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Model Predictive Guidance: A Fixed-Point algorithm

JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 2025年第4期48卷 723-735页

作者： Lu, Ping Davami, Christopher San Diego State Univ Dept Aerosp Engn San Diego CA 92182 USA

Tracking a reference trajectory is a frequent task in aerospace guidance. The technical approaches for tracking guidance design so far are largely application-dependent and heavily rely on labor-intensive practices such as manual gain generation. In this paper the model predictive control (MPC) method is used as the foundation for a systematic tracking guidance design, dubbed appropriately model predictive guidance (MPG). Unlike most existing work utilizing MPC, the method developed in this paper does not require a quadratic program (QP) solver. Instead, the constrained QP from the discretization of the MPC problem is strategically formulated in a novel way so that it has only box constraints. An exceedingly simple but globally convergent fixed-point algorithm with a theoretical guarantee is developed to solve the QP. The relatively general formulation of the MPG problem, the automated tracking command generation, and the very simple yet highly reliable algorithm render this method appealing to many tracking guidance problems and autonomous operations. The application of MPG is demonstrated in planetary powered descent guidance for a high-mass Mars mission.

关键词： optimization algorithm Powered Descent Guidance Spacecraft Guidance Autonomous Guidance and Control Convex optimization Derivative Free optimization Numerical optimization

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Robust Design for Multi-Carrier Multi-Relay Assisted Over-the-Air Computation Networks With Bounded Channel Uncertainties

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IEEE WIRELESS COMMUNICATIONS LETTERS 2024年第8期13卷 2290-2294页

作者： Hu, Changjie Li, Quanzhong Zhang, Qi Li, Qiang Sun Yat Sen Univ Sch Elect & Informat Technol Guangzhou 510006 Peoples R China Peng Cheng Lab Dept Broadband Commun Shenzhen 518055 Peoples R China Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou 510006 Peoples R China

Over-the-air computation (AirComp) has attracted vast attention due to its fast aggregation capacity for large scale distributed data. In order to further improve the computation performance of AirComp, we consider a multi-relay assisted AirComp network over a multi-carrier broadband channel, which can benefit from the multi-carrier diversity. Moreover, we take into account a more practical scenario with bounded channel uncertainties, and the worst-case computational mean square error (CMSE) is minimized by jointly designing the transmit and receiving scaling factors, as well as the amplification coefficient of each relay under the transmit power constraints. To handle the intractable non-convex robust optimization problem, we develop an efficient alternating optimization algorithm based on the cutting-set method (CSM). Numerical results are provided to validate the effectiveness of our proposed schemes.

关键词： Relays Sensors Uncertainty Data communication Atmospheric modeling optimization Wireless networks Over-the-air computation (AirComp) robust transceiver design optimization algorithm cooperative relay multi-carrier transmission

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AMBO:All Members-Based Optimizer for Solving optimization Problems

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Computers, Materials & Continua 2022年第2期70卷 2905-2921页

作者： Fatemeh Ahmadi Zeidabadi Sajjad Amiri Doumari Mohammad Dehghani Zeinab Montazeri Pavel Trojovsky Gaurav Dhiman Department of Mathematics and Computer Sciences Sirjan University of TechnologySirjanIran Department of Electrical and Electronics Engineering Shiraz University of TechnologyShirazIran Department of Mathematics Faculty of ScienceUniversity of Hradec KrálovéHradec Králové50003Czech Republic Department of Computer Science Government Bikram College of CommercePatialaPunjabIndia

There are many optimization problems in different branches of science that should be solved using an appropriate *** optimization algorithms are one of the most efficient approaches to solve this type of *** this paper,a new optimization algorithm called All Members-Based Optimizer(AMBO)is introduced to solve various optimization *** main idea in designing the proposedAMBOalgorithm is to use more information from the population members of the algorithm instead of just a few specific members(such as best member and worst member)to update the population ***,in AMBO,any member of the population can play a role in updating the population *** theory of AMBO is described and then mathematically modeled for implementation on optimization *** performance of the proposed algorithm is evaluated on a set of twenty-three standard objective functions,which belong to three different categories:unimodal,high-dimensional multimodal,and fixed-dimensional multimodal *** order to analyze and compare the optimization results for the mentioned objective functions obtained by AMBO,eight other well-known algorithms have been also *** optimization results demonstrate the ability of AMBO to solve various optimization ***,comparison and analysis of the results show that AMBO is superior andmore competitive than the other mentioned algorithms in providing suitable solution.

