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SSRN 2024年

作者： Xie, Yuan Zou, Tao Yang, Junjie Sun, Weijun Xie, Shengli School of Mechanical and Electrical Engineering Guangzhou University Guangdong-Hong Kong-Macao Key Laboratory of Multi-scale Information Fusion and Collaborative Optimization Control of Complex Manufacturing Process Guangzhou510006 China School of Automation Guangdong University of Technology Joint International Research Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education of the P.R.C. Guangzhou510006 China School of Automation Guangdong University of Technology Guangdong Key Laboratory of IoT Information Processing Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China School of Automation Guangdong University of Technology Key Laboratory of Intelligent Detection and the IoT in Manufacturing Ministry of Education 111 Center for Intelligent Batch Manufacturing Based on IoT Technology Guangzhou510006 China

Blind source separation under an underdetermined reverberation environment is a very challenging issue. The classical method is based on the expectation maximization algorithm. However, it is limited to high reverberation environments, resulting in bad or even invalid separation performance. To eliminate this restriction, a room impulse response reshaping-based expectation maximization method is designed to solve the problem of blind signal separation in an underdetermined reverberant environment. Firstly, a room impulse response reshaping technology is designed to eliminate the influence of audible echo on the reverberant environment, improving the quality of the received signals. Then, a new mathematical model of time-frequency mixing signals is established to reduce the approximation error of model transformation caused by high reverberation. Furthermore, an improved expectation maximization method is proposed for real-time update learning rules of model parameters, and then the sources are separated using the estimators provided by the improved expectation maximization method. Experimental results based on source separation of speech and music mixtures demonstrate that the proposed algorithm achieves better separation performance while maintaining much better robustness than popular expectation maximization methods. © 2024, The Authors. All rights reserved.

关键词： Reverberation

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Towards Energy-Efficiency: Integrating MATD3 Reinforcement Learning Method for Computational Offloading in RIS-Aided UAV-MEC Environments

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IEEE Internet of Things Journal 2025年

作者： Wu, Liangshun Zhang, Cong Zhang, Bin Du, Jianbo Qu, Junsuo Key Laboratory of Embedded System and Service Computing Tongji University Ministry of Education Shanghai201804 China Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai201210 China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai200240 China Xi'an Key Laboratory of Advanced Control and Intelligent Process School of Automation Xi'an University of Posts & Telecommunications Xi'an710061 China Xinjiang University of Political Science and Law Tumushuke832003 China School of Remote Sensing and Information Engineering Wuhan University Wuhan430079 China Department of Computer City University of Hong Kong 999077 Hong Kong School of Communications and Information Engineering Xi'an University of Posts & Telecommunications Xi'an710061 China

With the proliferation of IoT devices, there is an escalating demand for enhanced computing and communication capabilities. Mobile Edge Computing (MEC) addresses this need by relocating computing resources to the network edge, thereby delivering swifter and more efficient services. This paper introduces a computation offloading and energy consumption optimization framework that leverages Reconfigurable Intelligent Surfaces (RIS), Unmanned Aerial Vehicles (UAVs), and MEC. The scheme aims to maximize energy efficiency through the optimization of task allocation, RIS phase shifts, and UAV trajectories. By employing the Multi-Agent Twin Delayed Deep Deterministic Policy Gradient (MATD3) reinforcement learning algorithm, the paper further refines UAV trajectories and RIS configurations. The simulation results indicate that the proposed method surpasses the traditional Concave-Convex Procedure (CCCP) algorithm in both UAV trajectory control and RIS configuration, demonstrating quicker convergence and enhanced stability. The method proves to be adaptable to diverse environments and tasks, showcasing notable benefits in RIS-assisted interference suppression, particularly with large RIS, thereby enhancing UAV data reception rates. Additionally, MATD3 exhibits faster and smoother convergence for extended task durations and smaller RIS scenarios. Simulation results reveal that UAVs tend to move closer to RIS, with energy efficiency falling as IoT tasks increase, affirming the proposed algorithm's high energy efficiency and effectiveness. © 2025 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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On Optimal control of Flat Hybrid Automata

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IFAC-PapersOnLine 2020年第2期53卷 6800-6805页

作者： Frederik Zahn Tobias Kleinert Veit Hagenmeyer Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Karlsruhe Germany Chair of Process Control Engineering RWTH Aachen University Aachen Germany

Recently, Flat Hybrid Automata (FHA) were introduced as a new model class of hybrid systems. In order to answer the evident question of an optimal operation of the new class of FHA, a well-posed optimization problem definition for FHA is presented. This problem formulation also includes costs on discrete–state transitions and switching actions. We present a solution for a reduced problem: FHA with autonomous switching. For these, a new algorithm is introduced which solves the optimal control problem. The application of this optimization algorithm is shown with an electrical network example.

