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Multi-objective optimal design of a fuzzy adaptive robust fractional-order PID controller for a nonlinear unmanned flying system

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AEROSPACE SCIENCE AND TECHNOLOGY 2023年 141卷

作者： Ansarian, A. Mahmoodabadi, M. J. Isfahan Univ Technol Dept Elect & Comp Engn Esfahan Iran Sirjan Univ Technol Dept Mech Engn Sirjan Iran

In this study, a fuzzy adaptive robust fractional-order proportional-integral-derivative (FARFOPID) controller is introduced for stabilization of a nonlinear unmanned flying system. At first, a proportional-integral-derivative scheme improved by the fractionalorder technique is employed to control the complicated dynamics of the considered system. An adaptive mechanism based on sliding surfaces is proposed to update the effective gains of the fractional-order PID controller. To improve the performance of the controller in the presence of disturbances and uncertainties, fuzzy systems are used to adjust the parameters of the sliding surfaces. The fuzzy systems, considered in this article, employ the singleton fuzzifier, product inference engine and center average defuzzifier as well as triangular-trapezoidal membership functions. Then, the multi-objective thermal exchange optimization (MOTEO), which has presented a high convergence speed and good performance for global search problems, is utilized to determine the controller parameters in such a way that control efforts and system errors would be minimized, simultaneously. Finally, simulation results clearly depict the effectiveness and efficiency of the proposed approach to handle the regarded unmanned flying system in comparison with other controllers.& COPY;2023 Elsevier Masson SAS. All rights reserved.

关键词： PID controller Fractional-order calculus Adaptive robust control Fuzzy system Multi-objective thermal exchange optimization algorithm Unmanned flying system

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A novel approach to hybrid dynamic environmental-economic dispatch of multi-energy complementary virtual power plant considering renewable energy generation uncertainty and demand response

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RENEWABLE ENERGY 2023年 219卷

作者： Wei, Hui Wang, Wen-sheng Kao, Xiao-xuan China Univ Min & Technol Beijing Sch Management Beijing 100083 Peoples R China China Univ Min & Technol Beijing Res Inst Decis Making Sci & Big Data Beijing 100083 Peoples R China

Renewable energy generation significantly reduces harmful emissions and promotes sustainable development. Still, its volatility leads to extensive grid connections, affecting electricity system security. Diversifying forms of energy consumption further expands the peak valley difference in electricity demand. This study aims to investigate the hybrid dynamic environmental-economic dispatch problem of multi-energy complementary virtual power plant, considering the renewable energy generation uncertainty and demand response to promote renewable energy integration and mitigate the electricity system supply-demand mismatch. It proposes a novel approach based on the sine cosine and multi-objective particle swarm optimization algorithm to reduce the economic and environmental costs of the multi-energy complementary virtual power plant. First, a hybrid dynamic environmental-economic dispatch model of the multi-energy complementary virtual power plant is established, considering climbing power, equality, and inequality constraints. Second, targeting the multi objective, nonlinear, and high-dimension characteristics of the hybrid dynamic environmental-economic dispatch model of the multi-energy complementary virtual power plant, a sine cosine and multi-objective particle swarm optimization algorithm is proposed to optimize the particle position update method. Finally, simulation cases are constructed based on the development trend of the virtual power plant, setting various dispatching situations to validate the robustness of the proposed approach and determining a compromise solution by membership functions. The simulation results show that the lowest economic and environmental costs obtained by the sine cosine and multi-objective particle swarm optimization algorithm are at least 11.37 % and 2.79 % lower than those obtained by the NSGA-II algorithm and multi-objective particle swarm optimization algorithm when considering the renewable energy generation uncertainty and dem

关键词： Hybrid dynamic environmental-economic dispatch Multi-energy complementary virtual power plant Renewable energy generation uncertainty Demand response Sine cosine and multi-objective particle swarm optimization algorithm

