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Distributed design of deterministic discrete-time privacy preserving average consensus for multi-agent systems through network augmentation

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arXiv 2021年

作者： Ramos, Guilherme Pedro Aguiar, A. Kar, Soummya Pequito, Sérgio The Department of Electrical and Computer Engineering Faculty of Engineering University of Porto Portugal The Department of Electrical and Computer Engineering Carnegie Mellon University United States The Delft University of Technology The Delft Center for Systems and Control Netherlands

Average consensus protocols emerge with a central role in distributed systems and decision-making such as distributed information fusion, distributed optimization, distributed estimation, and control. A key advantage of these protocols is that agents exchange and reveal their state information only to their neighbors. Yet, it can raise privacy concerns in situations where the agents' states contain sensitive information. In this paper, we propose a novel (noiseless) privacy preserving distributed algorithms for multi-agent systems to reach an average consensus. The main idea of the algorithms is that each agent runs a (small) network with a crafted structure and dynamics to form a network of networks (i.e., the connection between the newly created networks and their interconnections respecting the initial network connections). Together with a re-weighting of the dynamic parameters dictating the inter-agent dynamics and the initial states, we show that it is possible to ensure that the value of each node converges to the consensus value of the original network. Furthermore, we show that, under mild assumptions, it is possible to craft the dynamics such that the design can be achieved in a distributed fashion. Finally, we illustrate the proposed algorithm with examples. Copyright © 2021, The Authors. All rights reserved.

关键词： Multi agent systems

Evolving Linear Neural Networks for Features Space Dimensionality Reduction

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Evolving Linear Neural Networks for Features Space Dimension...

International Joint Conference on Neural Networks

作者： Yury Tsoy Computer Engineering Department Tomsk Polytechnic University Department of Economical Mathematics Informatic and Statistics Tomsk State University of Control Systems and Radioelectronics

ISBN: (纸本)9781467314886

Principal Components Analysis (PCA) is one of the most wide-spread methods for dimensionality reduction, which is being applied in many research and problem domains. So far a lot of approaches to compute data matrix eigenvectors, which correspond to the Principal Components, were proposed, among which numerical methods and Hebbian-based learning for neural networks, including Generalized Hebbian Algorithm. In this paper a novel way for computing eigenvectors using evolving linear neural networks is introduced, which is not relying upon correlation between nodes, but uses special fitness function instead. Early removal of the low-informative linear subspaces is applied, which reduces computational complexity of the method, and besides eigenvectors coordinates are computed approximately to improve convergence and speed. The latter gave rise to the approach's name: pseudo-PCA. Experimental results show that not looking at inexact eigenvectors the approach allows effective reduction of the features space dimensionality with acceptable classification accuracy compared to some "classical" and modern approaches to solve classification problems.

关键词： Eigenvector dimensionality reduction Neural network Numerical methods complexity classes principal components analysis Ganglia Converge

Active Exploration in Iterative Gaussian Process Regression for Uncertainty Modeling in Autonomous Racing

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arXiv 2023年

作者： Benciolini, Tommaso Tang, Chen Leibold, Marion Weaver, Catherine Tomizuka, Masayoshi Zhan, Wei The Automatic Control Engineering The Technical University of Munich Germany The Department of Computer Science The University of Texas AustinTX United States The Department of Mechanical Engineering The University of California BerkeleyCA United States

Autonomous racing creates challenging control problems, but Model Predictive control (MPC) has made promising steps toward solving both the minimum lap-time problem and head-to-head racing. Yet, accurate models of the system are necessary for model-based control, including models of vehicle dynamics and opponent behavior. Both dynamics model error and opponent behavior can be modeled with Gaussian Process (GP) regression. GP models can be updated iteratively from data collected using the controller, but the strength of the GP model depends on the diversity of the training data. We propose a novel active exploration mechanism for iterative GP regression that purposefully collects additional data at regions of higher uncertainty in the GP model. In the exploration, a MPC collects diverse data by balancing the racing objectives and the exploration criterion;then the GP is re-trained. The process is repeated iteratively;in later iterations, the exploration is deactivated, and only the racing objectives are optimized. Thus, the MPC can achieve better performance by leveraging the improved GP model. We validate our approach in the highly realistic racing simulation platform Gran Turismo Sport of Sony Interactive Entertainment Inc for a minimum lap time challenge, and in numerical simulation of head-to-head. Our active exploration mechanism yields a significant improvement in the GP prediction accuracy compared to previous approaches and, thus, an improved racing performance. © 2023, CC BY-NC-ND.

