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arXiv 2022年

作者： Zhou, Jian Olofsson, Björn Frisk, Erik The Division of Vehicular Systems Department of Electrical Engineering Linköping University LinköpingSE-581 83 Sweden The Department of Automatic Control Lund University LundSE-221 00 Sweden

This paper proposes an interaction and safety-aware motion-planning method for an autonomous vehicle in uncertain multi-vehicle traffic environments. The method integrates the ability of the interaction-aware interacting multiple model Kalman filter (IAIMM-KF) to predict interactive multimodal maneuvers of surrounding vehicles, and the advantage of model predictive control (MPC) in planning an optimal trajectory in uncertain dynamic environments. The multi-modal prediction uncertainties, containing both the maneuver and trajectory uncertainties of surrounding vehicles, are considered in computing the reference targets and designing the collision-avoidance constraints of MPC for resilient motion planning of the ego vehicle. The MPC achieves safety awareness by incorporating a tunable parameter to adjust the predicted obstacle occupancy in the design of the safety constraints, allowing the approach to achieve a trade-off between performance and robustness. Based on the prediction of the surrounding vehicles, an optimal reference trajectory of the ego vehicle is computed by MPC to follow the time-varying reference targets and avoid collisions with obstacles. The efficiency of the method is illustrated in challenging highway-driving simulation scenarios and a driving scenario from a recorded traffic dataset. Copyright © 2022, The Authors. All rights reserved.

关键词： Model predictive control

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A Special Issue:"Key Technologies of Virtual Power Plant and Distributed Energy Resources"for Global Energy Interconnection

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全球能源互联网（英文版） 2024年第2期7卷前插1-前插2页

作者： Wei Pei Xiaojun Shen Xin Zhang North China Electric Power University BeijingChina Electrical Engineering Department Shanghai Jiaotong UniversityShanghaiChina University of Sheffield United Kingdom Electrical Machines and Drives Research Group and the Department of Automatic Control and Systems Engineering UK Research and Innovation (UKRI)

The increasing penetration of renewable and distributed energy resources (DERs) is transforming the power grid into a new type of clean and low-carbon power system. However, the uncertainty and volatility of DERs have also brought severe challenges to the secure and reliable operation of the power systems. In order to successfully integrate renewable DERs, virtual power plant (VPP) has emerged as a new technique for coordinating demand-side DERs, which has drawn significant attention from industry and academia.

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Real-time Hybrid Gait Phase Recognition Algorithm Under Continuous Multimodal Locomotion for Lower Limb Exoskeleton

Real-time Hybrid Gait Phase Recognition Algorithm Under Cont...

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2022 CSAA/IET International Conference on Aircraft Utility systems, AUS 2022

作者： Miao, Yinan Wang, Xingjian Wang, Jiaqi Wang, Shaoping Gavrilov, Alexander I. School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Qingdao Research Institute Beihang University Qingdao266104 China Beijing Advanced Innovation Center for Big Data-based Precision Medicine Beihang University Beijing100191 China Bauman Moscow State Technical University Department of Automatic Control Systems Moscow105005 Russia

ISBN: (数字)9781839537769

ISBN: (纸本)9781839537769

Nowadays, exoskeletons' ability to operate in complex environments is increasingly important. It is challenging to obtain an accurate gait phase under continuous multimodal locomotion. A hybrid gait phase recognition algorithm is proposed combining the adaptive-oscillator-based algorithm and the model-based algorithm. The modification law is adopted to make two algorithms amend each other reciprocally. The design of the modification law combines the advantages of oscillator-based algorithms and model-based algorithms. The gait phase estimation results are more accurate via the hybrid gait phase prediction algorithm (GPRA). Therefore, appropriate assistance actuation and higher efficiency are obtained under the complicated multimodal locomotion. An experiment was designed to verify the performance of the hybrid GPRA. As a result, the hybrid GPRA can still produce a high-precision gait phase with low latency even when the locomotion mode switches. The hybrid GPRA can provide accurate gait phase and assistance timing under continuous multimodal locomotion. © 2022 IET Conference Proceedings. All rights reserved.

