is devised to estimate independently parameters of nonlinear block and linear block for Hammerstein output error model. Firstly, using input-output data of binary signal, the linear block parameters are computed by means of auxiliary model recursive least square method, the unmeasurable variables of the Hammerstein model are effectively handled using auxiliary model technology. In addition, model error probability density function technology is applied to estimate parameters of nonlinear block measurable input-output data of random signal, which not only can control space state distribution of model error, but also make error distribution tend to normal distribution. The results verify that proposed parameter estimation method can effectively estimate the Hammerstein output error model. © 2021 IEEE.

关键词： Parameter estimation

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The Joint-Space Reconstruction of Human Fingers by using a Highly Under-Actuated Exoskeleton

The Joint-Space Reconstruction of Human Fingers by using a H...

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IEEE International Conference on Robotics and automation (ICRA)

作者： Yuan Su Gaofeng Li Yongsheng Deng Ioannis Sarakoglou Nikos G. Tsagarakis Jiming Chen College of Control Science and Engineering Zhejiang University Hangzhou China Key Laboratory of Collaborative Sensing and Autonomous Unmanned Systems of Zhejiang Province School of Mechanical Engineering and Automation Northeastern University Shenyang China Humanoids and Human Centered Mechatronics Research Line Istituto Italiano Di Technologia Genova Italy

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Hand motion tracking is essential in many fields, e.g., immersive virtual reality, teleoperation of robotic hand, and hand rehabilitation of stroke patient, as human hand plays a crucial role in our daily life. The highly under-actuated hand exoskeleton, which can track the 6-DoF motions of each fingertip via a highly under-actuated kinematic chain, exhibits many benefits in wearability and portability over other solutions. However, due to the non-anthropomorphic linkage, this hand exoskeleton also encounters difficulties in measuring human-finger’s joint angles. While the joint-space is important in many scenarios, such as teleoperating a robotic hand with anthropomorphic kinematics but with different size to human. Here we proposed a new method to reconstruct the human finger joints by using a highly under-actuated hand exoskeleton. Our key contribution is the arc-fitting algorithm, which is able to calibrate the misalignment between the exoskeleton’s and the human-finger’s base frames and estimate the length of human’s phalanxes, by using the fingertip’s circular motions. With knowing the aforementioned informations, the joint angles can be reconstructed in high precision based on the inverse kinematics models of human fingers. Furthermore, our proposed method is compared with a baseline method, in which the joint angles obtained by a motion capture system are served as ground-truth. The results demonstrate that our proposed method exhibits excellent performance in reconstructing finger’s joint configurations.

关键词： Couplings Tracking Exoskeletons Thumb Virtual reality Stroke (medical condition) Motion capture

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Optimal Scheduling for Unit Commitment with Electric Vehicles And Uncertainty of Renewable Energy Sourcesoptimal Scheduling for Unit Commitment with Electric Vehicles And Uncertainty of Renewable Energy Sources

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SSRN 2022年

作者： Pan, Jian Liu, Tingzhang Department of Automation College of Mechatronics Engineering and Automation Shanghai University Shanghai200072 China School of Physics and Electronic Electrical Engineering Huaiyin Normal University Jiangsu Huai’an223300 China Shanghai Key Laboratory of Power Station Automation Technology Shanghai200072 China

The unit commitment (UC) problem is a typical mixed integer nonlinear programming problem related to power systems. As electric vehicles (EVs) and renewable energy sources (RESs;e.g., wind and solar power) have become integrated into the power system, the UC problem has become increasingly complex. The optimal scheduling of UC, including thermal, wind, solar, and EV power units was investigated. The objective function of optimal scheduling was to minimize the operating cost of the unit and optimize the charging and discharging costs of EVs. In view of power generation characteristics of RESs, an interval number was introduced to describe the uncertainties in the outputs of wind and solar power generation. The enhanced-interval linear programming is introduced to deal with the UC problem including EVs and uncertainty of *** better accuracy, wind power was considered for each of the four seasons (spring, summer, autumn, and winter) and solar power was divided into sunny, cloudy, and rainy conditions. To reflect the influence of EV charging and discharging on UC, three different charging and discharging pricing schemes were considered. Various scenarios were simulated using the CPLEX solver. The simulation results reflect the relationship between economic cost and seasons / climate and confirm the suitability of using an interval number to describe uncertainty in energy generation. © 2022, The Authors. All rights reserved.

关键词： Electric vehicles

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Pallet Detection and Estimation with RGB-D Salient Feature Learning

Pallet Detection and Estimation with RGB-D Salient Feature L...