关键词： Model predictive control

Adaptive Generalized Synchronization of Two Nonidentical Hyperchaotic systems with Unknown Parameters

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Adaptive Generalized Synchronization of Two Nonidentical Hyp...

The 2nd IEEE International Conference on Advanced computer control(第二届先进计算机控制国际会议 ICACC 2010)

作者： Yan Zhao Tieyan Zhang Lihua Miao Qiuye Sun Department of Automatic Control Engineering Shenyang Institute of Engineering Shenyang China Fundamental Teaching Department of Computer Mathematics and Physics Shenyang Medical College Sheny College of Information Science and Engineering Northeastern University Shenyang China

ISBN: (纸本)9781424458455;9781424458486;9781424458479;9781424458462

This paper considers the generalized synchronization problems of two different hyperchaotic systems with unknown parameters. By denoting the synchronization error signals as the difference between the state variables of the drive system and the state variables of the response system, the hyperchaotic synchronization problem is transformed into the stabilization control problem of the origin of the synchronization error system. Based on the Lyapunov stability, a new adaptive synchronization controller is designed and the asymptotic stability of the origin of the synchronization error system is proved, simulation results illustrate the correctness of the proposed generalized synchronization method.

关键词： Lu hyperchaotic system Generalized Henon-Heiles hyperchaotic system adaptive generalized synchronization unknown parameters

On the efficiency of random walk approach to noise reduction in color images

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On the efficiency of random walk approach to noise reduction...

IEEE International Conference on Image Processing

作者： B. Smolka M. Szczepanski K.N. Plataniotis A.N. Venetsanopoulos Department of Automatic Control Silesian University of Technology Gliwice Poland Edward S. Rogers Sr. Department of Electrical and Computer Engineering University of Toronto Toronto Canada

We propose a new algorithm of noise reduction in color images. The new technique of multichannel image enhancement is capable of reducing impulse and Gaussian noise and it outperforms the basic methods based on vector median used for noise reduction in color images. A new smoothing operator, based on a random walk model and on a fuzzy similarity measure between pixels connected by a digital geodesic path is introduced. The efficiency of the proposed method was tested on the standard color images using the widely used objective image quality.

关键词： Noise reduction Color Filters Gaussian noise Level measurement Filtering Image enhancement Smoothing methods Testing Measurement standards

A least-squares interpretation of the single-stage maximization criterion for multichannel blind deconvolution

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A least-squares interpretation of the single-stage maximizat...

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

作者： S. Ohno Y. Inouye Department of Mathematics and Computer Science Shimane University Shimane Japan Department of Electronic and Control Systems Engineering Shimane University Shimane Japan

In order to attain multichannel blind deconvolution of linear time-invariant nonminimum-phase dynamic systems. Inouye and Habe (see Proc. IEEE Signal Processing Workshop on Higher-Order Statistics, p.96-100, 1995) proposed 1995 a single-stage maximization criterion. The criterion function is the sum of squared fourth-order cumulants of the equalizer outputs, and the coefficients of the equalizer are determined at once. On the other hand, one of possible approaches for multichannel blind deconvolution is to construct an equalizer based on the system identified by higher-order cumulant-matching. In this paper, it is shown that the single-stage maximization criterion is equivalent to a least-squares fourth-order cumulant-matching criterion after multichannel pre-whitening of channel outputs. This result provides us with an important interpretation of the single-stage maximization criterion.

关键词： Equalizers Deconvolution Vectors Signal processing Filters Transfer functions Wideband Speech enhancement Speech processing Image enhancement

Pareto genetic path planning hybridized with multi-objective Dijkstra's algorithm

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Pareto genetic path planning hybridized with multi-objective...