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Breast cancer detection in thermograms using a hybrid of GA and GWO based deep feature selection method

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EXPERT SYSTEMS WITH APPLICATIONS 2023年 219卷

作者： Pramanik, Rishav Pramanik, Payel Sarkar, Ram Jadavpur Univ Dept Comp Sci & Engn 188 Raja S C Mallick Rd Kolkata 700032 West Bengal India

Breast cancer is one of the most common reasons for the premature death of women worldwide. However, early detection and diagnosis of the same can save many lives. Hence, computer scientists across the world are also striving to develop reliable models to deal with this disease. One of the reliable methods of detecting breast cancer is through thermal images or thermograms. In medical image analysis, the current research trend is the use of deep learning (DL) models. However, many state-of-the-art DL models generate a large number of features and for processing those features we need a significant amount of memory and computation time. To address this issue, we have proposed a lightweight model to detect signs of abnormality in breast thermograms using a combination of transfer learning-based DL model and feature selection approaches. At first, we employed a deep learning model, called SqueezeNet 1.1 (pre-trained on the ImageNet dataset), fine-tuned on the breast cancer thermal images for the feature extraction purpose. Then a hybrid of Genetic algorithm (GA) and Grey Wolf Optimizer (GWO) is used to reduce the dimension of the obtained feature vector. Before that, a chaotic map is used to create the initial population of GA. The proposed model performs very well in detecting and differentiating malignant and healthy breasts. We have evaluated our model on a publicly available dataset, namely Database For Mastology Research (DMR-IR) and achieved 100% accuracy on the test set using only 3% features extracted by the SqueezeNet 1.1 model.

关键词： Breast cancer Thermal image Deep learning Feature selection optimization algorithm

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Robust optimal control of nonlinear fractional systems

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CHAOS SOLITONS & FRACTALS 2023年 175卷

作者： Liu, Chongyang Zhou, Tuo Gong, Zhaohua Yi, Xiaopeng Teo, Kok Lay Wang, Song Shandong Technol & Business Univ Sch Math & Informat Sci Yantai 264005 Peoples R China Shandong Technol & Business Univ Sch Informat & Elect Engn Yantai 264005 Peoples R China Sunway Univ Sch Math Sci Kuala Lumpur 47500 Malaysia Curtin Univ Sch Elect Engn Comp & Math Sci Perth 6845 Australia

Parameter uncertainty is inevitably found in almost every system and, if is ignored, it could jeopardize the effectiveness of control method. Motivated by this issue, we consider a robust optimal control problem governed by a nonlinear fractional system with uncertain parameters, where the system sensitivity with respect to the uncertain parameters is explicitly included in the cost functional. For this problem, we first prove that the system sensitivity can be expressed as the solution of an auxiliary fractional system. Then, we propose a numerical scheme for discretizing both the original and auxiliary fractional systems, resulting in a finite dimensional optimization problem. Furthermore, a numerical solution algorithm based on gradients of the cost functional is developed for the resulting optimization problem. Finally, numerical results for two example problems are given to demonstrate the validity and applicability of the proposed algorithm.

关键词： Robust optimal control Fractional system Discretization scheme optimization algorithm

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Enhancing long-term stability of photoacoustic gas sensor using an extremum-seeking control algorithm

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INFRARED PHYSICS & TECHNOLOGY 2023年 133卷

作者： Bednyakova, Anastasia Erushin, Evgenii Miroshnichenko, Ilya Kostyukova, Nadezhda Boyko, Andrey Redyuk, Alexey Novosibirsk State Univ Pirogova str 2 Novosibirsk 630090 Russia RAS Inst Laser Phys SB Lavrentyev Ave 15b Novosibirsk 630090 Russia Novosibirsk State Tech Univ K Marksa Ave 20 Novosibirsk 630073 Russia