关键词： algorithm all members optimization optimization algorithm optimization problem population-based algorithm

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Modelling the deflection of reinforced concrete beams using the improved artificial neural network by imperialist competitive optimization

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STEEL AND COMPOSITE STRUCTURES 2022年第6期42卷 733-745页

作者： Li, Ning Asteris, Panagiotis G. Tran, Trung-Tin Pradhan, Biswajeet Nguyen, Hoang Wuhan Univ Technol Sch Resource & Environm Engn Wuhan 430070 Hubei Peoples R China Sch Pedag & Technol Educ Computat Mech Lab Athens 14121 Greece FPT Univ Dept Informat Technol Danang 550000 Vietnam Univ Technol Sydney Fac Engn & IT Sch Civil & Environm Engn Ctr Adv Modelling & Geospatial Informat Syst CAMG Sydney NSW 2007 Australia King Abdulaziz Univ Ctr Excellence Climate Change Res POB 80234 Jeddah 21589 Saudi Arabia Univ Kebangsaan Malaysia Inst Climate Change Earth Observat Ctr Bangi 43600 Selangor Malaysia Hanoi Univ Min & Geol 18 Vien St Hanoi 100000 Vietnam Hanoi Univ Min & Geol Innovat Sustainable & Responsible Min ISRM Res Gr 18 Vien StDuc Thang Ward Hanoi 100000 Vietnam

This study proposed a robust artificial intelligence (AI) model based on the social behaviour of the imperialist competitive algorithm (ICA) and artificial neural network (ANN) for modelling the deflection of reinforced concrete beams, abbreviated as ICA-ANN model. Accordingly, the ICA was used to adjust and optimize the parameters of an ANN model (i.e., weights and biases) aiming to improve the accuracy of the ANN model in modelling the deflection reinforced concrete beams. A total of 120 experimental datasets of reinforced concrete beams were employed for this aim. Therein, applied load, tensile reinforcement strength and the reinforcement percentage were used to simulate the deflection of reinforced concrete beams. Besides, five other AI models, such as ANN, SVM (support vector machine), GLMNET (lasso and elastic-net regularized generalized linear models), CART (classification and regression tree) and KNN (k-nearest neighbours), were also used for the comprehensive assessment of the proposed model (i.e., ICA-ANN). The comparison of the derived results with the experimental findings demonstrates that among the developed models the ICA-ANN model is that can approximate the reinforced concrete beams deflection in a more reliable and robust manner.

关键词： artificial neural network deflection of RC beam ICA-ANN modelling optimization algorithm

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Neural Network Model of Surface Tension of Hydrocarbon-Alcohol Binary Mixtures

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JOURNAL OF THERMOPHYSICS AND HEAT TRANSFER 2024年第1期38卷 117-128页

作者： Amouei, Hamed Lashkarbolooki, Mostafa Movagharnejad, Kamyar Babol Noshirvani Univ Technol Enhanced Oil Recovery & Gas Proc Res Lab Babol *** Iran Babol Noshirvani Univ Technol Chem Engn Dept Enhanced Oil Recovery & Gas Proc Res Lab Babol *** Iran Babol Noshirvani Univ Technol Chem Engn Dept Babol *** Iran

Since the correlation of surface tension of hydrocarbons and alcohol mixtures has great importance, this study is aimed to propose an artificial neural network model to correlate this thermodynamic property. To calculate the best network architecture and the optimal number of neurons, five sets of input variables and two transfer functions are examined. The results reveal that a feed-forward network with the structure of 5-14-1 and logsis and purelin as transfer functions for hidden and output layers, respectively, leads to the best accuracy. Moreover, it is revealed that choosing temperature, mole fraction, molecular weight of hydrocarbons, molecular weight of alcohols, and critical temperature as input variables can be efficient for an accurate correlation of the surface tension of selected chemicals. The results of the proposed artificial neural network model are also compared to the Shereshefsky and Langmuir thermodynamic models. The results obtained from 34 binary mixtures show the generality and acceptable accuracy of the proposed feed-forward network (with an average absolute relative deviation [AARD] of 0.36%) compared to the Shereshefsky (with an AARD of 0.37%, obtained for 31 binary mixtures) and Langmuir (with an AARD of 0.52%) thermodynamic models.