关键词： Flat hybrid automata differential flatness hybrid switched systems modeling optimal control of hybrid systems

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Model-Free Data-Driven Prescribed-TimePerformance control for Underactuated ASV

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IEEE Transactions on Vehicular Technology 2025年

作者： Zhao, Ying Lu, Wenlong Guo, Ge Dalian Maritime University Marine Electrical Engineering College Dalian116026 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China Northeastern University at Qinhuangdao School of Control Engineering Qinhuangdao066004 China

In this paper, a model-free data-driven prescribedtime-performance (DDPTP) algorithm is developed for the underactuated autonomous surface vehicle (ASV). By defining a leader to provide the dynamic reference signal, the proposed DDPTP algorithm can achieve high-precision target tracking tasks while ensuring collision avoidance and connectivity between the leader and follower. A prescribed-time-performance function (PTPF) is designed for the ASV, featuring an adaptive shape and customizable convergence time. By using both real-time and historical data, a data-driven neural predictor (DDNP) is developed to estimate system uncertainties including unmodeled dynamics, unknown environmental disturbances and unknown input gains. An adaptive control law is employed based on the knowledge obtained from the DDNP, without relying on prior information about the motion model parameters. A switching dynamic event-triggered mechanism (SDETM) is introduced, which can switch between fixed-threshold triggering mechanism and dynamic event-triggered mechanism (DETM). Through this SDETM, not only are communication resources conserved, but frequent updates of the control signal are also mitigated. The effectiveness of the proposed algorithm is validated through verification simulations. © 1967-2012 IEEE.

关键词： Model reference adaptive control

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Incremental Gaussian Mixture Model for Time-varying process Monitoring

Incremental Gaussian Mixture Model for Time-varying Process ...

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Data Driven control and Learning Systems (DDCLS)

作者： Qingyang Dai Chunhui Zhao State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou State Key Laboratory of Process Automation in Mining & Metallurgy Beijing Key Laboratory of Process Automation in Mining & Metallurgy Beijing

ISBN: (数字)9781728159225

ISBN: (纸本)9781728159232

With the increasing complexity of industrial production, data-driven based monitoring methods attract more attention. However, the conventional static process monitoring methods may show poor performance for the time-varying processes since they fail to track the time-varying characteristics. As Gaussian Mixture Model (GMM) has been widely used for process monitoring, this paper presents a new incremental GMM model for monitoring time-varying processes. First, an incremental GMM (IGMM) model is proposed, which can recursively update model parameters, adaptively add new Gaussian components and discard the irrelevant component based on the shifting samples online. Then the Bayesian Inference Probability (BIP) is introduced for monitoring statistics and a two-level partition strategy that can separate normal shifting samples from fault samples is proposed, which reduces the possibility of adding fault samples to the model. On the basis of IGMM model, an adaptive monitoring scheme is developed, which can track the time-varying characteristics of processes. Finally, a time-varying numerical example and the Tennessee Eastman process are adopted to validate the feasibility of the proposed monitoring model. Experimental results clearly demonstrate the adaptiveness of the monitoring model to time-varying processes and the ability to avoid false updates.

关键词： Adaptation models process monitoring Mathematical model Gaussian mixture model Numerical models Data models Production

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Nonlinear unmanned aerial vehicle formation control via composite Laplacian quadratics

Advanced Control for Applications: Engineering and Industria...

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Advanced control for Applications: engineering and Industrial Systems 2020年第2期2卷

作者： Jing, Peng Chen, Fei State Key Laboratory of Synthetical Automation for Process Industries Northeastern University China School of Control Engineering Northeastern University at Qinhuangdao China

This article studies a leader-following formation control problem, in which the agent model is governed by continuous-time nonlinear differential equations. We are interested in designing a distributed control algorithm which is capable of handling the strong nonlinearities induced by the unmanned aerial vehicle model. Our primary objective is to investigate the convergence of the corresponding closed-loop system, and to understand how the nonlinear dynamics and the network topologies may impose intrinsic barriers on formation control. The results show that an exponential convergence is guaranteed under the proposed algorithm via the composite Laplacian quadratics function as long as the network topology has a directed spanning tree. © 2020 John Wiley & Sons, Ltd.

关键词： Network topology

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Secure synchronization control for a class of cyber-physical systems with unknown dynamics

Secure synchronization control for a class of cyber-physical...

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作者： Wang, Ning Li, Xiaojian College of Information Science and Engineering Northeastern University Shenyang110819 China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University and with Key Laboratory of Vibration and Control of Aero-Propulsion System Ministry of Education Shenyang110819 China

This paper investigates the secure synchronization control problem for a class of cyber-physical systems CPSs with unknown system matrices and intermittent denial-of-service DoS attacks. For the attack free case, an optimal control law consisting of a feedback control and a compensated feedforward control is proposed to achieve the synchronization, and the feedback control gain matrix is learned by iteratively solving an algebraic Riccati equation ARE . For considering the attack cases, it is difficult to perform the stability analysis of the synchronization errors by using the existing Lyapunov function method due to the presence of unknown system matrices. In order to overcome this difficulty, a matrix polynomial replacement method is given and it is shown that, the proposed optimal control law can still guarantee the asymptotical convergence of synchronization errors if two inequality conditions related with the DoS attacks hold. Finally, two examples are given to illustrate the effectiveness of the proposed approaches. © 2014 Chinese Association of automation.

