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Frequency Domain optimization Design of the Dual-Loop Controller for Piezoelectric Tube Scanners With Compound Dynamics

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 996-1005页

作者： Meng, Yixuan Pan, Minyu Li, Linlin Wang, Xiangyuan Zhu, Zhiwei Zhu, Limin Shanghai Jiao Tong Univ Sch Mech Engn State Key Lab Mech Syst & Vibrat Shanghai 200240 Peoples R China Weill Cornell Med Dept Anesthesiol New York NY 10065 USA Nanjing Univ Sci & Technol Sch Mech Engn Nanjing 210094 Jiangsu Peoples R China Shanghai Key Lab Networked Mfg & Enterprise Infor Shanghai 200240 Peoples R China

The dual-loop controller (DLC) with both inner damping and outer tracking controllers has demonstrated exceptional performance in high-speed control of piezo-actuated nano-positioners. However, the accurate low-order model of the plant is imperative for designing the damping controller, according to the conventional DLC design principle. This limits its application in controlling piezoelectric tube scanners (PTSs) with compound dynamics. To handle this problem, this study introduces a frequency domain method for designing the DLC based on the frequency response data of the PTS. This method mitigates issues related to modeling errors. Specifically, the Nyquist diagram is employed to provide the stability boundary for parameters determination. A constraint optimization problem is formulated to achieve a high bandwidth with a flat amplitude frequency response. And the differential evolution algorithm is then adopted to find an optimal solution. Experimental validation on a PTS comfirms the effectiveness of this frequency domain design method. The results indicate that the control bandwidth of the optimized DLC achieves 848 Hz for a PTS with the first resonant frequency of 702 Hz. The superiorities of the DLC designed by the proposed optimization method are also validated via comparative tracking experiments involving step and triangular trajectories.

关键词： Damping optimization Bandwidth Stability analysis Compounds Frequency-domain analysis Design methodology Damping and tracking control frequency response data constraint optimization problem piezoelectric tube scanner high-bandwidth control

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A multi-objective optimization system for mobile gateways selection in vehicular Ad-Hoc networks

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COMPUTERS & ELECTRICAL ENGINEERING 2019年 73卷 289-303页

作者： Retal, Sara Idrissi, Abdellah Mohammed V Univ Rabat IPSS Comp Sci Dept Fac Sci Rabat Morocco

Vehicular ad-hoc networks provide essential Internet services to users. In consequence, mobile gateways are deployed to guarantee access to the Internet for the entire network. However, the selection of the best gateway taking into account some constraints and trying to reach some high-level objectives is a significant issue in mobile gateways discovery. The number of connected client vehicles must be maximized while a fair load distribution must be performed. For this purpose, we propose a multi-objective optimization system for mobile gateways selection based on two models using different solving strategies allowing the decision maker to choose the adequate solution. The solving approaches are evaluated and compared, and the simulations prove its efficiency compared to that found in the literature. The results show the effectiveness of the system in supporting a decision maker in solving a gateway selection problem and finding a fair solution in case there are no user preferences. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Vehicular ad-Hoc network Mobile gateways selection Multi-Objective optimization constraint optimization problem Integer optimization problem

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Optimal Design of Configuration Scheme for Integrated Modular Avionics Systems With Functional Redundancy Requirements

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IEEE SYSTEMS JOURNAL 2021年第2期15卷 2665-2676页

作者： Chu, Jiayun Zhao, Tingdi Jiao, Jian Chen, Zhiwei Beihang Univ Sch Reliabil & Syst Engn Beijing 100191 Peoples R China

Integrated modular avionics architecture has become the most attractive idea to enhance system capability as well as improve efficiency. However, this architecture makes designing a system configuration scheme difficult because multiple resources are shared to support different functions that may have been irrelevant before. This article introduces a method to optimize the configuration scheme for an integrated modular avionics system while considering functional redundancy requirements. Not only the allocation scheme, which illustrates the relationships between functions and shared resources is taken into account, but also the resource scheme, which illustrates how many shared resources are enough to cover the functional requirements is considered as a part of the configuration scheme. Toward a generic integrated modular avionics system with functional redundancy requirements, we first give a configuration scheme model as well as its related schedulability and reliability constraints. Then, the optimization process of the configuration scheme is formalized as a combination of a constraint satisfaction problem and a constraint optimization problem. Specifically, it is decomposed into two steps including: 1) finding a resource scheme which has the lowest cost but still satisfies the constraints based on a forward checking algorithm;2) finding its corresponding allocation scheme which distributes the targeted functions to different resources as evenly and low-coupled as possible based on the NSGA-II algorithm. Finally, an example is used to prove the effectiveness of the method. The optimization method can serve as a guide to design a configuration scheme for the integrated modular avionics system.