关键词： Fluid Flow Properties Artificial Neural Network Thermodynamic Properties Thermodynamic Model Thermodynamic Process Particle Swarm optimization Flight Recorder optimization algorithm Universal Gas Constant Thermodynamic Free Energy

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A UAV-based coverage gap detection and resolution in cellular networks: A machine-learning approach

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COMPUTER COMMUNICATIONS 2024年 215卷 41-50页

作者： Mostafa, Ahmed Fahim Abdel-Kader, Mohamed Gadallah, Yasser Amer Univ Cairo New Cairo 11835 Egypt Univ Waterloo Waterloo ON N2L 3G1 Canada Alexandria Univ Alexandria 21544 Egypt

The use of unmanned aerial vehicles (UAVs) to extend the coverage of terrestrial base stations (BSs) in cellular communication systems has been gaining increasing interest in recent years. This is due to the ease of deploying UAV-mounted BSs within relatively short times with low associated costs. In this work, we formulate the problem of deploying UAV-mounted BSs to mitigate the coverage gaps of the terrestrial BSs of cellular networks in some geographic regions. We then devise a technique that provides the optimal bound of the solution to the coverage gap detection and mitigation problem. We also propose a machine-learning (ML) based technique to provide a real-time solution for deploying UAVs to determine and mitigate the coverage gaps. In both solutions, namely, the optimal and ML-based solutions, the deployment of the UAVs is done in such a way that addresses the coverage gaps at the minimum possible cost. Simulation results show that our ML-based technique performs quite closely to the performance of the optimal solution, at a significantly lower complexity, and hence fulfills the real-time requirements of such deployments. It also provides significantly better performance results than a common benchmark solution from the literature.

关键词： Cellular networks UAV deployment Coverage gaps optimization algorithm Machine learning

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CBHQD: A channel state information-based passive line-of-sight human queue detection

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DIGITAL SIGNAL PROCESSING 2024年 154卷

作者： Guo, Yufan Fei, Rong Li, Junhuai Wan, Yuxin Yang, Chenyu Zhao, Zhongqi Khan, Majid Habib Li, Mingyue Xian Univ Technol Sch Comp Sci & Engn Xian 710048 Shannxi Peoples R China Xian Univ Technol Shaanxi Key Lab Network Comp & Secur Technol Xian 710048 Shannxi Peoples R China Xian Univ Technol Human Machine Integrat Intelligent Robot Shaanxi U Xian 710048 Shannxi Peoples R China

As the number of monitored individuals rises and multipath effects disrupt signals, existing queue monitoring solutions fail to meet efficiency needs. This paper proposes a passive line-of-sight (LOS) human queue detection method based on channel state information (CSI), namely CBHQD. We present a novel time series crowd detection network (TSCD-Net), incorporating genetic algorithm, LSTM, and FC layers to automatically extract amplitude and phase features from CSI, enhancing the simulation of indoor conditions. The genetic algorithm effectively addresses the challenge of local optima, while the fully connected layers excel in dimension reduction, facilitating the integration of valuable information obtained from LSTM. Additionally, we design the Fresnel zone detection, merging the Fresnel zone model with WiFi to estimate people's walking direction, thereby maximizing the accuracy performance of crowd detection. Lastly, we validate the feasibility and efficiency of our approach in a realistic testbed, demonstrating its suitability for detecting larger numbers of individuals.

关键词： WiFi sensing Channel state information Human queue detection optimization algorithm Time series Long short-term memory (LSTM)

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A Novel Near-Field Calibration Method for Linear Array Based on Plane Wave Expansion

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IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION 2024年第11期72卷 8173-8183页

作者： Tang, Yuanhua Wang, Zhengpeng Fan, Wei Chen, Xiaoming Loh, Tian-Hong Luo, Zhiming Gao, Steven Beihang Univ Dept Elect & Informat Engn Beijing 100191 Peoples R China Southeast Univ Sch Informat Sci & Engn Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China Jiaotong Univ Sch Informat & Commun Engn Xian 710049 Peoples R China Natl Phys Lab Engn Electromagnet & Electrochem Technol Dept Teddington England Chinese Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China

This article presents a novel near-field calibration method for linear arrays based on plane wave expansion. The proposed method enables the calibration of an antenna under test (AUT) with a compact measurement distance and a probe scanning aperture as small as 1/10 of the AUT aperture. The calibration measurement can be implemented by moving a probe antenna in a sparse grid along a line segment parallel to the AUT and collecting the AUT array element response. In the proposed method, an ideal virtual linear array is employed to model the AUT, where the virtual AUT possesses the same configuration as the AUT and consists of array elements with consistent radiation characteristics. The virtual AUT is determined by minimizing the difference between the response of the virtual and actual AUT, where the virtual AUT response can be constructed based on plane wave expansion. The least square (LS) method is applied to minimize the difference, while an optimization algorithm is employed to solve the radiation characteristics and the initial excitations of the virtual AUT. In the validation measurement, a nonuniform linear array (NULA) is calibrated in the near field using the proposed method with a probe scanning aperture of only 1/10 of the AUT aperture and a measurement distance of 4.33 wavelengths at 2.6 GHz. Based on the obtained results, +/- 0.41 dB in amplitude and +/- 4.7 degrees in phase calibration accuracy are achieved, respectively, demonstrating the effectiveness of the proposed method.

