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Hierarchic distributed stabilization of a class of three-dimensional formations for underactuated agents

IET CONTROL theory AND applications 2021年第3期15卷 472-488页

作者： El-Hawwary, Mohamed I. Cairo Univ Fac Engn Dept Elect Dept Elect Power Engn Giza 12613 Egypt KTH Royal Inst Technol Div Decis & Control Syst Stockholm Sweden

The paper presents a distributed hierarchic control design solving a formations problem in three dimension. The agents are modelled as thrust-underactuated rigid bodies. The class of formations addressed encompasses path following of closed convex paths with shape, size, orientation, relative displacements and relative agents' on-path angular positions all seen as formation parameters. This problem can be seen as a generalization of circular formation problems which acquired particular attention in the field of multi-agents, and the agents model used is one that usually models certain unmanned aerial vehicles and other autonomous vehicles. The solution relies on reduction-based set stabilization where the problem is broken down into simpler nested sub-problems. The results illustrate how addressing complex control problems using hierarchical set stabilization is natural, simplifies the solution, and provides useful properties. The results are illustrated via simulations.

关键词： Spatial variables control Aerospace control Mobile robots linear algebra (numerical analysis) Combinatorial mathematics Control system analysis and synthesis methods Stability in control theory Multivariable control systems

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An Overview of applications of Gaussian numerical methods 2

An Overview of Applications of Gaussian Numerical Methods

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2nd International conference on Smart Electronics and Communication, ICOSEC 2021

作者： Tewari, Yogya Hadap, Arti Soni, Payal Sharma, Muskan Shukla, Daksh Malanker, Shreya NMIMS MPSTME Dept. of Computer Engineering Mumbai India

ISBN: (纸本)9781665433686

numerical methods have widespread applications in Engineering and the Gaussian methods are extensively used for solving complex equations as these methods arrive at a solution without the need for matrix inversion, they save on computation cost and time. The various methods namely Gauss Elimination, Gauss Seidel and Gauss Jordan have vast applications in fluid chemistry, massive MIMO technology, puzzle solving, resolving RFID in wireless network, Real time character inverse kinematics, and many more. The paper explores various applications of Gaussian methods as well as compares them based on performance, speed and efficiency. © 2021 IEEE.

关键词： linear algebra

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New relaxed stability and stabilization conditions for T-S fuzzy systems with time-varying delays

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IET CONTROL theory AND applications 2021年第14期15卷 1849-1867页

作者： Zhou, Kun Wang, Binrui Qi, Shunan China Jiliang Univ Coll Mech & Elect Engn Hangzhou Peoples R China China Jiliang Univ Coll Modern Sci & Technol Hangzhou Peoples R China

This paper investigates the stability analysis and stabilization of T-S fuzzy systems with time-varying delays. First, a new augmented Lyapunov-Krasovskii functional is constructed, delay-dependent stability criteria in terms of linear matrix inequalities (LMIs) are obtained by combining them with the integral inequality technique and the reciprocally convex combination inequality. Based on the state space decomposition method, some piecewise membership functions are employed to approximate the membership functions. The piecewise membership functions can be locally represented in terms of the convex combinations of the supremum and infimum of some local basis functions. The boundary information of the membership functions is adequately taken into consideration in stability analysis, and then some relaxed membership-function-dependent stability results are obtained. Second, state feedback controllers for fuzzy systems with time-varying delays are presented under the imperfect premise-matching technique, whose membership functions and the number of fuzzy rules are allowed to be designed freely, consequently, the flexibility of controller design is improved. Finally, four numerical examples are given to demonstrate the effectiveness of the presented approaches.

