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An optimization-based Adaptive Sliding Mode Control for an Inverted Pendulum

IETE JOURNAL OF RESEARCH 2024年第1期70卷 542-557页

作者： Pathak, Rajeev Ranjan Mohanty, Sidhanta Sengupta, Anindita Indian Inst Engn Sci & Technol Dept Elect Engn Howrah 711103 W Bengal India

The work proposes an optimization-based robust adaptive gain sliding mode control technique to handle bounded behavior of uncertainties/perturbations without having the exact knowledge of the bound a priori. The constant of the sliding surface follows dynamical adaptive nature using three optimization techniques, i.e. Harmony Search algorithm, bacterial foraging optimization algorithm and Gradient Search algorithm to extenuate the extent of chattering. The gain of the switching control law, which is a part of the sliding mode control design, is calculated using the gain adaption technique. Lyapunov Stability Criterion is applied to assure the stability of the closed-loop system with adaptable gain and the optimized value of the sliding constant. An inverted Pendulum system has been taken to validate the proposition both in the simulation environment and the hardware platform.

关键词： Adaptive sliding mode control bacterial foraging optimization algorithm Gradient search algorithm Harmony search algorithm Inverted pendulum Sliding mode control

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Solving dynamic economic dispatch problem with security constraints using bacterial foraging PSO-DE algorithm

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2012年第1期39卷 56-67页

作者： Vaisakh, K. Praveena, P. Rao, S. Rama Mohana Meah, Kala Andhra Univ Coll Engn Dept Elect Engn Waltair 530003 Andhra Pradesh India York Coll Penn York PA USA

This paper presents a heuristic optimization methodology, namely, bacterial foraging PSO-DE (BPSO-DE) algorithm by integrating bacterial foraging optimization algorithm (BFOA), Particle Swarm optimization (PSO) and Differential Evolution (DE) for solving non-smooth non-convex Dynamic Economic Dispatch (DED) problem. The DED problem exhibits non-smooth, non-convex nature due to valve-point loading effects, ramp rate limits, spinning reserve capacity, prohibited operating zones and security constraints. The proposed hybrid method eliminates the problem of stagnation of solution with the incorporated PSO and DE operators in original bacterial foraging algorithm. It achieves global cost by selecting the bacterium with good foraging strategies. The bacteria with good foraging strategies are obtained in the updating process of every chemo-tactic step by the PSO operator. The DE operator fine tunes the solution obtained through bacterial foraging and PSO operator. A 3- and 7-unit systems for static economic dispatch, a 26-bus, 6-generator test system and an IEEE 39-bus, 10-unit New England test systems are considered to show the effectiveness of the proposed method over other methods reported in the literature. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： bacterial foraging optimization algorithm Particle swarm optimization Differential evolution Dynamic economic dispatch Non-smooth fuel cost function

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An ant approach to define product families and remanufacturing cells 10th

An ant approach to define product families and remanufacturi...

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10th IFAC Triennial Conference on Manufacturing Modelling, Management and Control (MIM)

作者： Mejia-Moncayo, Camilo Rojas, Alix E. Kenne, Jean-Pierre Hof, Lucas A. Ecole Technol Super Montreal PQ Canada Univ Ean Bogota Colombia

Cellular manufacturing has the potential to reduce the complexity of a manufacturing system, decoupling a complex factory in mini-factories or cells. This concept has an immense potential to be implemented in remanufacturing, which is a promising circular economy strategy which allows to recover the value added to used products. A remanufacturing system is complex, and this creates a barrier to its implementation. This study introduces an ant-based algorithm which addresses the formation of product/part families and remanufacturing cells. A novel approach used in this study mimics ant behaviour by using an artificial pheromone that reinforces similarities between machines and parts, allowing the definition of cells and families with superior performance over Genetic algorithms (GA) and bacterial foraging optimization algorithm (BFOA) in the solution of benchmark problems. Copyright (C) 2022 The Authors.

关键词： Ant Colony optimization Genetic algorithm bacterial foraging optimization algorithm Cell formation Remanufacturing

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A Novel palmprint authetication system by XWT based feature extraction and BFOA based feature selection and optimization 2

A Novel palmprint authetication system by XWT based feature ...

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IEEE 2nd International Conference on Recent Trends in Information Systems (ReTIS)

作者： Sanyal, Nandita Chatterjee, Amitava Munshi, Sugata BP Poddar Inst Management & Technol Dept Elect Engn 137 VIP Rd Kolkata 700052 India Jadavpur Univ Dept Elect Engn Kolkata 700032 India

ISBN: (纸本)9781479983490

This paper is about a new palm-print based biometric authentication system. It is low-cost as it employs a scanner coupled to a PC, for acquiring the image of the human palm. The histogram of the palm-image is cross wavelet transformed(XWT) with respect to that of a reference palm-image. Several features are extracted from the resulting spectrum, and a trained ANN is used to identify the subject, based on these features. bacterial foraging optimization algorithm(BFOA) is used to select the combination of features that results in the best performance. An Adaptive bacterial foraging optimization algorithm (ABFOA) is also introduced to further augment the performance of the system. Extensive experimental investigations reveal that an authentication accuracy of more than 97% can be obtained with the system.

