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Motion mitigation of hinged floating structures by adaptive control

OCEAN ENGINEERING 2020年 216卷 107700-107700页

作者： Xia, Shuyan Xu, Daolin Zhang, Haicheng Shi, Qijia Wen, Guilin Wu, Yousheng Guangzhou Univ Ctr Res Leading Technol Special Equipment Sch Mech & Elect Engn Guangzhou 510006 Peoples R China Hunan Univ State Key Lab Adv Design & Mfg Vehicle Body Changsha 410082 Peoples R China China Ship Sci Res Ctr Wuxi 214082 Jiangsu Peoples R China

System stability has been a crucial issue in design of very large floating structures. Large responses of floating structures not only deteriorate operation conditions but also seriously affect the safety, service life and maintenance cost of structures. In this paper, an idea of using hinge connection in control is proposed for stabilizing a five-modular floating platform. It leads to a great simplification in the deployment of thrusters on semi-submersible modules and only the control of heave, roll and pitch motions needs to be concerned. The motion equation of the floating structure is formulated in conjunction with a Proportional-Derivative (PD) control algorithm and an adaptive estimator for uncertain loads based on Radial Basis Function neural network (RBFnn). sequential quadratic programming (SQP) is used to optimally distribute control thrusts. Numerical simulations verify the effectiveness of the proposed control method.

关键词： Motion control Floating structures Hinge connection Uncertain wave Adaptive estimator sequential quadratic programming

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Treatment of Restaurant Wastewater by Pilot-Scale Electrocoagulation-Electroflotation: Optimization of Operating Conditions

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JOURNAL OF ENVIRONMENTAL ENGINEERING 2013年第7期139卷 1004-1016页

作者： Qin, Xusong Yang, Bin Gao, Furong Chen, Guohua Hong Kong Univ Sci & Technol Dept Chem & Biomol Engn Kowloon Hong Kong Peoples R China Hong Kong Univ Sci & Technol Sch Engn Environm Engn Program Kowloon Hong Kong Peoples R China

In the present study, restaurant wastewater containing high concentrations of chemical oxygen demand (COD), total suspended solids (TSS), and oil and grease (OG) was treated by the combined electrocoagulation-electroflotation (EC-EF) process on a pilot scale. A central composite design was applied to the experiment design and the response surface methodology was adopted to build the response surface models of effluent COD, TSS, and OG. The sequential quadratic programming method was utilized to optimize the operating conditions of the treatment process. The analysis of variance of the experimental data shows that the coefficients of determination of the response surface models of effluent COD, TSS, and OG were 0.98, 0.982, and 0.989, respectively. The validity of these models was tested by confirmation experiments with satisfactory results. Zero trade effluent surcharges were achieved under optimized operating conditions. In spite of the operating cost calculated with respects to energy, electrode, and chemical consumptions, up to Hong Kong Dollar water or 52% of the concession in trade effluent surcharge can be saved, revealing the cost-effectiveness of the combined EC-EF technique and its potential for full-scale implementation. (C) 2013 American Society of Civil Engineers.

关键词： Wastewater management Optimization Costs Oxygen demand Electrochemical method Central composite design Response surface methodology Optimization sequential quadratic programming Wastewater treatment Pilot plant Operating cost

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Computational intelligence methodology for the analysis of RC circuit modelled with nonlinear differential order system

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NEURAL COMPUTING & APPLICATIONS 2018年第6期30卷 1905-1924页

作者： Raja, Muhammad Asif Zahoor Mehmood, Ammara Niazi, Shahab Ahmad Shah, Syed Muslim COMSATS Inst Informat Technol Dept Elect Engn Attock Pakistan Islamia Univ Bahawalpur UCE&T Dept Comp Syst Engn Bahawalpur Pakistan Capital Univ Sci & Technol Dept Elect Engn Islamabad Pakistan