关键词： Probes Calibration Antenna measurements Phased arrays Linear antenna arrays Antenna radiation patterns Phase measurement Linear array near-field calibration optimization algorithm plane wave expansion

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Machine learning-based strength prediction for circular concrete-filled double-skin steel tubular columns under axial compression

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ENGINEERING STRUCTURES 2025年 325卷

作者： Li, Shou-Zhen Wang, Jin-Jin Jiang, Liming Deng, Ran Wang, Yu-Hang Hong Kong Polytech Univ Dept Bldg Environm & Energy Engn Kowloon Hong Kong Peoples R China Hong Kong Polytech Univ Shenzhen Res Inst Shenzhen 518063 Peoples R China Hong Kong Polytech Univ Dept Civil & Environm Engn Kowloon Hong Kong Peoples R China Chongqing Univ Sch Civil Engn Chongqing 400000 Peoples R China Key Lab New Technol Construct Cities Mt Area Chongqing 400045 Peoples R China

Circular concrete-filled double skin steel tubular (CFDST) columns show great potential in various infrastructures due to their excellent structural performance and easy construction process. As the rapid development of infrastructure towards large scales and manifold functions, CFDST columns within an extended scope of dimensions and sectional configurations will be applied in engineering practice. This trend poses challenges to both the accuracy and efficiency of traditional strength prediction methods including design equations, experimental fitting and numerical simulations. Based on experimental datasets obtained from existing research, machine learning (ML) methods pave a path for understanding the complex relations between key parameters and the axial compressive ultimate strength of CFDST columns. This paper attempts to develop reliable ML-based models for predicting the axial compressive ultimate strength of CFDST columns. Three advanced ML algorithms, namely Back Propagation Neural Network (BPNN), Support Vector Regression (SVR) and Gaussian Process Regression (GPR), were adopted to develop prediction models by training an experimental database of 162 CFDST specimens from existing literature which covers a wide scope of parameters including high diameter-to-thickness ratios and large hollow ratios. Particularly, Particle Swarm optimization (PSO) method was used to optimize the hyperparameters of ML algorithms for enhancing the prediction accuracy. The performances of the developed prediction models were evaluated through an in-depth comparison analysis. It is found that the ML-based models could predict the strengths of CFDST columns with higher accuracies and wider applicable ranges than existing design methods;PSO-enhanced GPR (PSO-GPR) model achieved the most improved performance for strength prediction. Further validation on PSO-GPR model was conducted based on experimental data from recently tested large-dimension CFDST specimens and the results demon

关键词： Ultimate strength prediction Machine learning optimization algorithm CFDST Parameter effects

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A new fractional Abel polynomials neural network method for nonlinear models of heat transfer

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INTERNATIONAL COMMUNICATIONS IN HEAT AND MASS TRANSFER 2025年 162卷

作者： Hassani, Hossein Avazzadeh, Zakieh Turan-Dincel, Arzu Katani, Roghayeh Anand Int Coll Engn Dept Math Jaipur 303012 Rajasthan India Anhui Univ Stony Brook Inst Hefei 230601 Anhui Peoples R China Yildiz Tech Univ Dept Math Engn TR-34220 Istanbul Turkiye Univ Yasuj Fac Sci Yasuj Iran Univ South Africa Dept Math Sci Florida South Africa

Nowadays, the study and advancements in neural networks and optimization techniques are the most interesting research topics. This progress has empowered researchers to address more intricate and realistic challenges associated with nonlinear models. This research presents a new approach called the fractional Abel polynomials neural network (FAPNN) for solving nonlinear heat transfer models. The mentioned NN consists of three layers (input layer, hidden layer, output layer). Fractional Abel and arcsinh(t) functions have been used as activation functions in the hidden and output layers, respectively, to construct the FAPNN. To mitigate storage and computational costs, new operational matrices (OMs) are devised. Based on FAPNN and OMs for fractional Abel polynomials, a series solution is achieved for the nonlinear heat transfer models. The Lagrange multipliers method is adopted so that the nonlinear heat transfer models can be transformed into a class of nonlinear algebraic system of equations and we solve these equations using Matlab and Maple software. Ultimately, four sets of numerical examples are provided along with comparisons from the literature such as the variational iteration, homotopy perturbation, differential transformation, and Chebyshev wavelets methods, to confirm the effectiveness of the suggested FAPNN approach. Experimental results demonstrate the effectiveness of our proposed method.

关键词： Fractional Abel polynomials neural network Optimal solutions Nonlinear models of heat transfer optimization algorithm

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