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Game-Based Hierarchical Cooperative Control for Electric Vehicle Lateral Stability via Active Four-Wheel Steering and Direct Yaw-Moment Control

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ENERGIES 2019年第17期12卷 3339页

作者： Zhao, Lin Lu, Shaobo Zhang, Bohan Chongqing Univ Sch Automot Engn Chongqing 400040 Peoples R China Chongqing Univ State Key Lab Mech Transmiss Chongqing 400040 Peoples R China

A Stackelberg game-based cooperative control strategy is proposed for enhancing the lateral stability of a four-wheel independently driving electric vehicle (FWID-EV). An upperlower double-layer hierarchical control structure is adopted for the design of a stability control strategy. The leaderfollower-based Stackelberg game theory (SGT) is introduced to model the interaction between two unequal active chassis control subsystems in the upper layer. In this model, the direct yaw-moment control (DYC) and the active four-wheel steering (AFWS) are treated as the leader and the follower, respectively, based on their natural characteristics. Then, in order to guarantee the efficiency and convergence of the proposed control strategy, a sequential quadratic programming (SQP) algorithm is employed to solve the task allocation problem among the distributed actuators in the lower layer. Also, a double-mode adaptive weight (DMAW)- adjusting mechanism is designed, considering the negative effect of DYC. The results of cosimulation with CarSim and Matlab/Simulink demonstrate that the proposed control strategy can effectively improve the lateral stability by properly coordinating the actions of AFWS and DYC.

关键词： active four-wheel steering direct yaw-moment control lateral stability control Stackelberg game theory sequential quadratic programming

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Active Polynomial Chaos Expansion for Reliability-Based Design Optimization

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AIAA JOURNAL 2019年第12期57卷 5431-5446页

作者： Zhou, Yicheng Lu, Zhenzhou Northwestern Polytech Univ Sch Aeronaut POB 120 Xian 710072 Shaanxi Peoples R China

The aim of the present paper is to develop a strategy for solving reliability-based design optimization (RBDO) problems by sparse polynomial chaos expansion (PCE). Because classic sparse regression methods cannot provide the surrogate error measure that can be employed to improve the sampling performance for failure probability estimation in RBDO, Bayesian compressed sensing with state-of-the-art performance is employed to build sparse PCE in the paper. In the meanwhile, a new active learning function is proposed to adaptively select new training points. Two goals can be achieved using this criterion;that is, most of the selected training points are selected from the desired regions to approximate limit state surfaces, and these points tend to be far away from the existing points in the current design to avoid the clustering problem. Because the sparse PCEs are built in an augmented space, it is made numerically affordable to solve the RBDO problem with no extra computational cost. The computation capability of the proposed method is demonstrated by several analytical RBDO problems. Meanwhile, the design optimization of a stiffened rib of the wing edge in a certain aircraft also verifies its good engineering applicability.

关键词： Numerical Stability Cumulative Distribution Function sequential quadratic programming Finite Element Modeling Design of Experiments Mathematical Models Monte Carlo Simulation Support Vector Machine Sensitivity Analysis Iterative Algorithm

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Fast Evaluation of Low-Thrust Transfers via Multilayer Perceptions

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JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 2019年第12期42卷 2627-2637页

作者： Zhu, Yue-he Luo, Ya-Zhong Natl Univ Def Technol Coll Aerosp Sci & Engn Changsha 410073 Hunan Peoples R China

The design of low-thrust-based multitarget interplanetary missions requires a method to quickly and accurately evaluate the low-thrust transfer between any two visiting targets. Complete evaluation of the low-thrust transfer includes not only the estimation of the optimal fuel consumption but also the judgment of transfer feasibility. In this Paper, a learning-based method is proposed for quickly evaluating low-thrust transfer. An efficient database-generation method for obtaining both the infeasible and optimal transfers is developed. A classification multilayer perception and a regression multilayer perception are trained based on the infeasible and optimal transfers to judge the transfer feasibility and estimate the optimal fuel consumption, respectively. The simulation results show that the well-trained multilayer perceptions are capable of quickly determining the transfer feasibility with an accuracy of greater than 98.3% and approximating the optimal transfer fuel consumption with a relative estimation error of less than 0.37%. The tests on two asteroid chains further show the superiority of the multilayer-perception-based method for application to the design of low-thrust-based multitarget interplanetary missions.