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Chinese automation Congress (CAC)

作者： Huai Wang Bo Cui Xingfen Wen Yang Jiang Chongjin Gao Yibin Tian Litemaze Technology (Shenzhen) Co. Ltd. Shenzhen China Guangdong Jaten Robot Automation Co. Ltd. Shenzhen China College of Mechatronics and Control Engineering Shenzhen University Shenzhen China

In modern industrial settings, pallets are necessary to logistics transportation. This often necessitates the use of forklifts for pallet handling. However, steer and controlling forklifts require a high level of skill. Traditional methods have struggled to adapt to the rapid pace of businesses development, severely impairing normal production and operational efficiency. We describe a method for automatic detection and positioning of pallets that can be used on Automatic Guided Vehicle (AGV) forklifts. The algorithm initially employs a deep neural network (DNN) approach to learning salient features of the pallets in RGB and depth images, thereby achieving localization. Subsequently, the point cloud of the target area is filtered and matched to obtain the orientation information of the pallets. The entire calculation process can run in real-time on low-scale edge computing devices, and both RGB and depth data come from low-cost TOF cameras. In the experimental section, we study the stability and accuracy of the algorithm in several typical and special scenarios. The results show that our method is robust in normal scenarios and outperforms other methods in special scenarios, with a running time of less than 1300 milliseconds. For the logistics industry, this is a promising solution.

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Output-based event-triggered control of nonlinear systems under deception attacks

Output-based event-triggered control of nonlinear systems un...

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第40届中国控制会议

作者： Fuqiang Li Lisai Gao Xiaoqing Gu Baozhou Zheng College of Sciences Henan Agricultural University School of Mechatronics Engineering and Automation Shanghai University

This paper studies event-triggered output-based security control of nonlinear system under deception attacks obeying a Bernoulli distribution. Firstly, to save system resources of a T-S fuzzy system, an output-based discrete event-triggered mechanism（ETM） is introduced, which excludes Zeno behavior absolutely. Secondly, a closed-loop T-S fuzzy system model is built, which integrates parameters of the nonlinear plant, the ETM, stochastic attacks, fuzzy dynamic output feedback controller and network-induced delays in a unified framework. Thirdly, sufficient conditions for asymptotic stability of the T-S fuzzy system are derived, and the design method of a fuzzy output-based security controller is presented. Finally, an example illustrates effectiveness of the proposed method.

关键词： T-S fuzzy system event-triggered control dynamic output feedback control deception attacks

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Predictive current control of three-phase three-wire APF with error feedback correction

Predictive current control of three-phase three-wire APF wit...

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第33届中国控制与决策会议

作者： Jialin Huang Zhi Zhang Shaoyong Wang Zhaoyun Zhang Department of Electrical Engineering and Automation Dongguan University of Technology College of Mechatronics and Control Engineering Shenzhen University

In this paper,a three-phase three-leg shunt active power filter(SAPF) which is based on model predictive control(MPC) is ***,the mathematical model of three-leg SAPF is derived,and the predictive current control is adopted for the current loop in order to improve the tracking dynamic response of the compensation current,optimize the effect of compensating harmonic current and improve the adaptive ability of the *** ensure the reliability of APF system,traditional PI control is introduced into voltage loop to maintain the stability of DC voltage,minimize overshoot and static ***,simulation results show that the proposed MPC method is correct and feasible.

关键词： Active power filter Predictive current control PI control

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A new SOTIF scenario hierarchy and its critical test case generation based on potential risk assessment 1

A new SOTIF scenario hierarchy and its critical test case ge...

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1st IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2021

作者： Wu, Siyu Wang, Hong Yu, Wenhao Yang, Kai Cao, Dongpu Wang, Feiyue School of Vehicle and Mobility Tsinghua University Beijing China College of Mechanical and Vehicle Engineering Chongqing University Chongqing China University of Waterloo Department of Mechanical and Mechatronics Engineering Waterloo Canada Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665433372

Scenarios are the basis of virtual simulation and should be properly designed and created to ensure the diversity, rationality and criticality. Many researches involve the description of scenario and generation of test case. In this work, we propose a new SOTIF scenario hierarchy to depict static entities, dynamic information and relationships in the scenario. The concept of scenario potential risk is defined in this paper. The prior collision probability and safety control intensity are introduced to quantize the scenario potential risk. Finally, based on the architecture of scenario, we designed a novel process to automatically generate scenarios with the genetic algorithm. The experiments carried out show that proposed methods can generate diverse and trivial test case library. © 2021 IEEE.

关键词： Genetic algorithms

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Data-driven modeling fast-slow coupling delayed dynamics using noisy data and sparse enhancement 5

Data-driven modeling fast-slow coupling delayed dynamics usi...