关键词： Denial-of-service attack

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Distributionally Robust Fault Detection by using Kernel Density Estimation ⁎

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IFAC-PapersOnLine 2020年第2期53卷 652-657页

作者： Ting Xue Maiying Zhong Lijia Luo Linlin Li Steven X. Ding Institute for Automatic Control and Complex Systems University of Duisburg-Essen Germany College of Electrical Engineering and Automation Shandong University of Science and Technology China Institute of Process Equipment and Control Engineering Zhejiang University of Technology China School of Automation and Electrical Engineering University of Science and Technology Beijing China

In this paper, a method of distributionally robust fault detection (FD) is proposed for stochastic linear discrete-time systems by using the kernel density estimation (KDE) technique. For this purpose, an H 2 optimization-based fault detection filter is constructed for residual generation. Towards maximizing the fault detection rate (FDR) for a prescribed false alarm rate (FAR), the residual evaluation issue regarding the design of residual evaluation function and threshold is formulated as a distributionally robust optimization problem, wherein the so-called confidence sets are constituted to model the ambiguity of distribution knowledge of residuals in fault-free and faulty cases. A KDE based solution, robust to the estimation errors in probability distribution of residual caused by the finite number of samples, is further developed to address the targeting problem such that the residual evaluation function, threshold as well as the lower bound of FDR can be achieved simultaneously. A case study on a vehicle lateral control system demonstrates the applicability of the proposed FD method.

关键词： Fault detection distributionally robust optimization kernel density estimation

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Classification on Point-cloud of Shoe-last Curvature using Weight-updated Boosting based Ensemble Learning

Classification on Point-cloud of Shoe-last Curvature using W...

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IEEE Joint International Information Technology and Artificial Intelligence Conference (ITAIC)

作者： Zairan Li Dan Wang Tianjin Key Laboratory of Process Measurement and Control School of Electrical Engineering and Automation Tianjin University PR China

ISBN: (数字)9781728152448

ISBN: (纸本)9781728152455

Optimizing the curvature shape of shoe last is an important research content and a promising direction in current shoe-last design. The emergence of plantar pressure imaging method provided a new technical mean for shoe-last design. The point-cloud dataset was acquired from the plantar pressure experiments which may be used for generating comfort shoe-last curvature, and the key zones on last curvature may be classified and has potential benefit to the designers to produce comfort shoe. In this paper ensemble learning technologies are applied for classifying those points through the proposed weights-updated algorithms. The proposed weights-updated algorithm with boosting operation performed high effectiveness by comparing to k-NN, random forest (RF) and Bayers (NB) in classification accuracy. The proposed boosting based ensemble learning method will be potential applied in interactive design of industry and be beneficial to produce comfort shoes.

关键词： Training Machine learning algorithms Footwear Boosting Classification algorithms Statistics Unsupervised learning

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Intelligent decision-making of shield tunneling machine using deep reinforcement learning in complex geological environment

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engineering Applications of Artificial Intelligence 2025年 156卷

作者： Zhang, Wenshuai Liu, Xuanyu Zhang, Jiliang State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110004 China College of Information Science and Engineering Northeastern University Shenyang110004 China School of Information and Control Engineering Liaoning Petrochemical University Liaoning Fushun113001 China

To reduce constructional hazards of manual operation, the implementation an intelligent decision-making of the shield machine will be an inevitable development trend in the future. Hence, this paper suggests an intelligent decision-making approach using the Twin Delayed Deep Deterministic Policy Gradient (TD3) agent, which is constructed under the Actor-Critic framework. A robust nonlinear correlation exists between sealed cabin pressure and tunneling parameters. Therefore, a deep reinforcement learning (DRL) training environment is established using the sealed cabin pressure mechanism model. TD3 agent is trained by exploring empirical data obtained from continuous interactions between TD3 agent and the environment. Critic networks calculate the evaluation value of the action. TD3 selects the minimum evaluation value to avoid overestimating the parameters. Actor network adopts delayed updating parameters to make control process more stable and accurate. Based on earth theory model, target pressure values under different earth conditions are calculated. An appropriate reward function is established based on the target pressure for current soil conditions. TD3 agent dynamically adjusts the propulsion speed and screw conveyor speed in real-time according to geological environment to achieve intelligent control of the sealed cabin earth pressure. The experimental results show that the decision-making error of this method is 0 Megapascal (MPa), and it better adapts to random soil variations and can complete nearly a hundred different soil transition tasks in complex environments. This study promotes technological advancements in tunnel engineering by introducing artificial intelligence (AI) technology into tunnel construction field, realizing deep integration of AI technology and civil engineering. © 2025 Elsevier Ltd

关键词： Deep reinforcement learning

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