Smart sensor systems have gained increasing importance in various fields, including healthcare, environmental monitoring, industrial automation, and security. Photoacoustic gas sensors are an emerging type of optical sensor used in various applications due to its enhanced performance characteristics. However, the accuracy and reliability of gas concentration measurements from photoacoustic gas sensors may be impacted by several known limitations, including drift of the gas cell resonant frequency over extended periods of time. Researchers have proposed various solutions, including optimization methods and signal processing algorithms, to address this and others issues. In this paper, we propose a novel solution using an extremum-seeking control algorithm to manage the laser modulation frequency of photoacoustic gas sensors. By tracking the changing resonant frequency of the gas cell, long-term stability can be achieved, making it suitable for environmental monitoring, petroleum exploration, and industrial process control. Our approach has the potential to improve the accuracy and reliability of long-term measurements obtained from photoacoustic gas sensors, providing a stable and reliable method for gas concentration estimation.

关键词： Gas sensing Gas analysis Photoacoustics Absorption Spectroscopy Long-term stability Resonator frequency drift Concentration methane monitoring Extremum-seeking control optimization algorithm

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Parameters determination of the modified J-A model with an optimization algorithm

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INTERNATIONAL JOURNAL OF APPLIED ELECTROMAGNETICS AND MECHANICS 2013年第3期41卷 259-266页

作者： Cong, Haoxi Zou, Liang Zhang, Li Li, Qingmin Shandong Univ Sch Elect Engn Jinan 250061 Shandong Peoples R China North China Elect Power Univ Sch Elect & Elect Engn Beijing Peoples R China

J-A model is widely used in hysteresis modeling as well as performance simulation of the magnetic materials. To achieve preferable adequacy, a modified J-A model is given, however, accurate solution of the parameters is very important for the modified J-A model, especially for over-determined nonlinear equations. In response to solving the over-determined nonlinear equations, this paper first turned the problem of the over-determined nonlinear equations into solving the minimum value of a multivariate function by means of the least square method. While the multivariate function is of high nonlinearity (the function is not continuous and the matrix of the partial derivatives is singular), solution methods using derivative calculation were abandoned, and the direct seeking methods with no derivative calculations (simplex algorithm) were involved to solve the problem. In the end the solution was validated with the use of the genetic algorithm and the simulated annealing algorithm.

关键词： Modified J-A model overdetermined nonlinear equations least square method optimization algorithm simulation

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A fast and efficient discrete evolutionary algorithm for the uncapacitated facility location problem

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EXPERT SYSTEMS WITH APPLICATIONS 2023年 213卷

作者： Zhang, Fazhan He, Yichao Ouyang, Haibin Li, Wenben Hebei GEO Univ Sch Informat Engn Shijiazhuang 050031 Peoples R China Guangzhou Univ Sch Mech & Elect Engn Guangzhou 510006 Peoples R China

In order to solve the uncapacitated facility location problem (UFLP) quickly and effectively, an enhanced group theory-based optimization algorithm (EGTOA) is proposed in this paper. Firstly, a new local search operator, One Direction Mutation Operator, is proposed, which is suitable for solving UFLP. Secondly, a Redundant Checking Strategy is presented to further optimize the quality of feasible solutions. To verify the performance of EGTOA, 15 benchmark instances of UFLP is selected in OR-Library, the comparison results with the 16 existing algorithms show that the solution obtained by EGTOA is better than other algorithms, moreover its speed is much faster than state-of-the-art algorithms. These demonstrates that EGTOA is a fast and effective algorithm for solving UFLP.

