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arXiv 2022年

作者： Verginis, Christos K. Kantaros, Yiannis Dimarogonas, Dimos V. The Division of Signals and Systems Department of Electrical Engineering Uppsala University Uppsala Sweden The Department of Electrical and Systems Engineering Washington University St. LouisMO United States The Division of Decision and Control Systems Department of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

We develop an algorithm for the motion and task planning of a system comprised of multiple robots and unactuated objects under tasks expressed as Linear Temporal Logic (LTL) constraints. The robots and objects evolve subject to uncertain dynamics in an obstacle-cluttered environment. The key part of the proposed solution is the intelligent construction of a coupled transition system that encodes the motion and tasks of the robots and the objects. We achieve such a construction by designing appropriate adaptive control protocols in the lower level, which guarantee the safe robot navigation/object transportation in the environment while compensating for the dynamic uncertainties. The transition system is efficiently interfaced with the temporal logic specification via a sampling-based algorithm to output a discrete path as a sequence of synchronized actions of the robots;such actions satisfy the robots' as well as the objects' specifications. The robots execute this discrete path by using the derived low level control protocol. Simulation results verify the proposed framework. © 2022, CC BY.

关键词： Multi agent systems

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Full Point Encoding for Local Feature Aggregation in 3D Point Clouds

arXiv

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arXiv 2023年

作者： He, Yong Yu, Hongshan Yang, Zhengeng Liu, Xiaoyan Sun, Wei Mian, Ajmal National Engineering Laboratory for Robot Visual Perception and Control Technology College of Electrical and Information Engineering Hunan University Lushan South Rd. Yuelu Dist. Changsha410082 China Department of Computer Science The University of Western Australia WA6009 Australia

Point cloud processing methods exploit local point features and global context through aggregation which does not explicity model the internal correlations between local and global features. To address this problem, we propose full point encoding which is applicable to convolution and transformer architectures. Specifically, we propose Full Point Convolution (FPConv) and Full Point Transformer (FPTransformer) architectures. The key idea is to adaptively learn the weights from local and global geometric connections, where the connections are established through local and global correlation functions respectively. FPConv and FPTransformer simultaneously model the local and global geometric relationships as well as their internal correlations, demonstrating strong generalization ability and high performance. FPConv is incorporated in classical hierarchical network architectures to achieve local and global shape-aware learning. In FPTransformer, we introduce full point position encoding in self-attention, that hierarchically encodes each point position in the global and local receptive field. We also propose a shape aware downsampling block which takes into account the local shape and the global context. Experimental comparison to existing methods on benchmark datasets show the efficacy of FPConv and FPTransformer for semantic segmentation, object detection, classification, and normal estimation tasks. In particular, we achieve state-of-the-art semantic segmentation results of 76% mIoU on S3DIS 6-fold and 72.2% on S3DIS Area 5. Copyright © 2023, The Authors. All rights reserved.

关键词： Convolution

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Distributed Prescribed-Time Observer for Nonlinear Systems in Block-Triangular Form

arXiv

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arXiv 2025年

作者： de Heij, Vincent Niazi, M.B. Umar Johansson, Karl H. Ahmed, Saeed Faculty of Science and Engineering University of Groningen Groningen9747 AG Netherlands Division of Decision and Control Systems Digital Futures KTH Royal Institute of Technology StockholmSE-100 44 Sweden Division of Decision and Control Systems Digital Futures KTH Royal Institute of Technology StockholmSE-100 44 Sweden Laboratory for Information and Decision Systems Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States

This paper proposes a distributed prescribed-time observer for nonlinear systems representable in a block-triangular observable canonical form. Using a weighted average of neighbor estimates exchanged over a strongly connected digraph, each observer estimates the system state despite the limited observability of local sensor measurements. The proposed design guarantees that distributed state estimation errors converge to zero at a user-specified convergence time, irrespective of observers’ initial conditions. To achieve this prescribed-time convergence, distributed observers implement time-varying local output injection gains that monotonically increase and approach infinity at the prescribed time. The theoretical convergence is rigorously proven and validated through numerical simulations, where some implementation issues due to increasing gains have also been clarified. Copyright © 2025, The Authors. All rights reserved.

关键词： Digital arithmetic

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Quantum advantage for differential equation analysis

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Physical Review A 2022年第2期105卷 022415-022415页

作者： Bobak Toussi Kiani Giacomo De Palma Dirk Englund William Kaminsky Milad Marvian Seth Lloyd Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Research Laboratory of Electronics Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Giacomo De Palma Scuola Normale Superiore 56126 Pisa Italy QuEra Computing Boston Massachusetts 02143 USA Department of Mechanical Engineering Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Department of Electrical and Computer Engineering Center for Quantum Information and Control University of New Mexico Albuquerque New Mexico 87131 USA

Quantum algorithms for differential equation solving, data processing, and machine learning potentially offer an exponential speedup over all known classical algorithms. However, there also exist obstacles to obtaining this potential speedup in useful problem instances. The essential obstacle for quantum differential equation solving is that outputting useful information may require difficult postprocessing, and the essential obstacle for quantum data processing and machine learning is that inputting the data is a difficult task just by itself. In this study, we demonstrate that, when combined, these difficulties solve one another. We show how the output of quantum differential equation solving can serve as the input for quantum data processing and machine learning, allowing dynamical analysis in terms of principal components, power spectra, and wavelet decompositions. To illustrate this, we consider continuous-time Markov processes on epidemiological and social networks. These quantum algorithms provide an exponential advantage over existing classical Monte Carlo methods.