International Conference on System Theory, control, and Computing (ICSTCC)

作者： Lavinia Ferariu Corina Cîmpanu Department of Automatic Control and Applied Informatics “Gheorghe Asachi” Technical University of Iasi Iasi Romania Department of Computer Engineering “Gheorghe Asachi” Technical University of Iasi Iasi Romania

This paper presents an evolutionary approach to multi-objective path planning. The paths are defined on continuous scenes with disjoint and/or non-convex obstacles, for robots moving towards their destinations along linearly piecewise trajectories with any number of vertices. The fastest feasible route is genetically selected via a simultaneous minimization of path length and path steering angle. In order to assure an effective partial sorting of the potential solutions, the genetic algorithm makes use of a self-adaptive Pareto ranking scheme, based on individuals' grouping and dominance analysis. Before ranking, all the unfeasible solutions are corrected, by replacing the sub-paths which intersect the obstacles. The feasible segments are chosen from graphs generated with Delaunay triangulation, by applying a new multi-objective ranking-based fastest path procedure derived from the classical Dijsktra's algorithm. This new procedure is compatible with any nonlinear objective functions and allows using the same ranking scheme during the evolutionary search and correction of paths, thus making possible to guarantee the feasibility of the paths without any significant intrusion in the evolutionary exploration. The proposed algorithm can also be used for solving any graph - based path planning problem, involving any number of objectives. The experiments show the usefulness of the suggested techniques on working scenes with different layouts of obstacles.

关键词： Genetic algorithms Trajectory Sociology Statistics Algorithm design and analysis Optimization

UNav-Sim: A Visually Realistic Underwater Robotics Simulator and Synthetic Data-generation Framework

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arXiv 2023年

作者： Amer, Abdelhakim Álvarez-Tuñón, Olaya Ugurlu, Halil Ibrahim le Fevre Sejersen, Jonas Brodskiy, Yury Kayacan, Erdal The Department of Electrical Engineering and Computer Engineering Aarhus University Aarhus8200 Denmark EIVA a/s Skanderborg8660 Denmark The Automatic Control Group Department of Electrical Engineering and Information Technology Paderborn University Paderborn Germany

Underwater robotic surveys can be costly due to the complex working environment and the need for various sensor modalities. While underwater simulators are essential, many existing simulators lack sufficient rendering quality, restricting their ability to transfer algorithms from simulation to real-world applications. To address this limitation, we introduce UNav-Sim, which, to the best of our knowledge, is the first simulator to incorporate the efficient, high-detail rendering of Unreal Engine 5 (UE5). UNav-Sim is open-source1 and includes an autonomous vision-based navigation stack. By supporting standard robotics tools like ROS, UNav-Sim enables researchers to develop and test algorithms for underwater environments efficiently. © 2023, CC BY.

关键词： Data visualization

Non-uniqueness of stationary solutions in extremum seeking control

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arXiv 2018年

作者： Trollberg, Olle Jacobsen, Elling W. Department of Automatic Control Kth Royal Institute of Technology School of Electrical Engineering and Computer Science Osquldas v. 10 Stockholm100 44 Sweden

Extremum seeking control (ESC) is a classical adaptive control method for steady-state optimization, purely based on output feedback and without the need for a plant model. It is well known that the extremum seeking control loop, under certain mild conditions on the controller, has a stable stationary periodic solution in the vicinity of an extremum point of the steady-state input-output map of the plant. However, this is a local result only and this paper investigates whether this solution is necessarily unique given that the underlying optimization problem is convex. Wefirst derive a necessary condition that any stationary solution of the ESC loop must satisfy. For plants in which the extremum point is due to a purely static nonlinearity, such as in Hammerstein or Wiener plants, the condition involves the steady-state gradient. However, for more general plants the necessary condition involves the phase lag of the locally linearized plant, indicating the possible existence of solutions without any relationship to optimality. Combining the derived necessary condition with the existence of a local solution close to the optimum, we employ the implicit function theorem and bifurcation theory to trace out branches of stationary solutions for varying loop parameters. The focus is on solutions corresponding to limit cycles of the same period as the forcing. We derive conditions on when these branches bifurcate, resulting in multiple stationary solutions. The results show that cyclic fold bifurcations may exist, resulting in the existence of multiple stationary period one solutions, of which only one is related to the optimality conditions. We illustrate the results through an example in which the conversion in a chemical reactor is optimized using ESC. We show that at least_ve stationary solutions may exist simultaneously for realistic control parameters, and that several of these solutions are stable. One consequence of the non-uniqueness is that one in general needs to

关键词： Bifurcation (mathematics)