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Wave Motion Compensation controller Design for a Catamaran Upper Platform in the Presence of External Disturbance

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Wave Motion Compensation Controller Design for a Catamaran U...

Fully Actuated system Theory and Applications (CFASTA), Conference on

作者： Junyu Shen Rongji Yin Yao Yu Rijie Zeng Zhihuan Hu Weidong Zhang Jinhui Lu Wei Xie Department of Automation Shanghai Jiao Tong University Shanghai China SJTU Yazhou Bay Institute of Deepsea Sci-Tech Shanghai Jiao Tong University Hainan China National Engineering Research Center of Ship & Shipping Control System Shanghai China

ISBN: (数字)9798350373691

ISBN: (纸本)9798350373707

This paper presents a nonlinear controller for the upper platform of a catamaran in the presence of external time-varying disturbance induced by wind or wave. To handle disturbances, an observer is employed. Designed using the backstepping method, the controller effectively maintains the platform within a small neighborhood of 10 −3 (m or rad) of the desired attitude and the observer monitors external disturbance in real time. Simulation results validate the strategy, demonstrating the platform’s ability to consistently maintain an ideal posture in the presence of dynamic disturbance.

关键词： Robust control Attitude control Simulation Observers Stability analysis Real-time systems Motion compensation

LU2Net: A Lightweight Network for Real-time Underwater Image Enhancement

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arXiv 2024年

作者： Yang, Haodong Xu, Jisheng Lin, Zhiliang He, Jianping The Department of Computer Science Shanghai Jiao Tong University China The Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China The School of Ocean and Civil Engineering Shanghai Jiao Tong University State Key Laboratory of Ocean Engineering Shanghai200240 China

Computer vision techniques have empowered underwater robots to effectively undertake a multitude of tasks, including object tracking and path planning. However, underwater optical factors like light refraction and absorption present challenges to underwater vision, which cause degradation of underwater images. A variety of underwater image enhancement methods have been proposed to improve the effectiveness of underwater vision perception. Nevertheless, for real-time vision tasks on underwater robots, it is necessary to overcome the challenges associated with algorithmic efficiency and real-time capabilities. In this paper, we introduce Lightweight Underwater Unet (LU2Net), a novel U-shape network designed specifically for real-time enhancement of underwater images. The proposed model incorporates axial depthwise convolution and the channel attention module, enabling it to significantly reduce computational demands and model parameters, thereby improving processing speed. The extensive experiments conducted on the dataset and real-world underwater robots demonstrate the exceptional performance and speed of proposed model. It is capable of providing well-enhanced underwater images at a speed 8 times faster than the current state-of-the-art underwater image enhancement method. Moreover, LU2Net is able to handle real-time underwater video enhancement. Copyright © 2024, The Authors. All rights reserved.

关键词： Motion planning

The Trajectory Re-Planning Method of the Hyper-Sonic Vehicle Facing Sudden Threats

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The Trajectory Re-Planning Method of the Hyper-Sonic Vehicle...

International Conference on control and automation (ICCA)

作者： Jie Ren Jianglong Yu Xiwang Dong Zhang Ren School of Automation Science and Electrical Engineering Beihang University Beijing China Institute of Unmanned System Beihang University Beijing P.R. China Institute of Artificial Intelligence Beihang University Beijing P.R. China Science and Technology on Aircraft Control Laboratory School of Automation Science and Electrical Engineering Beihang University Beijing P.R. China

ISBN: (数字)9798350354409

ISBN: (纸本)9798350354416

Aiming at the trajectory re-planning problem of multiple hyper-sonic vehicles with the time coordination under sudden threats, a time-coordinated fast trajectory re-planning method was proposed to avoid the no-fly zone. Firstly, the three-dimensional trajectory planning problem of the vehicle is decomposed into the longitudinal trajectory planning problem and the transverse trajectory planning problem. Secondly, a single sudden threat is regarded as a circular no-fly zone, and the circumvention of no-fly zone can only be considered in a transverse trajectory planning. Based on the in-depth study of the longitudinal trajectory planning with flight time constraints and range constraints, a reinforcement learning method is introduced into the transverse trajectory re-planning, and a fast tilting angle flipping self decision method based on the reinforcement learning is proposed. Finally, by training DQN to generate a tilting angle reversal decision maker, a fast three-dimensional trajectory re-planning method that can avoid the no-fly zone is realized. The proposed trajectory re-planning method not only has a fast trajectory re-planning speed, but also greatly improves the penetration probability against sudden threats. The simulation results show the feasibility and effectiveness of the proposed method.

关键词： Training automation Trajectory planning Simulation Reinforcement learning Trajectory Planning

A Performance-Based Model Recovery Anti-Windup Design for Linear systems Subject to Actuator Saturation

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A Performance-Based Model Recovery Anti-Windup Design for Li...

American control Conference (ACC)

作者： Wenxin Lai Yuanlong Li Zongli Lin Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China Charles L. Brown Department of Electrical and Computer Engineering University of Virginia Char-lottesville VA U.S.A.