关键词： Machine learning Quantum algorithms Quantum computation Markovian processes Numerical techniques

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Acoustic Emission Recognition Based on a Two-Streams Convolutional Neural Network

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computers, Materials & Continua 2020年第7期64卷 515-525页

作者： Weibo Yang Weidong Liu Jinming Liu Mingyang Zhang School of Information and Communication Engineering Nanjing Institute of TechnologyNanjing211167China School of Information and Control Engineering China University of Mining and TechnologyXuzhou221000China School of Information Science and Engineering Southeast UniversityNanjing211100China Department of Electrical and Computer Engineering National University of Singapore119077Singapore

The Convolutional Neural Network(CNN)is a widely used deep neural *** with the shallow neural network,the CNN network has better performance and faster computing in some image recognition *** can effectively avoid the problem that network training falls into local *** present,CNN has been applied in many different fields,including fault diagnosis,and it has improved the level and efficiency of fault *** this paper,a two-streams convolutional neural network(TCNN)model is *** on the short-time Fourier transform(STFT)spectral and Mel Frequency Cepstrum Coefficient(MFCC)input characteristics of two-streams acoustic emission(AE)signals,an AE signal processing and classification system is constructed and compared with the traditional recognition methods of AE signals and traditional CNN *** experimental results illustrate the effectiveness of the proposed *** with single-stream convolutional neural network and a simple Long Short-Term Memory(LSTM)network,the performance of TCNN which combines spatial and temporal features is greatly improved,and the accuracy rate can reach 100%on the current database,which is 12%higher than that of single-stream neural network.

关键词： Convolutional neural network acoustic emission fault detection

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Data-Based Actuator Selection for Optimal control Allocation

Data-Based Actuator Selection for Optimal Control Allocation

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IEEE Conference on Decision and control

作者： Filippos Fotiadis Kyriakos G. Vamvoudakis Zhong-Ping Jiang School of Aerospace Engineering Georgia Institute of Technology Atlanta GA USA Department of Electrical and Computer Engineering Control and Networks Lab Tandon School of Engineering New York University Brooklyn USA

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

In this work, we consider an actuator redundant system, i.e., a system with more actuators than the number of effective control inputs, and bring together connections between control allocation, actuator selection, and learning. In this kind of systems, the actuator commands can be chosen to meet a given control objective while still having leftover degrees of freedom to use towards minimizing the overall actuation energy. We show that this energy can be further minimized by optimally selecting the actuators themselves, which we perform in two different scenarios; first, in the case where the control objective is not known beforehand; and second, in the case where the control objective is defined to be a stabilizing state feedback controller. To relax the requirement for knowledge of the system’s plant matrix, we compose a novel learning mechanism based on policy iteration, which computes the anti-stabilizing solution to an associated algebraic Riccati equation using trajectory data. Simulations are performed that demonstrate our approach.

关键词： Actuators State feedback Computational modeling Riccati equations Optimal control Mathematical models Real-time systems

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Modelling and Experimental Characteristics of Photovoltaic Modules in Typical Days at an Actual Photovoltaic Power Station 4

Modelling and Experimental Characteristics of Photovoltaic M...

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4th IEEE International Conference on Automation, Electronics and electrical engineering, AUTEEE 2021

作者： Lodhi, Ehtisham Yang, Pu Wang, Lina Lodhi, Zeeshan Khan, M. Adil Muhammad, Sijjad Tamir, Tariku Sinshaw Beihang University School of Automation Science and Electrical Engineering Beijing100191 China COMSATS University Islamabad Department of Electrical and Computer Engineering Abbottabad Campus 22060 Pakistan Chang'An University Department of Computer and Technology Xi'an710062 China Real Engineering Services Deans Trade Center Peshawar25000 Pakistan Institute of Automation Chinese Academy of Sciences The SKL For Management and Control of Complex Systems Beijing100190 China

ISBN: (纸本)9781665428392

Solar energy among the renewable energy sources (RES) plays an important role in fulfilling any country's energy demands. When the sunlight irradiates the photovoltaic (PV) modules, part of the light energy is directly converted into electric energy. Another part is converted into heat energy, which increases the working temperature and affects the output performance characteristics of the PV module. This paper proposes a dynamic mathematical model of PV modules working on solar irradiance and ambient temperature by calculating the PV module model parameters under different environmental conditions. The dynamic model of the PV module is suggested to simulate PV modules' output characteristics (voltage and current) under any environmental conditions. By employing MATLAB/Simulink, a PV output characteristics program is compiled. The whole day output characteristics of PV strings of a real PV power station are tested and compared with simulation results. The results depict that the established model can effectively reflect PV modules' output characteristics in actual operation and can be used for dynamic simulation of PV power generation. © 2021 IEEE.