关键词： stabilization conditions convex combinations delay-dependent stability criteria augmented Lyapunov-Krasovskii local basis functions Combinatorial mathematics time-varying systems Time-varying control systems stability control system synthesis Stability in control theory stability analysis Control system analysis and synthesis methods Nonlinear control systems Fuzzy control state feedback state space decomposition method fuzzy systems stability criteria algebra delays Lyapunov methods integral inequality technique nonlinear control systems fuzzy control linear matrix inequalities reciprocally convex combination inequality time-varying delays relaxed membership-function-dependent stability results piecewise membership functions Distributed parameter control systems fuzzy rules fuzzy set theory

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Global optimization control for nonlinear full-car active suspension system with multi-performances

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IET CONTROL theory AND applications 2021年第14期15卷 1882-1905页

作者： Chen, Chung-Cheng Chen, Yen-Ting Univ Technol City Coll Dongguan Dongguan Guangdong Peoples R China Natl Chung Hsing Univ Dept Elect Engn Taichung Taiwan 1 Wenchang RdLiaobu Town Dongguan 523419 Guangdong Peoples R China

In this article, based on feedback linearized approach and linear quadratic regulator optimization control approach using particle swarm optimization, the almost disturbance decoupling, input amplitude reduction, tuning parameter optimization, controllable convergence rate, improved suspension and globally exponential stability multi-performances of highly nonlinear multi-input multi-output full-car uncertain system are simultaneously achieved without applying any nonlinear function approximator including neural network approach and fuzzy logic approach. The article proposes a new method to achieve the optimal control matrices of LQR such that the composite controller can reduce the amplitudes of system control inputs. Determination of the LQR tuning parameters is conventionally determined via trial and error approach. In addition to being very troublesome, it is difficult to find the globally best tuning matrices with LQR method. This study has first proposed the convergence rate formula of the nonlinear system response as the fitness function of LQR approach by using PSO optimization algorithm to take the place of the conventional trial and error method and locally Jacobian linearized approach under the guarantee of globally exponential stability. The simulation results show how the full-car system can be controlled optimally and satisfactorily and confirm the superiority of the proposed method.

关键词： linear quadratic regulator optimization control approach conventional trial Optimisation techniques nonlinear systems system control inputs particle swarm optimisation improved suspension feedback linearized approach globally best tuning matrices full-car system error method fuzzy logic neural nets function approximation highly nonlinear multiinput multioutput neural network approach controllable convergence rate Mechanical components Stability in control theory control system synthesis linear quadratic control nonlinear system response Control system analysis and synthesis methods Nonlinear control systems PSO optimization algorithm error approach LQR approach convergence rate formula LQR tuning parameters matrix algebra globally exponential stability multiperformances asymptotic stability global optimization control optimal control matrices fuzzy logic approach disturbance decoupling feedback algebra nonlinear function approximator nonlinear control systems Interpolation and function approximation (numerical analysis) input amplitude reduction uncertain systems Optimal control particle swarm optimization locally Jacobian linearized approach LQR method suspensions (mechanical components) optimal control linearisation techniques parameter optimization composite controller nonlinear full-car active suspension system

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Inverse Scattering theory and Transmission Eigenvalues, Second Edition

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丛书名： CBMS-NSF Regional conference Series in Applied Mathematics

2022年

作者： Fioralba Cakoni David Colton Houssem Haddar

ISBN: (数字)9781611977424

ISBN: (纸本)9781611977417

Inverse scattering theory is a major theme in applied mathematics, with applications to such diverse areas as medical imaging, geophysical exploration, and nondestructive testing. The inverse scattering problem is both nonlinear and ill-posed, thus presenting challenges in the development of efficient inversion algorithms. A further complication is that anisotropic materials cannot be uniquely determined from given scattering data.

In the first edition of Inverse Scattering theory and Transmission Eigenvalues, the authors discussed methods for determining the support of inhomogeneous media from measured far field data and the role of transmission eigenvalue problems in the mathematical development of these methods.

In this second edition, three new chapters describe recent developments in inverse scattering theory. In particular, the authors

explore the use of modified background media in the nondestructive testing of materials and methods for determining the modified transmission eigenvalues that arise in such applications from measured far field data,
examine nonscattering wave numbers — a subset of transmission eigenvalues — using techniques taken from the theory of free boundary value problems for elliptic partial differential equations, and
discuss the dualism of scattering poles and transmission eigenvalues that has led to new methods for the numerical computation of scattering poles.

关键词： scattering theory inverse problems linear sampling method factorization method interior transmission problem transmission eigenvalues inverse spectral problems non-scattering wave numbers scattering poles

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Accelerating Parallel First-Principles Excited-State Calculation by Low-Rank Approximation with K-Means Clustering 51

Accelerating Parallel First-Principles Excited-State Calcula...