关键词： Thrust Fuel Consumption Asteroids Interplanetary Mission Trajectory Optimization Convolutional Neural Network Thrust Curve Spacecraft Trajectory Optimization sequential quadratic programming Astronomical Unit

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Interfacial debonds of layered anisotropic materials using a quasi-static interface damage model with Coulomb friction

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INTERNATIONAL JOURNAL OF FRACTURE 2018年第1-2期211卷 163-182页

作者： Vodicka, Roman Kormanikova, Eva Ksinan, Filip Tech Univ Kosice Civil Engn Fac Vysokoskolska 9 Kosice 04200 Slovakia

A new quasi-static and energy based formulation of an interface damage model which includes Coulomb friction at the interface between anisotropic solids is provided. The interface traction-relative displacement response is based on an assumption of a thin adhesive layer whose behaviour is analogous to cohesive zone models. The damaged interface is considered, if exposed to a pressure, as a contact zone where Coulomb friction law is also taken into account. As the contacting solids are generally anisotropic, the friction may exhibit some anisotropic behaviour, too, which is included into the proposed model. The solution of the problem is sought numerically by a semi-implicit time-stepping procedure which uses recursive decoupled double minimisation in displacements and damage variables. The spatial discretisation is based on the symmetric Galerkin boundary-element method of a multidomain problem, where the interface variables are calculated by sequential quadratic programming, being a tool for resolving each partial minimisation in the proposed recursive scheme. Sample numerical examples demonstrate applicability of the described model.

关键词： Cohesive contact Coulomb friction Anisotropic friction Anisotropic elasticity Delamination Interface fracture Interface damage Mixed mode crack Symmetric Galerkin BEM sequential quadratic programming

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A novel chaotic differential evolution hybridized with quadratic programming for short-term hydrothermal coordination

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NEURAL COMPUTING & APPLICATIONS 2018年第11期30卷 3533-3544页

作者： Chaudhry, F. A. Amin, M. Iqbal, M. Khan, R. D. Khan, J. A. Comsats Inst Informat Technol Dept Elect Engn Wah Pakistan HITEC Univ Taxila Pakistan

In this paper, a viable global optimizer based on chaotic differential evolution is hybridized with sequential quadratic programming, an efficient local search technique to exploit short-term hydrothermal coordination (STHTC) involved for power generation and its efficient management. A multi-objective optimization framework is established for minimizing the total cost of thermal generators with valve point loading effects satisfying power balance constraint as well as generator operating and hydrodischarge limits, respectively. The proposed model is implemented on various systems comprising hydrogenerating units as well as different thermal units. The results are compared with state-of-the-art heuristic techniques recently employed on STHTC problems, while the reliability, stability and effectiveness of the proposed framework are validated through the comprehensive analysis of Monte Carlo simulations.

关键词： Short-term hydrothermal coordination Chaotic differential evolution sequential quadratic programming Multi-objective optimization Stability analysis

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A new converged Emperor Penguin Optimizer for biding strategy in a day-ahead deregulated market clearing price: A case study in China

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Energy 2021年 227卷

作者： Xiaohui Lu Yang Yang Peifang Wang Yiming Fan Fangzhong Yu Nicholas Zafetti School of Earth Science and Engineering Ministry of Education Key Laboratory of Integrated Regulation and Resource Development on Shallow Lakes Hohai University Nanjing 210098 China Clemson University North Charleston South Carolina United States

In this research, a new approach has been suggested for providing an optimization bidding strategy in the day-ahead market for case research in China. This research uses a newly developed version of Emperor Penguin Optimizer (CEPO) to govern the fitness function of all individuals based on the Market Clearing Price (MCP) probability function. The resemblance amounts between each day and the next day are used for clustering. The clustering is performed based on the well-known subtractive clustering methodology. A simulation model using the probability function in MCP estimates the fitness function of the generated strategies. The results indicate that this method proposed is a statistically effective bidding design in China’s day-ahead market related to two other plans from the literature.

关键词： Market clearing price Resemblance value Subtractive clustering Converged emperor penguin optimizer sequential quadratic programming

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Application of hybrid SPSO-SQP algorithm for simultaneous estimation of space-dependent absorption coefficient and scattering coefficient fields in participating media

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INTERNATIONAL JOURNAL OF THERMAL SCIENCES 2018年 124卷 424-432页

作者： Wei, Lin-Yang Qi, Hong Ren, Ya-Tao Sun, Jian-Ping Wen, Shuang Ruan, Li-Ming Harbin Inst Technol Sch Energy Sci & Engn 92 West Dazhi St Harbin 150001 Heilongjiang Peoples R China

A hybrid stochastic particle swarm optimization (SPSO) and sequential quadratic programming (SQP) algorithm is proposed to estimate the space-dependent absorption coefficient (k(a)) and scattering coefficient (k(s)) fields simultaneously. The retrieval results of SQP algorithm are highly affected by the initial guess, and thus, SPSO is adopted to obtain the initial value of SQP because of its global search capability and lesser dependence on initial guess. The regularization term based on the generalized Gaussian Markov random field model is employed to overcome the ill-posed characteristic of the inverse problem. The inverse coupled radiation-conduction problem is solved in a one-dimensional participating medium with uniform refractive and graded refractive indices exposed to a pulse laser, respectively. All the results show that the separate space-dependent K-a and k(s) fields can be estimated simultaneously and accurately from the coupled optical and thermal signals.