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5th International Conference on Industrial Artificial Intelligence, IAI 2023

作者： Tang, Huifang Wu, Yuqiang Dong, Yingming Cheng, Cheng Li, Xiuting Huazhong Agricultural University College of Science Wuhan China Yunnan Mechanical Research and Design Institute of Machinery Research Institute Group Yunnan Key Laboratory of Mechatronics Applied Technology Yunnan China Ministry of Education of China Huazhong University of Science and Technology Autonomous Intelligent Unmanned System Engineering Research Center Wuhan430074 China Huazhong University of Science and Techonology School of Artificial Intelligence and Automation Wuhan430074 China

ISBN: (纸本)9798350325294

In this work, we focus on fast-slow delayed dynamical systems, which are usually described by coupled delayed differential equations with two-time scales. The purpose of this article is to propose a data-driven discovery method to identify the fast-slow delayed differential equations from noisy measurements. The proposed method blends delay dictionary-based sparse identification of nonlinear dynamics (SINDy) framework and a fixed interval sampling strategy. Specifically, we leverage the SINDy methodology to describe the fast-slow delayed differential equations by a few active terms of a large dictionary containing the delay terms and many candidate nonlinear basis functions. To identify the active terms and the corresponding coefficients, a sequential thresholded re-weighted 2 least squares algorithm is used to solve the resulting sparse regression problem to enhance the sparsity. Once a sparse model with fixed delay is selected, the proposed method further estimates the delayed time by calculating the reconstruction error from the sparse model until the one that makes the reconstruction error minimum. We verify the effectiveness of the proposed method with several classical fast-slow coupling delayed systems. The experimental results show that the proposed method can well automate the fast-slow delayed differential equations from noisy data and the delayed terms also can be detected accurately. © 2023 IEEE.

关键词： Dynamical systems

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Research on a Coupled System Design for Mineral Lifting and Wastewater Drainage in Deep-Sea Mining Applications

SSRN

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SSRN 2024年

作者： Kang, Yajuan Wang, Kaile Hu, Qiong Liu, Shaojun School of Mechatronics Engineering and Automation Foshan University Foshan528000 China College of Mechanical and Electrical Engineering Central South University Changsha410083 China Shenzhen Research Institute of Central South University Shenzhen518000 China National Key Laboratory of Deep Sea Mineral Researches Development and Utilization Technology Changsha410083 China

In response to various technical, cost, and safety concerns associated with the use of deep-water cables in hydraulic lifting systems for deep-sea mining, as well as issues related to the seabed disposal of mining wastewater, this study proposes a synergistic approach that harnesses wastewater drainage to operate seabed pumps for slurry transportation, thus combining mineral lifting with wastewater drainage in a coupled system. In the context of commercial exploitation of polymetallic nodules at depths of 6,000meters, this paper outlines the main technical structure of the proposed coupled system. The basic process and structural parameters of the system were determined by means ofa computational analysis of mineral lifting and resistance in water pipes. Comparing this coupled system with traditional deep-sea mining designs, the results indicate that this solution can reduce the reliance on deep-water cables, lower the overall volume of the lifting system, and improve the efficiency of mineral lifting. © 2024, The Authors. All rights reserved.

关键词： Minerals

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Machine Learning Models of Intermittent Operation of Ro Wellhead Water Treatment for Salinity Reduction and Nitrate Removal

SSRN

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SSRN 2024年

作者： Zhou, Yang Khan, Bilal Marki, Nora Jarma, Yakubu Aguilar, Christian Cohen, Yoram Department of Automation College of Mechatronics Engineering and Automation Shanghai University Shanghai200072 China Water Technology Research Center Department of Chemical and Biomolecular Engineering Henry Samueli School of Engineering Applied Science University of California Los Angeles Los AngelesCA90095-1592 United States School of Computer Science and Engineering California State University San BernardinoCA United States Institute of the Environment and Sustainability University of California Los Angeles Los AngelesCA United States

Machine learning models were developed for the intermittent multi-mode operation of a wellhead reverse osmosis water purification and desalination system to predict salt passage, nitrate passage, and permeate flux. The models, based on long short-term memory (LSTM) recurrent neural network (RNN) architecture, included an attention mechanism to increase model performance in proximity of the regulatory limit for nitrate. Training and testing of the models for the Startup, Production, Shutdown and Flushing operational modes were based on operational data (consisting of 22 process variables per data sample) acquired every 2-5 seconds over a six-month period. The significant sets of model input attributes for the different operational modes were assessed via Spearman ranking correlation, Self-Organizing Map (SOM) analysis and feed forward feature selection (FFFS). Although the variability of nitrate passage, salt passage and permeate flux was significant over the four operational modes, prediction performance for the three outcomes were with R2and Average Absolute Relative Error (AARE) of 0.78-0.95 and 2.96-6.16%, respectively. Model updates post membrane elements replacement demonstrated similar levels of prediction accuracy. The study results suggest that there is merit in exploring the utility of multi-mode models for sensor fault detection, data imputation, and for potential use in model-predictive control. © 2024, The Authors. All rights reserved.

关键词： Nitrates

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