关键词： ANN Biometric Authentication bacterial foraging optimization algorithm Cross wavelet transform Feature optimization Palm print identification

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Demand Estimation of Water Resources based on Coupling algorithm 31

Demand Estimation of Water Resources based on Coupling Algor...

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第31届中国控制与决策会议

作者： Jiangjiang Zhang Zhuanghua Zhu Yu Chang Di Wu Lei Du Zhihua Cui Complex System and Computational Intelligence Laboratory TaiYuan University of Science and Technology Shanxi Finance &Taxation College

ISBN: (纸本)9781728101071;9781728101064

Demand estimation of water resources is an important basic content in the process of urban water resources planning. To predict water resource demand accurately, a coupling algorithm based on bacterial foraging optimization algorithm(BFOA) and glowworm swarm optimization(GSO) is employed to estimate the demand of water resources in this paper. The historical data(2003-2015) is divided into two parts for convenience in the simulation experiments, one(2003-2012) is trained to gain the weighting factors of the estimation and the other(2013-2015) is used for predicting and evaluating water resources. In addition, the simulation results show that the coupling algorithm achieves a higher accuracy compare with BFOA and GSO on prediction.

关键词： water resources bacterial foraging optimization algorithm glowworm swarm optimization

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FDI Detection Method for Smart Grid Based on IBFO-CNN

FDI Detection Method for Smart Grid Based on IBFO-CNN

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9th International Conference on Signal and Image Processing (ICSIP)

作者： Wang, Hao Zhang, Xiangcong Wang, Zhiying Zhang, Chan Zhang, Feifei Lu, Weizhi Wang, Lei Wang, Bingjie State Grid Henan Informat & Telecommun Co Data Ctr Zhengzhou Peoples R China

ISBN: (纸本)9798350350920

In recent years, false data injection (FDI) poses a serious security threat to the operational status of smart grid. Aiming at the current difficulty of smart grid to detect FDI attacks, this paper proposes a FDI detection method for smart grid based on IBFO-CNN. Firstly, the simulation modeling of FDI is completed based on the state estimation of smart grid. Then the convolutional neural network (CNN) is introduced as the basic FDI detection model, and for the problem of inefficient parameter optimization, the bacterial foraging optimization (BFO) algorithm improved by the multivariate updating strategy is used to realize automatic parameter optimization of the CNN, so as to obtain the FDI detection model of smart grid based on IBFO-CNN. Finally, the designed model is applied to the IEEE 14 bus system to test the model's detection performance for FDI. The experimental results show that the IBFO-CNN model proposed in this paper has high precision for FDI detection in the IEEE 14 bus system, which has a better detection performance and anti-noise performance compared with other models. The proposed method has good performance in engineering practice can effectively guarantee the data security of the smart grid.

关键词： false data injection smart grid convolutional neural network bacterial foraging optimization algorithm multivariate updating strategy

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Image Compression Using Shannon Entropy-Based Image Thresholding 1

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3rd International Conference on Computational Intelligence in Data Mining (ICCIDM)

作者： Chiranjeevi, Karri Jena, Uma Ranjan Harika, Asha VSSUT Dept Elect & Telecommun Engn Burla 768018 Odisha India

ISBN: (数字)9789811038747

ISBN: (纸本)9789811038747;9789811038730

In this paper, we proposed multilevel image thresholding for image compression using Shannon entropy which is maximized by the nature-inspired bacterial foraging optimization algorithm (BFOA). Ordinary threading methods are computationally expensive, while extending for multilevel image thresholding, so there is a need of optimization techniques to reduce the computational time. Particle swarm optimization undergoes instability when particle velocity is maximum. So we proposed a BFOA-based multilevel image thresholding by maximizing Shannon entropy and the results are compared with differential evolution and Particle swarm optimization and proved better in Peak signal-to-noise ratio (PSNR), Compression ratio and reconstructed image quality.

关键词： Image compression Image thresholding Shannon entropy bacterial foraging optimization algorithm Differential evolution

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Flexible Job-Shop Scheduling Problem With Sequencing Flexibility: Mathematical Models And Solution algorithms

Flexible Job-Shop Scheduling Problem With Sequencing Flexibi...