关键词： Stochastic particle swarm optimization sequential quadratic programming Absorption coefficient Scattering coefficient Coupled radiation-conduction problem

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A sensitivity result for quadratic semidefinite programs with an application to a sequential quadratic semidefinite programming algorithm

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COMPUTATIONAL & APPLIED MATHEMATICS 2012年第1期31卷 205-218页

作者： Garces, Rodrigo Gomez, Walter Jarre, Florian Univ La Frontera Dept Engn Math Temuco 01145 Chile EuroAmerica SA Santiago Chile Univ Dusseldorf Inst Math D-40225 Dusseldorf Germany

In this short note a sensitivity result for quadratic semidefinite programming is presented under a weak form of second order sufficient condition. Based on this result, also the local convergence of a sequential quad... 详细信息

关键词： semidefinite programming second order sufficient condition sequential quadratic programming quadratic semidefinite programming sensitivity convergence

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Investigation of Combined SVC and TCSC Versus IPFC in Enhancing Power System Static Security

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INTERNATIONAL JOURNAL OF ENGINEERING RESEARCH IN AFRICA 2018年 40卷 119-135页

作者： Buraimoh, Elutunji Ariyo, Funso Kehinde Omoigui, Michael Osaretin Davidson, Innocent Ewaen Durban Univ Technol Dept Elect Power Engn Durban South Africa Obafemi Awolowo Univ Dept Elect & Elect Engn Ife Nigeria

Electrical power systems are often required to operate at full loading capacity due to ever increasing demand and transmission line contingencies with limited grid expansion. This results in line overload and operating near system limit, thereby threatening system security. Utilization of existing system can be achieved using Flexible Alternating Current Transmission System (FACTS) devices without violating system limits. This research investigation involves static security assessment of a modelled IEEE 30-bus test system in MATLAB/SIMULINK/PSAT environment. The security status with the incorporation of combined Static Var Compensator (SVC), Thyristor Controlled Series Compensator (TCSC) and Interline Power Flow Controller (IPFC) were determined. Prior to this, Contingency Severity Index (CSI) based on Performance Index (PI) of voltage and active power was employed to determine the optimal location of the FACTS devices. sequential quadratic programming (SQP) was applied to determine the optimal sizing/percentage compensation of FACTS. Subsequently, power system with and without the incorporation of FACTS devices were modelled. The ability of the compensated system to withstand credible transmission line contingencies without violating the normal operating limits (bus voltage and line thermal) was examined and presented. The paper presents how combined SVC/TCSC and an IPFC aided the power system to boost its steady state security in the face of possible line contingencies.

关键词： Static security FACTS Power Systems Contingency Analysis sequential quadratic programming

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Uncertainty-Based Structural Optimization Using Universal Grey Number Theory

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AIAA JOURNAL 2019年第11期57卷 5002-5013页

作者： Rao, S. S. Wang, Shuo Univ Miami Dept Mech & Aerosp Engn Coral Gables FL 33146 USA Univ Miami Coral Gables FL 33146 USA Harbin Engn Univ Coll Automat Harbin 150001 Heilongjiang Peoples R China

Several types of uncertainties are encountered in the design of machines and structures. The geometric parameters or dimensions are specified in terms of nominal values and tolerances, the experimentally determined material properties exhibit scatter over a range, and many types of loads are inherently variable (loads due to wind, floods, and earthquakes). It is difficult to develop probability distributions to all such uncertain parameters. Even if probability distributions are developed, their confidence levels need to be established using a t- or chi-squared distribution. However, it is easier and more accurate to model the uncertain quantities/parameters as intervals. Thus, for example, in the interval parameter-based optimization, only the known or expected ranges of the design parameters are used with no need to establish the confidence levels of their intervals. This work presents an interval parameter-based optimum design of structural components. Although the field of interval analysis deals with computation of interval numbers, it suffers from the so-called dependency problem, whereby the computations get more and more inaccurate, resulting in wider intervals of the computed response quantities as compared to the true intervals if multiple arithmetic operations are performed using the same interval number or if complicated expressions are evaluated using interval numbers. Hence, a more accurate method, known as the universal grey number theory, is used in this work for the analysis and computations involved in the optimization procedure. Two numerical optimization problems, which are the design of a stepped bar subjected to an axial load and the design of a welded beam subjected to a transverse load, are presented to illustrate the procedure.

关键词： Structural Optimization Probability Distribution Material Properties Numerical Optimization MATLAB Young's Modulus sequential quadratic programming Genetic Algorithm Compressive Stress Structural System

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