关键词： Solar energy

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Convergence Analysis of the Best Response Algorithm for Time-Varying Games

Convergence Analysis of the Best Response Algorithm for Time...

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IEEE Conference on Decision and control

作者： Zifan Wang Yi Shen Michael M. Zavlanos Karl H. Johansson Division of Decision and Control Systems School of Electrical Enginnering and Computer Science KTH Royal Institute of Technology and also with Digital Futures Stockholm Sweden Department of Mechanical Engineering and Materials Science Duke University Durham NC USA

This paper studies a class of strongly monotone games involving non-cooperative agents that optimize their own time-varying cost functions. We assume that the agents can observe other agents' historical actions and choose actions that best respond to other agents' previous actions; we call this a best response scheme. We start by analyzing the convergence rate of this best response scheme for standard time-invariant games. Specifically, we provide a sufficient condition on the strong monotonicity parameter of the time-invariant games under which the proposed best response algorithm achieves exponential convergence to the static Nash equilibrium. We further illustrate that this best response algorithm may oscillate when the proposed sufficient condition fails to hold, which indicates that this condition is tight. Next, we analyze this best response algorithm for time-varying games where the cost functions of each agent change over time. Under similar conditions as for time-invariant games, we show that the proposed best response algorithm stays asymptotically close to the evolving equilibrium. We do so by analyzing both the equilibrium tracking error and the dynamic regret. Numerical experiments on economic market problems are presented to validate our analysis.

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Distributed Coordinated Optimal control for Synchronous Operation of Virtually Coupled Train Set

Distributed Coordinated Optimal Control for Synchronous Oper...

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International Conference on Intelligent Transportation

作者： Xiaolin Luo Dongming Wang Wei Wang Yindong Ji Hongjie Liu State Key Laboratory of Advanced Rail Autonomous Operation Beijing Jiaotong University Beijing China Department of Electrical and Computer Engineering University of California Riverside CA USA Traffic Control Technology Co. Ltd Beijing China Department of Automation Tsinghua University Beijing China School of Automation and Intelligence Beijing Jiaotong University Beijing China

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

Virtual coupling technologies have been widely studied in recent years, due to their merits of flexibility and efficiency. However, to operate a virtually coupled train set (VCTS) as a single train, the synchronous operation is still a critical problem to be addressed, especially in arriving. To this end, we propose a distributed coordinated optimal control (DCOC) approach integrated with distributed optimization and model predictive control. First, we formulate a globally coordinated optimal control problem for VCTS, where the states of units in VCTS are coupled in the cost and constraints. Then, we design a distributed algorithm to solve the global problem, where local problems are decoupled into ones with fewer dimensions. Next, we employ distributed model predictive control to guarantee the satisfaction of safety constraints before the convergence of the distributed algorithm. Finally, experimental results demonstrate that our approach can achieve stable and synchronous operation for VCTS and save by 90% computation time than existing centralized approaches.

关键词： Optimal control Robustness Safety Topology Synchronization Distributed algorithms Optimization Intelligent transportation systems Predictive control Convergence

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On the Approximation of the Quotient of Two Gaussian Densities for Multiple-Model Smoothing

On the Approximation of the Quotient of Two Gaussian Densiti...

引用

International Conference on Information Fusion

作者： Yi Liu Xi Li Le Yang Lyudmila Mihaylova Yanbo Xue Department of Electrical and Computer Engineering University of Canterbury Christchurch NZ State Key Lab of Complex Electromagnetic Environment Effects on Electronics and Information Systems National University of Defense Technology Changsha China Department of Automatic Control and System Engineering University of Sheffield Sheffield UK Career Science Lab Beijing China

The quotient of two multivariate Gaussian densities can be written as an unnormalized Gaussian density, which has been applied in some recently developed multiple-model fixed-interval smoothing algorithms. However, this expression is invalid if instead of being positive definite, the covariance of the unnormalized Gaussian density is indefinite (i.e., it has both positive and negative eigenvalues) or undefined (i.e., computing it requires inverting a singular matrix). This paper considers approximating the quotient of two Gaussian densities in this case using two different approaches to mitigate the caused numerical problems. The first approach directly replaces the indefinite covariance of the unnormalized Gaussian density with a positive definite matrix nearest to it. The second approach computes the approximation through solving, using the natural gradient, an optimization problem with a Kullback-Leibler divergence-based cost function. This paper illustrates the application of the theoretical results by incorporating them into an existing smoothing method for jump Markov systems and utilizing the obtained smoothers to track a maneuvering target.

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