关键词： Aerospace electronics Redundancy Resource management constraint optimization Aircraft Configuration scheme constraint optimization problem constraint satisfaction problem functional redundancy hierarchical scheduling integrated modular avionics

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Target to sensor allocation: A hierarchical dynamic Distributed constraint optimization approach

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COMPUTER COMMUNICATIONS 2013年第9期36卷 1024-1038页

作者： Semnani, Samaneh Hosseini Basir, Otman A. Univ Waterloo Dept Elect & Comp Engn Waterloo ON N2L 3G1 Canada

Distributed target allocation and tracking is an important research problem. This problem is complex but has many applications in various domains, including, pervasive computing, surveillance and military systems. In this paper we propose a technique to solve the target to sensor allocation problem by modeling the problem as a hierarchical Distributed constraint optimization problem (HDCOP). Distributed Constrain optimization problems (DCOPs) tend to be computationally expensive and often intractable, particularly in large problem spaces such as Wireless Sensor Networks (WSNs). To address this challenge we propose changing the sensor to target allocation as a hierarchical set of smaller DCOPs with a shared system of constraints. Thus, we avoid significant computational and communication costs. Furthermore, in contrast to other DCOP modeling methods, a non-binary variable modeling is employed to reduce the number of intra-agent constraints. To evaluate the performance of the proposed approach, we use the surveillance system of the Regional Waterloo Airport as a test case. Two DCOP solution algorithms are considered, namely, the Distributed Breakout Algorithm (DBA) and the Asynchronous Distributed optimization (ADOPT). We evaluate the computational and communication costs of these two algorithms for solving the target to sensor allocation problem using the proposed hierarchical formulation. We compare the performance of these algorithms with respect to the incurred computational and communication costs. (C) 2013 Elsevier B.V. All rights reserved.

关键词： constraint optimization problem Multi-agent systems Sensor networks Target to sensor allocation

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A new metaheuristic based on vapor-liquid equilibrium for solving a new patient bed assignment problem

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EXPERT SYSTEMS WITH APPLICATIONS 2020年 158卷 113506-113506页

作者： Taramasco, Carla Crawford, Broderick Soto, Ricardo Cortes-Toro, Enrique M. Olivares, Rodrigo Univ Valparaiso Valparaiso Chile Pontificia Univ Catolica Valparaiso Valparaiso Chile Univ Playa Ancha Valparaiso Chile

Bio-inspired computing is an emerging paradigm which is based on the basics and inspiration of natural phenomena to design new and robust competing techniques. Various nature science areas have motivated the inspiration for the design of new intelligent systems. Chemical engineering is one of them. In Chemistry, the vapor-liquid equilibrium process describes the distribution of chemical species combining two essential phases: vapor phase and liquid phase. Using a binary system of compounds is possible to simulate a search process based on the equilibrium between both phases. In this paper, we propose a new algorithm inspired by this chemical phenomenon for solving a new patient bed assignment problem. This problem consists of assigning patients to beds by considering relevant medical requirements trying to maximize the most covered soft constraints. For that, we take a traditional model, and we transform it by using the constraint optimization paradigm. We test our algorithm on 30 benchmarks taken of Chilean health services. To verify results, we perform statistical comparatives with artificial bee algorithm, ant colony optimization, the bat method, cuckoo search, genetic algorithm, particle swarm optimization, and a random strategy. Computational experiments illustrate that the VLE algorithm properly solved 30 instances, finding all global optimal. In nineteen instances, VLE converged towards the best solution in its median value. In ten instances, the median, the average and the best value, all of them achieved the global optimal. Now, when comparing VLE against other techniques, we can note that VLE is sur passed by the artificial bee colony in two instances only. The rest of the results show VLE as a robust algorithm able to suppress classical, such as genetic algorithm and particle swarm optimization. (c) 2020 Elsevier Ltd. All rights reserved.

关键词： Vapor-liquid equilibrium algorithm Metaheuristic Patient bed assignment problem constraint optimization problem

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Towards more efficient local search algorithms for constrained clustering

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INFORMATION SCIENCES 2023年 621卷 287-307页

作者： Gao, Jian Tao, Xiaoxia Cai, Shaowei Northeast Normal Univ Coll Informat Sci & Technol Changchun Peoples R China Dalian Univ Sci & Technol Sch Informat Sci & Technol Dalian Peoples R China Chinese Acad Sci Inst Software State Key Lab Comp Sci Beijing Peoples R China

Constrained clustering extends clustering by integrating user constraints, and aims to determine an optimal assignment under the constraints. In this paper, we propose a local search algorithm called FastCCP to solve the constrained clustering problem. In the algo-rithm, instances connected by must-link constraints are first merged into nodes, and then, a local search method is performed to handle the cannot-link constraints while minimizing the Within-Cluster Sum of Squares (WCSS). Several strategies are proposed to enhance the solution diversity and achieve a trade-off between constraint satisfaction and WCSS min-imization during the search. Furthermore, a node-filtering strategy is proposed to improve the efficiency of the algorithm. Experiments are performed on benchmark datasets to eval-uate our algorithm. The comparative results indicate that our algorithm outperforms state-of-the-art algorithms in terms of both the solution quality and CPU runtime.(c) 2022 Elsevier Inc. All rights reserved.