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

In this paper, we investigate the problem of reference tracking for a class of linear systems subject to actuator saturation and propose a performance-based model recovery anti-windup strategy. We adopt the classic model recovery anti-windup framework and regard the states or one of the outputs of the anti-windup compensator as the tracking error, by which the difference between the unconstrained system and the saturated system is quantified. Then, instead of employing the commonly used $\mathcal{L}_{2}$ gain, we present the prescribed performance functions (PPFs) to characterize the real-time tracking error such that the system performance can be captured more specifically. In particular, to avoid singular issues arising from the occurrence of saturation, we modify the existing PPFs with an auxiliary system when saturation occurs. Based on these modified performance functions, we follow a modified prescribed performance control approach and design the remaining output of the anti-windup compensator. Such a design procedure is constructive, making it more promising for extension to nonlinear systems. Theoretical results establish the boundedness of all signals in the closed-loop system. Simulation results verify the effectiveness of our design strategy.

关键词： Linear systems Actuators Simulation system performance Stability analysis Real-time systems Trajectory

Reinforcement Learning-Based Adaptive Search Strategy Combination Algorithm

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Reinforcement Learning-Based Adaptive Search Strategy Combin...

International Symposium on Parallel and Distributed Processing with Applications, ISPA

作者： Xiaotong Liu Zhiqiang Zeng Sicheng Wan Jiajie Tian Tianlei Wang Zhiyong Hong School of Mechanical and Automation Engineering Wuyi University Jiangmen China Jiangmen Key Laboratory of Kejie Semiconductor Bonding Technology and Control System Jiangmen China School of Electronic and Information Engineering Wuyi University Jiangmen China Guangdong Kejie Technology Co. Ltd. Jiangmen China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Integrating multiple search operators to leverage the characteristics of different search operators is one of the common methods to enhance the performance of evolutionary algorithms. Most of these combination algorithms adopt a fixed framework, which makes it difficult to adaptively select the appropriate search operator during the algorithm iteration process, resulting in poor performance on certain problems. To overcome the aforementioned shortcomings of combination algorithms, an adaptive search strategy combination algorithm based on reinforcement learning and neighborhood search (RLASCA) is proposed. During the algorithm iteration process, a reinforcement learning-based adaptive search operator selection method (RLAS) is designed, enabling the algorithm to adaptively select the appropriate search operator based on the state of the individual. Furthermore, to address the issue of premature convergence, a neighborhood search strategy based on differential evolution (NSDE) is designed to increase the diversity of the population. To verify the effectiveness of the proposed algorithm, a comprehensive testing was conducted using the CEC2017 test suite and two constrained engineering design problems. The algorithm was compared with four basic algorithms that compose RLASCA and four classical combination algorithms. The experimental results indicate that RLASCA outperforms the other eight algorithms in terms of convergence speed and accuracy.

关键词： Distributed processing Accuracy Reinforcement learning Evolutionary computation Search problems Optimization Convergence Testing

Incremental Online Learning of Randomized Neural Network with Forward Regularization

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arXiv 2024年

作者： Wang, Junda Hu, Minghui Li, Ning Al-Ali, Abdulaziz Suganthan, Ponnuthurai Nagaratnam The Key Laboratory of System Control and Information Processing Ministry of Education of China China The Department of Automation Shanghai Jiao Tong University Shanghai200240 China The Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China The School of Electrical and Electronic Nanyang Technological University Singapore The KINDI Computing Research Center College of Engineering Qatar University Qatar

Online learning of deep neural networks suffers from challenges such as hysteretic non-incremental updating, increasing memory usage, past retrospective retraining, and catastrophic forgetting. To alleviate these drawbacks and achieve progressive immediate decision-making, we propose a novel Incremental Online Learning (IOL) process of Randomized Neural Networks (Randomized NN), a framework facilitating continuous improvements to Randomized NN performance in restrictive online scenarios. Within the framework, we further introduce IOL with ridge regularization (-R) and IOL with forward regularization (-F). -R generates stepwise incremental updates without retrospective retraining and avoids catastrophic forgetting. Moreover, we substituted -R with -F as it enhanced precognition learning ability using semi-supervision and realized better online regrets to offline global experts compared to -R during IOL. The algorithms of IOL for Randomized NN with -R/-F on non-stationary batch stream were derived respectively, featuring recursive weight updates and variable learning rates. Additionally, we conducted a detailed analysis and theoretically derived relative cumulative regret bounds of the Randomized NN learners with -R/-F in IOL under adversarial assumptions using a novel methodology and presented several corollaries, from which we observed the superiority on online learning acceleration and regret bounds of employing -F in IOL. Finally, our proposed methods were rigorously examined across regression and classification tasks on diverse datasets, which distinctly validated the efficacy of IOL frameworks of Randomized NN and the advantages of forward regularization. Copyright © 2024, The Authors. All rights reserved.