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作者： Alejandro Vital Vital Soto University of Windsor

学位级别：博士

Marketing strategists usually advocate increased product variety to attend better market demand. Furthermore, companies increasingly acquire more advanced manufacturing systems to take care of the increased product mix. Manufacturing resources with different capabilities give a competitive advantage to the industry. Proper management of the current productions resources is crucial for a thriving industry. Flexible job shop scheduling problem (FJSP) is an extension of the classical Job-shop scheduling problem (JSP) where operations can be performed by a set of candidate capable machines. An extended version of the FJSP, entitled FJSP with sequencing flexibility (FJSPS), is studied in this work. The extension considers precedence between the operations in the form of a directed acyclic graph instead of sequential order. In this work, a mixed integer programming (MILP) formulation is presented. A single objective formulation to minimize the weighted tardiness for the FJSP with sequencing flexibility is proposed. A different objective to minimize makespan is also considered. Due to the NP-hardness of the problem, a novel hybrid bacterial foraging optimization algorithm (HBFOA) is developed to tackle the FJSP with sequencing flexibility. It is inspired by the behaviour of the E. coli bacteria. It mimics the process to seek for food. The HBFOA is enhanced with simulated annealing (SA). The HBFOA has been packaged in the form of a decision support system (DSS). A case study of a small and medium-sized enterprise (SME) manufacturing industry is presented to validate the proposed HBFOA and MILP. Additional numerical experiments with instances provided by the literature are considered. The results demonstrate that the HBFOA outperformed the classical dispatching rules and the best integer solution of MILP when minimizing the weighted tardiness and offered comparable results for the makespan instances. In this dissertation, another critical aspect has been studied. In the indu

关键词： Alternative process plans bacterial foraging optimization algorithm Dual-resource problem Flexible job-shop scheduling problem Multi-objective optimization Sequencing flexibility

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Substation Expansion Planning Based on BFOA 30

Substation Expansion Planning Based on BFOA

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30th International Power System Conference (PSC)

作者： Rad, H. Kiani Moravej, Z. Semnan Univ Fac Elect & Comp Engn Semnan Iran

ISBN: (纸本)9781509027057

In recent years, significant research efforts have been devoted to the optimal planning of power systems. Substation Expansion Planning (SEP) as a sub-system of power system planning consists of finding the most economical solution with the optimal location and size of future substations and/or feeders to meet the future demand. The large number of design variables, and combination of discrete and continuous variables makes the substation expansion planning a very challenging problem. So far, various methods have been presented to solve such a complicated problem. Since the bacterial foraging optimization algorithm (BFOA) has been proper results in studies of power systems, and has not been applied to SEP problem yet, this paper develops a new BFO-based method to solve the SEP problem. The technique discussed in this paper uses BFOA to simultaneously optimize the sizes and locations of both the existing and new installed substation and feeders by considering reliability constraints. To clarify the capabilities of the presented method a typical network is considered and the results of applying GA and BFOA on the network are compared. The simulation results demonstrate that the BFOA has the potential to find more optimal results than the other algorithm under the same conditions. Also, the fast convergence, consideration of real-world networks limitations as problem constraints and simplicity in applying to large scale networks are the main features of the proposed method.

关键词： bacterial foraging optimization algorithm Genetic algorithm Substation Expansion Planning

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Bio-inspired hybrid BFOA-PSO algorithm-based reactive power controller in a standalone wind-diesel power system

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INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2021年第3期31卷

作者： Wagle, Raju Sharma, Pawan Sharma, Charu Gjengedal, Terje Pradhan, Chittaranjan UiT Arctic Univ Norway Dept Elect Engn Narvik Norway

With an increase in the penetration of renewable energy sources such as wind into the power systems, the operation and control of voltage/reactive power have become more complicated and challenging than ever. As a result, the reactive power imbalance between reactive power generation and demand instigates a reduction in system voltage stability. To deal with the aforesaid scenarios, automatic voltage regulator (AVR) and static synchronous compensator (STATCOM) are incorporated to curtail the voltage deviations in a standalone wind-diesel power system. In this article, a hybrid bacterial foraging optimization algorithm-particle swarm optimization (hBFOA-PSO) algorithm is proposed for optimizing the PI controller parameters of AVR and STATCOM to further improve the system voltage/reactive power performance. Additionally, H-infinity-loop shaping technique is designed to analyze the performance indexes (ie, robustness and stability) of the presented controller with the aim of handling the unstructured uncertainties from generation and loading situation. In order to present the efficiency of the proposed controllers, the performance of the hBFOA-PSO controller is compared with the performance of the BFOA, PSO, and modified grey wolf optimization (MGWO)-based PI controllers for the same wind-diesel system. The dynamic responses of the wind-diesel system for different disturbance cases have been investigated in the MATLAB/SIMULINK environment.

关键词： bacterial foraging optimization algorithm hybrid BFOA‐ PSO PI controller PSO wind‐ diesel standalone power system

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