关键词： Exoskeleton (Robotics)

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Safe Exploration and Escape Local Minima with Model Predictive control under Partially Unknown Constraints

arXiv

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arXiv 2022年

作者： Soloperto, Raffaele Mesbah, Ali Allgöwer, Frank Institute for Systems Theory and Automatic Control University of Stuttgart Pfaffenwaldring 9 Stuttgart70569 Germany Department of Chemical and Biomolecular Engineering University of California BerkeleyCA94720 United States

In this paper, we propose a novel model predictive control (MPC) framework for output tracking that deals with partially unknown constraints. The MPC scheme optimizes over a learning and a backup trajectory. The learning trajectory aims to explore unknown and potentially unsafe areas, if and only if this might lead to a potential performance improvement. On the contrary, the backup trajectory lies in the known space, and is intended to ensure safety and convergence. The cost function for the learning trajectory is divided into a tracking and an offset cost, while the cost function for the backup trajectory is only marginally considered and only penalizes the offset cost. We show that the proposed MPC scheme is not only able to safely explore the unknown constraints, but also escape from local minima that may arise from the presence of obstacles. Moreover, we provide formal guarantees for convergence and recursive feasibility of the MPC scheme, as well as closed-loop constraint satisfaction. Finally, the proposed MPC scheme is demonstrated in simulations using an example of autonomous vehicle driving in a partially unknown environment where unknown obstacles are present. Copyright © 2022, The Authors. All rights reserved.

关键词： Model predictive control

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Generalized Multiscale RBF Networks and the DCT for Breast Cancer Detection

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International Journal of Automation and computing 2020年第1期17卷 55-70页

作者： Carlos Beltran-Perez Hua-Liang Wei Adrian Rubio-Solis Department of Automatic Control and Systems Engineering University of SheffieldSheffield S13JDUK Tecnologico de Monterrey Campus TolucaEduardo Monroy Cardenas 2000San Antonio BuenavistaToluca 50110Mexico

The use of the multiscale generalized radial basis function(MSRBF)neural networks for image feature extraction and medical image analysis and classification is proposed for the first time in this *** MSRBF networks hold a simple and flexible architecture that has been successfully used in forecasting and model structure detection of input-output nonlinear *** this work instead,MSRBF networks are part of an integrated computer-aided diagnosis(CAD)framework for breast cancer detection,which holds three stages:an input-output model is obtained from the image,followed by a high-level image feature extraction from the model and a classification module aimed at predicting breast *** the first stage,the image data is rendered into a multiple-input-single-output(MISO)*** order to improve the characterisation,the nonlinear autoregressive with exogenous inputs(NARX)model is introduced to rearrange the available input-output data in a nonlinear *** forward regression orthogonal least squares(FROLS)algorithm is then used to take advantage of the previous arrangement by solving the system as a model structure detection problem and finding the output layer weights of the NARX-MSRBF *** the second stage,once the network model is available,the feature extraction takes place by stimulating the input to produce output signals to be compressed by the discrete cosine transform(DCT).In the third stage,we leverage the extracted features by using a clustering algorithm for classification to integrate a CAD system for breast cancer *** test the method performance,three different and well-known public image repositories were used:the mini-MIAS and the MMSD for mammography,and the BreaKHis for histopathology images.A comparison exercise was also made between different database partitions to understand the mammogram breast density effect in the performance since there are few remarks in the literature on this *** results show that

关键词： Nonlinear system identification image processing discrete cosine transform radial basis functions computer-aided diagnosis neural networks

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High Real-time Locomotion Recognition Algorithm Using Inertial Measurement Units

High Real-time Locomotion Recognition Algorithm Using Inerti...

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2022 CSAA/IET International Conference on Aircraft Utility systems, AUS 2022

作者： Wang, Jiaqi Wang, Xingjian Miao, Yinan Wang, Shaoping An, Mailing Miao, Yiqi Gavrilov, Alexander I. School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Qingdao Research Institute Beihang University Qingdao266104 China Beijing Advanced Innovation Center for Big Data-based Precision Medicine Beihang University Beijing100191 China Bauman Moscow State Technical University Department of Automatic Control Systems Moscow105005 Russia

ISBN: (数字)9781839537769

ISBN: (纸本)9781839537769

Human-machine interaction devices (HMI), such as assisted exoskeletons and assisted prostheses, that improve human motion behaviours are playing an increasingly important role in various fields. In order for these HMI devices to provide accurate and appropriate assistance to wearers, the real-time recognition of locomotion with accuracy is among the key issues to be solved. This paper proposes a high real-time motion pattern recognition algorithm using inertial measurement units (IMU) that can accurately and quickly identify the current motion state by collecting various motion parameters of the lower limb during human walking. The algorithm uses a trajectory solving and motion direction discrimination method based on the shank zero velocity point and a terrain classification method based on an F-score improved support vector machine (F-score-SVM). In this paper, experimental data was used to verify the performance of the algorithm. Results show that the algorithm performs better than traditional recognition algorithms in complex environments. © 2022 IET Conference Proceedings. All rights reserved.