关键词： Evolutionary algorithm Facility location problem optimization algorithm One direction mutation operator Redundant checking strategy

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Optimize the Efficiency of Lossy Matching Network: A Top-Down Splitting algorithm Based on Generalized Quality-Based Equation

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2021年第8期68卷 2750-2754页

作者： He, Fei Xie, Qian Wang, Zheng Univ Elect Sci & Technol China Sch Elect Sci & Engn Chengdu 611731 Peoples R China

For the lossy matching network, the efficiency of transferring the power is one of the most important factors. In this brief, a novel generalized quality-based equation has been derived which can accurately calculate the efficiency. This generalized equation covers all the situations that the source and load impedance are both complex and consider both the loss of inductor and capacitor. Meanwhile, the SPICE simulations have been carried out to validate the accuracy of the proposed equation. Based on this generalized equation, a top-down splitting algorithm is proposed to further optimize the efficiency of multi-stage matching network. The proposed method shows better improvement of efficiency than prior works.

关键词： Mathematical model Impedance Inductors Q-factor Capacitors Simulation Shunts (electrical) Lossy matching network efficiency optimization algorithm generalized quality-based equation multi-stage network

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A New Discretization Approach of Bat and K-Means

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2021年第1期12卷 510-516页

作者： Mohamed, Rozlini Samsudin, Noor Azah Univ Tun Hussein Onn Malaysia UTHM Fac Comp Sci & Informat Technol Pant Raja Johor Malaysia

Bat algorithm is one of the optimization techniques that mimic the behavior of bat. Bat algorithm is a powerful algorithm in finding the optimum feature data collection. Classification is one of the data mining tasks that useful in knowledge representation. But, the high dimensional data become the issue in the classification that interrupt classification accuracy. From the literature, feature selection and discretization able to overcome the problem. Therefore, this study aims to show Bat algorithm is potential as a discretization approach and as a feature selection to improve classification accuracy. In this paper, a new hybrid Bat-K-Mean algorithm refer as hBA is proposed to convert continuous data into discrete data called as optimize discrete dataset. Then, Bat is used as feature selection to select the optimum feature from the optimized discrete dataset in order to reduce the dimension of data. The experiment is conducted by using k-Nearest Neighbor to evaluate the effectiveness of discretization and feature selection in classification by comparing with continuous dataset without feature selection, discrete dataset without feature selection, and continuous dataset without discretization and feature selection. Also, to show Bat is potential as a discretization approach and feature selection method. The experiments were carried out using a number of benchmark datasets from the UCI machine learning repository. The results show the classification accuracy is improved with the Bat-K-Means optimized discretization and Bat optimized feature selection.

关键词： Classification discretization feature selection optimization algorithm bat algorithm

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Hierarchical Method for Mining a Prevailing Flight Pattern in Airport Terminal Airspace

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JOURNAL OF AEROSPACE INFORMATION SYSTEMS 2023年第11期20卷 702-718页

作者： Chu, Xiao Zeng, Weili Tan, Xianghua Zhou, Yadong Zhu, Dan Nanjing Univ Aeronaut & Astronaut Coll Civil Aviat Nanjing 211106 Peoples R China Natl Key Lab Air Traff Flow Management Nanjing 210016 Peoples R China

Due to the variety of flight patterns in airport terminal airspace, as well as the high global similarity of different flight patterns entering and leaving from the same runway or corridor, it is difficult for current mainstream methods to achieve good clustering. To this end, this paper first constructs a truncated dynamic time warping (TDTW) trajectory similarity measurement to characterize different trajectory patterns with high global similarity and large local differences. Furthermore, a hierarchical flight pattern mining method is proposed, which is divided into four layers according to different characteristics. The first three layers of the method classify trajectories according to takeoff and landing types, runways, and corridors;whereas the fourth layer uses a K-medoid clustering method based on TDTW, thereby making the mining process more controllable and in line with actual operation. Compared to dynamic time warping, the experimental results show that the intraclass compactness and interclass separation of the cluster obtained by the proposed method have decreased and increased by 44.6 and 20.1%, respectively, and the overall performance has improved by 54.1%. More refined and reasonable flight patterns have been obtained.

关键词： Airport Terminal Airspace Runway Configuration Take off and Landing optimization algorithm Secondary Radar Flight Trajectory Gaussian Mixture Models Artificial Neural Network Probability Density Functions

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