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51st International conference on Parallel Processing (ICPP)

作者： Jiang, Qingcai Chen, Junshi Wan, Lingyun Qin, Xinming Li, Jielan Liu, Jie An, Hong Hu, Wei Yang, Jinlong Univ Sci & Technol China Hefei Anhui Peoples R China

ISBN: (纸本)9781450397339

First-principles time-dependent density functional theory (TDDFT) is a powerful tool to accurately describe the excited-state properties of molecules and solids in condensed matter physics, computational chemistry and materials science. However, a perceived drawback in TDDFT calculations is its ultrahigh computational cost O(N-5 similar to N-6) and large memory usage O(N-4) especially for plane-wave basis set, confining its applications to large systems containing thousands of atoms. Here, we present a massively parallel implementation of linear-response TDDFT (LR-TDDFT) and reduce the complexity to O(N-3) by combining K-Means clustering based low-rank approximation with iterative eigensolve algorithm. Furthermore, we carefully design the parallel data and task distribution schemes to accommodate with the physical nature in different steps of the computation, also, several optimization methods are employed to effectively handle the matrix operations and data communications of constructing and diagonalizing the LR-TDDFT Hamiltonian. In particular, our method can significantly reduce the cost of computation and memory by nearly 2 orders of magnitude compared to conventional LR-TDDFT calculations. numerical results demonstrate that our implementation can gain an overall speedup of 10x and efficiently scale up to 12,288 CPU cores for large systems up to 4,096 atoms within dozens of seconds.

关键词： Quantum mechanic calculation Time-dependent density functional theory linear-response Iterative eigensolver Low-rank approximation Parallel implementation

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Sufficient Condition for the Existence of a Limit Cycle in Integrated Relay Output Feedback Systems 5

Sufficient Condition for the Existence of a Limit Cycle in I...

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5th IEEE conference on Control Technology and applications (IEEE CCTA)

作者： Yoon, Yongeun Choi, Woojin Agcy Def Dev Daejeon 34186 South Korea

ISBN: (纸本)9781728171401

An integrated relay output feedback (IROF) system is a piecewise linear system representing many electric and/or mechanical systems. Like other piecewise linear systems IROF inherently has limit-cycle oscillation(LCO), which plays a critical role in the performance of the system, entailing the necessity of reliable analysis on the LCO. However, due to the nature of the solution trajectory of LCO, we cannot safely apply existing methods on the analysis of LCO such as describing function, Floquet theory, or piecewise linear system analysis. This research provides a new theoretical approach with which to establish the sufficient condition of an LCO in the IROF system accompanying the analysis on its stability and main parameters. We start from confirming that the switching time between each switching plane is finite. Then, by the introduction of a discrete-time nonlinear system representing the Poincare map and the linear phase can we establish the sufficient condition for the existence of an LCO and its stability. Finally, we demonstrate the effectiveness and efficiency of the main results with a simple numerical example.

关键词： Piecewise linear techniques

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Quasi-bipartite synchronisation of multiple inertial signed delayed neural networks under distributed event-triggered impulsive control strategy

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IET CONTROL theory AND applications 2021年第12期15卷 1615-1627页

作者： Udhayakumar, K. Rihan, Fathalla A. Li, Xiaodi Rakkiyappan, R. Bharathiar Univ Dept Math Coimbatore Tamil Nadu India UAE Univ Coll Sci Dept Math Sci Al Ain 15551 U Arab Emirates Shandong Normal Univ Sch Math & Stat Jinan Peoples R China

The central concern of this paper is to study leader-following quasi-bipartite synchronisation of a multiple inertial signed neural networks with varying time-delay by utilising distributed event-triggered impulsive control scheme, where connections between adjacent nodes of the neural networks either positive or negative. The second-order neural networks, called inertial neural networks, can be transformed into differential equations of first-order by implementing suitable variable substitution. Under certain hypothesis about the node dynamics, signed graph theory and balanced topology of networks, some conditions are derived in terms of lower-dimensional linear matrix inequalities (LMIs) to achieve leader-following quasi-bipartite synchronisation. In addition, a basic algebraic condition is derived to estimate the theoretical upper bound for the error node. Finally, some numerical simulations are provided to illustrate the correctness of the theoretical results.