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Multi-kernel Correntropy-based Orientation Estimation of IMUs: Gradient Descent Methods

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arXiv 2023年

作者： Li, Shilei Li, Lijing Shi, Dawei Lou, Yunjiang Shi, Ling The Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong The School of Information and Control Engineering China University of Mining and Technology China The School of Automation Beijing Institute of Technology China The State Key Laboratory of Robotics and System School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen Shenzhen518055 China

This paper presents two computationally efficient algorithms for the orientation estimation of inertial measurement units (IMUs): the multi-kernel correntropy-based gradient descent (CGD) and the multi-kernel correntropy-based decoupled orientation estimation (CDOE). Traditional methods, such as gradient descent (GD) and decoupled orientation estimation (DOE), rely on the least square (LS) criterion in algorithm derivation, making them vulnerable to external acceleration and magnetic interference. To address this issue, we first demonstrate that the multi-kernel correntropy loss (MKCL) is an optimal objective function under the maximum likelihood estimation (MLE) framework when the noise follows a specific type of heavy-tailed distribution. Then, we provide some important properties of the MKCL as a cost function. By replacing the LS cost with the MKCL, we develop the CGD and CDOE algorithms. We evaluate the effectiveness of our proposed methods by comparing them with existing algorithms in various situations. Experimental results indicate that our proposed methods (CGD and CDOE) outperform their conventional counterparts (GD and DOE), especially when faced with external acceleration and magnetic disturbances. Furthermore, the new algorithms demonstrate significantly lower computational complexity than Kalman filter-based approaches, making them suitable for applications with low-cost microprocessors. Copyright © 2023, The Authors. All rights reserved.

关键词： Maximum likelihood estimation

Multi-kernel Correntropy Regression: Robustness, Optimality, and Application on Magnetometer Calibration

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arXiv 2023年

作者： Li, Shilei Chen, Yihan Lou, Yunjiang Shi, Dawei Li, Lijing Shi, Ling The Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong The State Key Laboratory of Robotics and System School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen Shenzhen518055 China The School of Automation Beijing Institute of Technology China The School of Information and Control Engineering China University of Mining and Technology China

This paper investigates the robustness and optimality of the multi-kernel correntropy (MKC) on linear regression. We first derive an upper error bound for a scalar regression problem in the presence of arbitrarily large outliers and state that the kernel bandwidth plays an important role in minimizing the lowest upper error bound. Then, we find that the proposed MKC is related to a specific heavy-tail distribution, where its head shape is consistent with the Gaussian distribution but its tail shape is heavy-tailed, and the extent of heavy-tail is controlled by the kernel bandwidth. It becomes a Gaussian distribution when the bandwidth is infinite, which allows one to tackle both Gaussian and non-Gaussian problems without explicitly investigating the noise distributions. To explore the optimal underlying distribution parameters, an expectation-maximization-like (EM) algorithm is developed to estimate the parameter vectors and the distribution parameters in an alternating manner. The results show that our algorithm can achieve equivalent performance compared with the traditional linear regression under Gaussian noise, and it significantly outperforms the conventional method under heavy-tailed noise. Both numerical simulations and experiments on a magnetometer calibration application verify the effectiveness of the proposed method. Note to Practitioners-The goal of this paper is to enhance the accuracy of conventional linear regression in handling outliers while maintaining its optimality under Gaussian assumptions. Our algorithm is formulated under the maximum likelihood estimation (MLE) framework, assuming the regression residuals follow a type of heavy-tailed noise distribution with an extreme case of Gaussian. The degree of the heavy tail is explored alternatingly using an Expectation-Maximization (EM) algorithm which converges very quickly. The robustness and optimality of the proposed approach are investigated and compared with the traditional approaches. Both th

关键词： Linear regression

Dynamic characteristics of a shrouded blade with impact and friction

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Frontiers of Mechanical engineering 2020年第2期15卷 209-226页

作者： Xumin GUO Jin ZENG Hui MA Chenguang ZHAO Lin QU Bangchun WEN School of Mechanical Engineering and Automation Northeastern UniversityShenyang 110819China Key Laboratory of Vibration and Control of Aero-Propulsion System Ministry of Education Northeastern UniversityShenyang 110819China

A simplified computational model of a twisted shrouded blade with impact and friction is *** this model,the shrouded blade is simulated by a flexible Timoshenko beam with a tip-mass,and the effects of centrifugal stiffening,spin softening,and Coriolis force are *** force is simulated using a linear spring model,and friction force is generated by a tangential spring model under sticking state and a Coulomb friction model under sliding *** proposed model is validated by a finite element ***,the effects of initial gap and normal preload,coefficient of friction,and contact stiffness ratio(the ratio of tangential contact stiffness to normal contact stiffness)on system vibration responses are *** show that resonant peaks become inconspicuous and impact plays a dominant role when initial gaps are large between adjacent *** contrast,in small initial gaps or initial normal preloads condition,resonant speed increases sharply,and the optimal initial normal preloads that can minimize resonant amplitude becomes *** of friction affects the optimal initial normal preload,but it does not affect vibration responses when the contact between shrouds is under full *** resonant amplitude decreases with the increase of contact stiffness ratio,but the optimal initial normal preload is unaffected.

关键词： twisted shrouded blade dynamic analysis impact friction separate-stick-slip motion