In this study, we solve nonlinear initial value problems arising in circuit analysis by applying bio-inspired computational intelligence technique using feed-forward artificial neural networks (ANNs) optimized with genetic algorithms (GAs), sequential quadratic programming (SQP), and their combined scheme. The system of resister-capacitor (RC) circuit having nonlinear capacitance is mathematically modelled with unsupervised ANNs by defining an energy function in mean-square error (MSE) sense. The objectives are to minimize the MSE for which the parameters of the networks are estimated initially with GA-based global search and in steady state with SQP algorithm for efficient local search. We consider a set of scenarios to evaluate the performance of the proposed scheme for different resistance and capacitance values along with current variations in the nonlinear RC circuit system. The results are compared with well-established fully explicit Runge-Kutta numerical solver in order to verify the accuracy of the applied bio-inspired heuristics. To prove the worth of the scheme, a comprehensive statistical analysis is provided for the performance metrics based on root MSE, mean absolute error, Theil's inequality coefficient, Nash-Sutcliffe efficiency, variance account for, and the coefficient of determination (R-2).

关键词： Electric circuits Artificial neural networks Genetic algorithms sequential quadratic programming Hybrid computing

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A SPARSE NONLINEAR OPTIMIZATION ALGORITHM

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JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS 1994年第3期82卷 519-541页

作者： BETTS, JT FRANK, PD 1. Mathematics and Engineering Analysis Department Research and Technology Division Boeing Computer Services Seattle Washington

One of the most effective numerical techniques for solving nonlinear programming problems is the sequential quadratic programming approach. Many large nonlinear programming problems arise naturally in data fitting and when discretization techniques are applied to systems described by ordinary or partial differential equations. Problems of this type are characterized by matrices which are large and sparse. This paper describes a nonlinear programming algorithm which exploits the matrix sparsity produced by these applications. Numerical experience is reported for a collection of trajectory optimization problems with nonlinear equality and inequality constraints.

关键词： SPARSE NONLINEAR programming sequential quadratic programming TRAJECTORY OPTIMIZATION

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An algorithm for constrained nonlinear optimization under uncertainty

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AUTOMATICA 1999年第2期35卷 217-228页

作者： Darlington, J Pantelides, CC Rustem, B Tanyi, BA Univ London Imperial Coll Sci Technol & Med Dept Comp London SW7 2BZ England Univ London Imperial Coll Sci Technol & Med Ctr Proc Syst Engn London SW7 England

This paper considers robust formulations for the constrained control of systems under uncertainty. The underlying model is nonlinear and stochastic. A mean-variance robustness framework is adopted. We consider formulations to ensure feasibility over the entire domain of the uncertain parameters. However, strict Feasibility may not always be possible, and can also be very expensive. We consider two alternative approaches to address feasibility. Flexibility in the operational conditions is provided via a penalty framework. The robust strategies are rested on a dynamic optimization problem arising from a chemical engineering application. (C) 1999 Elsevier Science Ltd. All rights reserved.

关键词： uncertainty robust optimization risk management mean-variance analysis nonlinear programming sequential quadratic programming Goldstein-Levitin-Polyak algorithm

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Adaptive non-linear coordinated optimal dynamic platoon control of connected autonomous distributed electric vehicles on curved roads

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IET INTELLIGENT TRANSPORT SYSTEMS 2020年第12期14卷 1626-1637页

作者： Guo, Jinghua Jingyao, Wang Li, Keqiang Luo, Yugong Xiamen Univ Sch Aerosp Engn Xiamen Fujian Peoples R China Xiamen Univ Shenzhen Res Inst Xiamen Peoples R China Tsinghua Univ State Key Lab Automot Safety & Energy Beijing Peoples R China