关键词： algebra Combinatorial mathematics Control system analysis and synthesis methods Stability in control theory

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Multiple extended stealth target tracking from image observation based on non-linear sub-random matrices approach

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IET RADAR SONAR AND NAVIGATION 2021年第3期15卷 274-293页

作者： Barbary, Mohamed Abd ElAzeem, Mohamed H. Alexandria Univ Dept Elect Engn Alexandria Egypt Egyptian Tech Res & Dev Ctr Cairo Egypt Arab Acad Sci Technol & Maritime Transport Dept Elect & Commun Cairo Egypt

The detection and tracking of extended stealth targets (ESTs) is a challenging task in radar technology, especially if from image observations because of the fluctuations of radar cross section. To overcome this challenge, multi-Bernoulli (MB) filter can be used to extract the extended target (ET) states in efficient and reliable manner. Recently, the MB-filter-based random matrices model (RMM) approach has been proposed for ellipsoidal ET tracking with additional state variables. However, RMM-MB filter is demonstrated with known detection profile, which is unsuitable for EST tracking. Thus, a joint detection and tracking of multiple ESTs based on track-before-detect (TBD) approach, which is an efficient way to track low-observable ESTs, is proposed. In EST-RMM-TBD scenarios, although the extension ellipsoid is efficient, it may not be accurate because of some missing useful information, such as size, shape, and orientation. To address this, a EST-sub-RMM-TBD composed of sub-ellipses is introduced, each representing an RMM. Based on such models, a sub-RMM-MB-TBD filter is used to estimate the kinematic states and extensions of sub-objects for each EST. Furthermore, a sequential Monte Carlo (SMC) implementation to estimate non-linear kinematic EST state is applied. The results indicate that the proposed SMC-sub-RMM-MB-TBD filter has more accurate cardinality estimation and smaller optimal sub-pattern assignment errors than the recent extended tracking filters.

关键词： Computer vision and image processing techniques nonlinear sub-random matrices approach nonlinear kinematic EST state extension ellipsoid recent extended tracking filters multiple ESTs image observation EST tracking Optical, image and video signal processing matrix algebra track-before-detect approach extended target states extended stealth targets algebra Radar equipment, systems and applications MB-filter-based random matrices model approach SMC-sub-RMM-MB-TBD filter stealth target Signal detection efficient manner Filtering methods in signal processing low-observable ESTs EST-RMM-TBD scenarios tracking filters known detection profile multiBernoulli filter RMM-MB filter additional state variables Integral transforms EST-sub-RMM-TBD radar technology Interpolation and function approximation (numerical analysis) object detection ellipsoidal ET tracking Image recognition radar cross-sections target tracking radar cross section Monte Carlo methods kinematic states filtering theory Other topics in statistics

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Analysis of alpha-SVRG under Strong Convexity

Analysis of alpha-SVRG under Strong Convexity

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International conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Sean Xiao Sangwoo Park Stefan Vlaski Department of Electrical Engineering Imperial College London

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Stochastic first-order methods for empirical risk minimization employ gradient approximations based on sampled data in lieu of exact gradients. Such constructions introduce noise into the learning dynamics, which can be corrected through variance-reduction techniques. There is increasing evidence in the literature that in many modern learning applications noise can have a beneficial effect on optimization and generalization. To this end, the recently proposed variance-reduction technique, α-SVRG [1] allows for fine-grained control of the level of residual noise in the learning dynamics, and has been reported to empirically outperform both SGD and SVRG in modern deep learning scenarios. By focusing on strongly convex environments, we first provide a unified convergence rate expression for α-SVRG under fixed learning rate, which reduces to that of either SGD or SVRG by setting α=0 or α=1, respectively. We show that α-SVRG has faster convergence rate compared to SGD and SVRG under suitable choice of α. Simulation results on linear regression validate our theory.

关键词： Risk minimization Simulation Noise linear regression Stochastic processes Focusing numerical models Speech processing Optimization Convergence

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