关键词： Support vector machines

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Fault-tolerant Optimal control Scheme for Satellite Micro-launchers

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IFAC-PapersOnLine 2020年第2期53卷 4115-4120页

作者： Alexandra Reitu Andrei Sperilă Bogdan D. Ciubotaru Automatic Control and Systems Engineering Department University Politehnica of Bucharest

An integration of Fault Detection, Isolation and Recovery (FDIR) with the Linear Quadratic Gaussian (LQG) technique is presented, which achieves fault tolerance while maintaining control optimality. The FDIR scheme is tested on a space micro-launcher model, and simulation results show successful accommodation of both sensor and actuator faults.

关键词： Aerospace control LQG control Fault detection Fault diagnosis Fault tolerant systems

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A Study for Obstacle Determination fusing MMW Radar and Vision Sensor 2

A Study for Obstacle Determination fusing MMW Radar and Visi...

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2nd IEEE International Conference on Information Technology, Big Data and Artificial Intelligence, ICIBA 2021

作者： Rui, Wang Xin, Gu Tao, Wang National Key Laboratory of Science and Technology on Aerospace Intelligence Control Beijing Aerospace Automatic Control Institute Beijing China China Academy of Launch Vehicle Technology Department of Research Development Center Beijing China School of Intelligent Systems Engineering Sun Yat-Sen University Guangzhou China

ISBN: (纸本)9781665428767

This paper, on the basis of attention mechanism and driver's visual consciousness, presented a robust and adaptive approach for on-road obstacle boundary detection through fusing vision sensor and millimeter wave (MMW) radar in order to implement the navigation task, which is the sequent research work of radar and vision fusion for autonomous ground vehicle (AGV) navigation. It is the first most important issue for practically real-time precise guidance, navigation and control (GNC) of AGV. The novel exploration of prior knowledge about the features, shadow and edge or the position indicator lamps was discussed and employed with the attentive information from millimeter wave radar. An adaptive threshold algorithm based on the local significant peak and trough inside peaks determination (LSiPTiD) of the gray image histogram was provided in order to automatically judge the validity of obstacle shadow detection and detect the obstacle robustly. Vertical edge detection and possible intensity mutant were integrated with shadow and radar vision fusion point so as to recognize obstacle boundary correctly and robustly for further GNC task. In the end, through huge experiments of real on-road measured data, the algorithm was verified to be robust, adaptive and fast for real-time applications in natural road environment. © 2021 IEEE.

关键词： Behavioral research

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Model Predictive control for Wave Energy Converters: A Moving Window Blocking Approach

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IFAC-PapersOnLine 2020年第2期53卷 12815-12821页

作者： Juan Guerrero-Fernández Oscar J. González-Villarreal John Anthony Rossiter Bryn Jones Department of Automatic Control and Systems Engineering University of Sheffield UK

Ocean wave energy is one of the most concentrated sources of renewable energy. However, until now it has not reached the economic feasibility required to be commercialised. To improve the efficiency of wave energy converters, several advanced control strategies have been proposed, including Model Predictive control (MPC). Nevertheless, the computational burden of the underlying optimisation problem is a drawback of conventional (Full-Degree of Freedom, F-DoF) MPC, which typically limits its application for real-time control of systems. In this paper, a Moving Window Blocking (MWB) approach is proposed to speed-up the time required for each optimisation problem by reducing the number of decision variables using input parameterised solutions. Numerical simulation of a generic single device point absorber wave energy converter controlled by this scheme confirms the potential of this approach.

关键词： Wave energy converters Model predictive controller Moving window blocking

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Resistance Tuning of Soft Strain Sensor Based on Saline Concentration and Volume Changes 21st

Resistance Tuning of Soft Strain Sensor Based on Saline Conc...

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21th Annual Conference on Towards Autonomous Robotics, TAROS 20120

作者： Jones, Joanna Gillett, Zachary Perez-Guagnelli, Eduardo Damian, Dana D. Automatic Control and Systems Engineering Department University of Sheffield SheffieldS1 3JD United Kingdom

ISBN: (纸本)9783030634858

Soft sensors have a wide potential in augmenting the functionality of soft robots for healthcare, by providing information without compromising the mechanical compliance. Soft sensors that are based on ionic solutions are of particular interest, as they can be used for in-the-body medical applications due to their biocompatibility. In this paper, we present a soft strain sensor whose resistance is tuned by varying its volume and its ionic concentration. The study opens up the possibility of creating soft sensors whose electrical properties could be adjusted dynamically in fluidic soft robots, in order to suit specific tasks. © 2020, Springer Nature Switzerland AG.

关键词： Biocompatibility

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