A novel adaptive coordinated platoon control of connected autonomous distributed electric vehicles (CADEVs) on curved roads is proposed to increase traffic security. The nonlinear vehicle dynamic model that can precisely reflect the driving behaviours of CADEVs on curved roads is deduced by the Newton-Euler method. Owing to the fact that CADEVs have the strong coupled, uncertain non-linear and over-actuated features, a novel disturbance observer-based adaptive coordinated optimal dynamic platoon control strategy is presented to supervise the lateral and longitudinal coupled motions of CADEVs on curved roads, in which the switching gains of backstepping sliding mode control term are precisely adjusted by the neural-network technique, and the uniform ultimate boundedness of closed-loop high-level control system is guaranteed through the Lyapunov stability theory. Then, a sequential quadratic programming (SQP) tire distributor is the basic component of the low-level control law, which can realise the dynamic control distribution of over-actuated tire actuators of CADEVs. Finally, the results manifest the effectiveness of the proposed adaptive coordinated platoon control strategy.

关键词： Lyapunov methods nonlinear control systems stability adaptive control electric vehicles control nonlinearities vehicle dynamics quadratic programming closed loop systems neurocontrollers observers variable structure systems uncertain systems actuators road vehicles tyres motion control optimal control mobile robots adaptive nonlinear coordinated optimal dynamic platoon control connected autonomous distributed electric vehicles curved roads CADEVs nonlinear vehicle dynamic model disturbance observer-based adaptive coordinated optimal dynamic platoon control strategy lateral coupled motions longitudinal coupled motions backstepping sliding mode control term dynamic control distribution closed-loop high-level control system Lyapunov stability theory sequential quadratic programming tire actuators neural-network technique

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FMNEICS: fractional Meyer neuro-evolution-based intelligent computing solver for doubly singular multi-fractional order Lane-Emden system

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COMPUTATIONAL & APPLIED MATHEMATICS 2020年第4期39卷 1-18页

作者： Sabir, Zulqurnain Raja, Muhammad Asif Zahoor Shoaib, Muhammad Aguilar, J. F. Gomez Hazara Univ Dept Math & Stat Mansehra Pakistan Natl Yunlin Univ Sci & Technol Future Technol Res Ctr 123 Univ RdSect 3 Touliu 64002 Yunlin Taiwan COMSATS Univ Islamabad Dept Elect & Comp Engn Attock Campus Attock 43600 Pakistan COMSATS Univ Islamabad Dept Math Attock Campus Attock 43600 Pakistan CONACyT Tecnol Nacl Mexico CENIDET Interior Internado Palmira S-N Cuernavaca 62490 Morelos Mexico

In the present study, a novel fractional Meyer neuro-evolution-based intelligent computing solver (FMNEICS) is presented for numerical treatment of doubly singular multi-fractional Lane-Emden system (DSMF-LES) using combined heuristics of Meyer wavelet neural networks (MWNN) optimized with global search efficacy of genetic algorithms (GAs) and sequential quadratic programming (SQP), i.e., MWNN-GASQP. The design of novel FMNEICS for DSMF-LES is presented after derivation from standard Lane-Emden equation, and the singular points and shape factors along with fractional-order terms are analyzed. The MWNN modeling strength is used to represent the system model DSMF-LES in the mean-squared error-based merit function and optimization of the networks is carried out with integrated optimization ability of GASQP. The verification, validation, and perfection of the FMNEICS for three different cases of DSMF-LES are established through comparative studies from reference solutions on convergence, robustness, accuracy, and stability measures. Moreover, the observations through the statistical analysis further authenticate the worth of proposed fractional MWNN-GASQP-based stochastic solver.