关键词： Constrained clustering constraint optimization problem Local search Search strategy

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Hybrid genetic algorithm for engineering design problems

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2017年第1期20卷 263-275页

作者： Yan, Xuesong Liu, Hanmin Zhu, Zhixin Wu, Qinghua China Univ Geosci Sch Comp Sci Wuhan 430074 Hubei Peoples R China Wuhan Inst Ship Bldg Technol Wuhan 430050 Hubei Peoples R China WuHan Inst Technol Fac Comp Sci & Engn Wuhan 430074 Hubei Peoples R China

Genetic algorithm has made lots of achievements in the aspect of solving constrained optimization problems, but engineering design problem is one of typical optimization problems for complicated constraint condition and correlative variable parameters. The results optimized by classical mathematical optimization method are often poor. In this paper, one hybrid search strategy was designed aiming to the defects of simple genetic algorithm. With improvement, the algorithm is less likely to trap in local optimum. And the simulation test shows that the algorithm for engineering design problem has made great effects in stability and convergence precision.

关键词： Genetic algorithm Engineering design constraint optimization problem

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Distributed Scheme for Unit Commitment problem Using constraint Programming and ADMM

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IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES 2022年第5期E105A卷 788-798页

作者： Inoue, Yuta Miyamoto, Toshiyuki Osaka Univ Suita Osaka 5650871 Japan

The unit commitment problem (UCP) is the problem of deciding up/down and generation-level patterns of energy production units. Due to the expansion of distributed energy resources and the liberalization of energy trading in recent years, solving the distributed UCP (DUCP) is attracting the attention of researchers. Once an up/down pattern is determined, the generation-level pattern can be decided distributively using the alternating direction method of multipliers (ADMM). However, ADMM does not guarantee convergence when deciding both up/down and generation-level patterns. In this paper, we propose a method to solve the DUCP using ADMM and constraint optimization programming. Numerical experiments show the efficacy of the proposed method.

关键词： distributed energy management system distributed unit commitment constraint optimization problem ADMM

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Multi-Target Engagement in Complex Mobile Surveillance Sensor Networks

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UNMANNED SYSTEMS 2017年第1期5卷 31-43页

作者： Semnani, Samaneh Hosseini Basir, Otman A. Univ Waterloo Elect & Comp Engn Dept Waterloo ON Canada

Efficient use of the network's resources to collect information about objects (events) in a given volume of interest (VOI) is a key challenge in large-scale sensor networks. Multi-sensor multi-target tracking in surveillance applications is an example where the network's success in tracking targets, efficiently and effectively, hinges significantly on the network's ability to allocate the right set of sensors to the right set of targets so as to achieve optimal performance which minimizes the number of uncovered targets. This task can be even more complicated when both the sensors and the targets are mobile. To ensure timely tracking of mobile targets, the surveillance sensor network needs to perform the following tasks in real-time: (i) target-to-sensor allocation;(ii) sensor mobility control and coordination. The computational complexity of these two tasks presents a challenge, particularly in large scale dynamic network applications. This paper proposes a formulation based on the Semi-flocking algorithm and the distributed constraint optimization problem (DCOP). The semi-flocking algorithm performs multi-target motion control and coordination, a DCOP modeling algorithm performs the target engagement task. As will be demonstrated experimentally in the paper, this algorithmic combination provides an effective approach to the multisensor/multi-target engagement problem, delivering optimal target coverage as well as maximum sensors utilization.

关键词： Surveillance systems biologically inspired computing constraint optimization problem semi-flocking algorithm hierarchical DCOP modeling sensor coordination mobility control target to sensor allocation sensor networks

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constraint Handling Using Tournament Selection: Abductive Inference in Partly Deterministic Bayesian Networks

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EVOLUTIONARY COMPUTATION 2009年第1期17卷 55-88页

作者： Galan, Severino F. Mengshoel, Ole J. Univ Nacl Educ Distancia Dept Artificial Intelligence Madrid 28040 Spain NASA Ames Res Ctr CMU Moffett Field CA 94035 USA

constraints occur in many application areas of interest to evolutionary computation. The area considered here is Bayesian networks (BNs), which is a probability-based method for representing and reasoning with uncertain knowledge. This work deals with constraints in BNs and investigates how tournament selection can be adapted to better process such constraints in the context of abductive inference. Abductive inference in BNs consists of finding the most probable explanation given some evidence. Since exact abductive inference is NP-hard, several approximate approaches to this inference task have been developed. One of them applies evolutionary techniques in order to find optimal or close-to-optimal explanations. A problem with the traditional evolutionary approach is this: As the number of constraints determined by the zeros in the conditional probability tables grows, performance deteriorates because the number of explanations whose probability is greater than zero decreases. To minimize this problem, this paper presents and analyzes a new evolutionary approach to abductive inference in BNs. By considering abductive inference as a constraint optimization problem, the novel approach improves performance dramatically when a BN's conditional probability tables contain a significant number of zeros. Experimental results are presented comparing the performances of the traditional evolutionary approach and the approach introduced in this work. The results show that the new approach significantly outperforms the traditional one.

关键词： constraint optimization problem genetic algorithm Bayesian network approximate abductive inference most probable explanation

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