关键词： Multi-fractional Lane– Emden model Multi-singular systems Artificial neural networks Meyer wavelet neural networks sequential quadratic programming Genetic algorithms

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Dynamic optimal control of batch rice drying process

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DRYING TECHNOLOGY 2002年第7期20卷 1319-1345页

作者： Olmos, A Trelea, IC Courtois, F Bonazzi, C Trystram, G INAPG INRA ENSIA CemagrefUMR Genie Ind AlimentaireENSIA F-91744 Massy France INA PG UMR Genie & Microbiol Procedes Alimentaires F-78850 Thiverval Grignon France

The drying of paddy rice may result in quality degradation, expressed as a head kernel yield, leading to significant commercial depreciation of the product. A mathematical model of the drying and of the quality degradation process was combined with a dynamic optimization algorithm to determine the drying conditions (air temperature and relative humidity as functions of time) that ensured the highest possible final product quality for a specified drying time and a specified final moisture content. The robustness of the optimal drying strategy with respect to the initial state of the product, to the model parameters and to the initialization of the optimization algorithm was verified. The compromise between the highest achievable final quality and the allowed total drying time was studied. The combination of simulation and optimization yielded a new insight in the rice drying process and in the quality preservation strategies.

关键词： grain breakage dynamic optimization optimal control paddy rice drying sequential quadratic programming

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An NE/SQP method for the bounded nonlinear complementarity problem

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JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS 1998年第2期97卷 493-506页

作者： Gabriel, SA ICF Kaiser Int Fairfax VA 22031 USA

NE/SQP (Refs. 2-3) is a recent algorithm that has proven quite effective for solving the nonlinear complementarity problem (NCP). NE/SQP is robust in the sense that its direction-finding subproblems are always solvable;in addition, the convergence rate of this method is q-quadratic. In this note, we consider a generalized version of NE/SQP, as first described in Ref. 4, which is suitable for the bounded NCP. We extend the work in Ref. 4 by demonstrating a stronger convergence result and present numerical results on test problems.

关键词： nonlinear complementarity problem mathematical programming sequential quadratic programming

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Integrated Neuro-Evolution-Based Computing Paradigm to Study the COVID-19 Transposition and Severity in Romania and Pakistan

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2022年第1期15卷 1-17页

作者： Shoaib, Muhammad Abukhaled, Marwan Kainat, Saba Nisar, Kottakkaran Sooppy Raja, Muhammad Asif Zahoor Zubair, Ghania COMSATS Univ Islamabad Dept Math Attock Campus Attock Pakistan Amer Univ Sharjah Dept Math & Stat Sharjah U Arab Emirates Prince Sattam Bin Abdulaziz Univ Coll Arts & Sci Dept Math Wadi Aldawaser Saudi Arabia Natl Yunlin Univ Sci & Technol Future Technol Res Ctr 123 Univ RdSect 3 Touliu 64002 Yunlin Taiwan

Numerical treatment of the COVID-19 transposition and severity in Romania and Pakistan has been presented in this study, i.e., ANN-GA-SQP through artificial neural network genetic algorithms (ANN-GA) and sequential quadratic programming (SQP), a design of an integrated computational intelligent paradigm, COVID-19 is widely considered to be the greatest health threat humanity has ever faced. In terms of both health and economics, COVID-19 is a huge disaster. Many academics have looked at the COVID-19 model in their research papers, although they use different traditional techniques to represent it. The use of hybrid suggested solutions to solve this issue in the present article is significant, demonstrating the study's novelty. The SIR model of COVID-19 consists of a susceptible, infectious, and recovered class of population. The activation function for the construction of functions based on fitness in mean squared error sense is developed using nonlinear equations of the COVID-19 SIR model for the best performance of ANN-GA-SQP with the combined potential of GA and SQP of a network. While detailed refining is done with efficient local search with SQP, GAs operates as a global search. In addition, a neuron analysis will be presented to verify the effectiveness and complexity of the proposed method. Adam's numerical methodology is applied to compare the sustainability and efficacy of the presented paradigm. Analytical evaluations of mean, median, and semi-interquartile range values, as well as Theil's inequality coefficients, root mean squared error, and mean of absolute deviation) values have been observed. The convergence and correctness of the ANN-GA-SQP approach are further validated by statistical analyses.

关键词： Numerical treatment COVID-19 transmission Artificial neural networks Genetic algorithms sequential quadratic programming

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