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Parameter Identification and Nonlinear Compensation Control Design of Electronic Throttle 5

Parameter Identification and Nonlinear Compensation Control ...

5th International-Federation-of-Automatic-Control (IFAC) Conference on Engine and Powertrain Control, Simulation and Modeling (E-COSM)

作者： Feng, Kai Sun, Boqi Hu, Yunfeng Gao, Jinwu Jilin Univ State Key Lab Automot Simulat & Control Nanling Campus Changchun 130022 Jilin Peoples R China Jilin Univ Dept Control Sci & Engn Nanling Campus Changchun 130022 Jilin Peoples R China Jilin Univ Dept Automot Engn Nanling Campus Changchun 130022 Jilin Peoples R China

Precise electronic throttle control is a basic problem of engine control. This paper identifies the model parameters based on input PWM, throttle output angle and angular velocity. But the angular velocity can not be measured directly. Therefore, the kalman filter is applied to calculate the angular velocity. A hybrid optimization algorithm is used to search the parameters of throttle model which minimizes the errors between model and real throttle output angle and angular velocity. The controller is designed based on identified parameters and triple step nonlinear control design. Experimental results indicate the effectiveness of the identified parameters and the controller. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： throttle control parameter identification hybrid optimization algorithm triple-step nonlinear control design

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Study of Valuation Function and Search Strategy in Random Game 30

Study of Valuation Function and Search Strategy in Random Ga...

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30th Chinese Control and Decision Conference (CCDC)

作者： Zhang, Xiao-chuan Li, Qin Wang, Wan-wan Huang, Long-chao Sun, Yong-jie Peng, Li-rong Li, Yi Chongqing Univ Technol Sch Comp Sci & Engn Chongqing 400054 Peoples R China Chongqing Ind Polytechn Coll Chongqing 401120 Peoples R China

ISBN: (纸本)9781538612446

The balance between attack and defense is one of the key problems of computer game system. And EinStein Wilrfelt Nicht! game injects randomness of the game process with the aid of the dice for choosing, increasing the game fun, but also making valuations harder. For this reason, this paper selects two valuation factors of distance and probability, and designs the valuation function specially for this randomness. Introducing multi-thread technology,a hybrid optimization algorithm based on Monte Carlo algorithm and Expected Search algorithm is proposed. From the perspective of system balance, the game system of EinStein Wurfelt Nicht! is reconstructed based on the two aspects of attack and defense. The experimental results show that the improved game system continued to maintain a strong chess force and won the national championship.

关键词： valuation function hybrid optimization algorithm EinStein Wiirfelt Nicht! game

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Model parameters estimation of aero-engine based on hybrid optimization algorithm

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航空动力学报 2011年第7期26卷 1665-1671页

作者： LI Qiu-hong LI Ye-bo JIANG Dian-wen (College of Energy and Power EngineeringNanjing University of Aeronautics and Astronautics Nanjing 210016 China

A hybrid optimization algorithm for the time-domain identification of multivariable,state space model for aero-engine was presented in this *** optimization procedure runs particle swarm optimization(PSO) and least squares optimization(LSO) "in series".PSO starts from an initial population and searches for the optimum solution by updating ***,it can sometimes run into a suboptimal *** LSO can start from the suboptimal solution of PSO,and get an optimum solution by conjugate gradient *** algorithm is suitable for the high-order multivariable system which has many parameters to be estimated in wide *** optimization algorithm is applied to estimate the parameters of a 4-input 4-output state variable model(SVM) for *** simulation results demonstrate the effectiveness of the proposed algorithm.

关键词： aero-engine state variable model(SVM) particle swarm optimization(PSO) least squares optimization(LSO) hybrid optimization algorithm

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A novel hybrid optimization algorithm of computational intelligence techniques for highway passenger volume prediction

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EXPERT SYSTEMS WITH APPLICATIONS 2011年第4期38卷 4198-4205页

作者： Deng, Wu Li, Wen Yang, Xin-hua Dalian Jiaotong Univ Software Inst Dalian 116028 Peoples R China Dalian Univ Technol Minist Educ Key Lab Adv Design & Intelligent Comp Dalian Peoples R China Xiangtan Univ Key Lab Intelligent Mfg Hunan Prov Xiangtan Peoples R China

A novel hybrid optimization algorithm combining computational intelligence techniques is presented to solve the multifactor highway passenger volume prediction problem. In this paper, we can get and discretize a reduced decision table, which implies that the number of evaluation criteria such as travel quantity, fixed-asset investment, railway mileage, and waterway passenger volume are reduced with no information loss through rough set theory (RST) method. Particle swarm optimization (PSO) algorithm based on the random global optimization is inducted into the network training. The PSO algorithm is used for glancing study in order to confirm the initial values, and then the back propagation neural network (BPNN) is used for given accuracy to found the PSO-BPNN model. And this reduced information is used to form a classification rule set, which is regarded as an appropriate input parameter to training PSO-BPNN model. The RST-PSO-BPNN model is obtained to forecast highway passenger volume. The rules developed by RST analysis show the best prediction accuracy if a case matches any one of the rules. The keystone of this hybrid optimization algorithm is using rules developed by RST for an object that matches any one of the rules and the PSO-BPNN model for one that does not match any of them. The effectiveness of our optimization algorithm was verified by experiments comparing the traditional gray model method. For the experiment, highway passenger volumes of China during the period 1995-2009 were selected, and for the validation, the novel hybrid optimization algorithm is reliable. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Highway passenger volume prediction Back propagation neural network Rough set Computational intelligence hybrid optimization algorithm Particle swarm optimization algorithm Discretization

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CFD BASED MULTI-DISCIPLINARY optimization DESIGN OF HIGH-PERFORMANCE DEEP SEA SEISMIC VESSEL 36

CFD BASED MULTI-DISCIPLINARY OPTIMIZATION DESIGN OF HIGH-PER...

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36th ASME International Conference on Ocean, Offshore and Arctic Engineering (OMAE) / Symposium on Safety of Marine Structures and Operations in Honor of Torgeir Moan

作者： Mao Xiaofei Zhang Wenxu Qian Jiankui Wu Minghao WUT Sch Transportat Minist Educ Key Lab High Performance Ship Technol Wuhan Hubei Peoples R China WUT Sch Transportat Wuhan Hubei Peoples R China

ISBN: (纸本)9780791857632

This paper focuses on the application of a ship hull form multi-disciplinary optimization (MDO) system based on the computational fluid dynamics (CFD). Using the iSIGHT software, the MIO system integrates an automatic geometry transformation program and high-fidelity CFD solvers for different sub-disciplines. Hydrodynamics analysis subsystem includes resistance, seakeeping and stability modules. The resistance and seakeeping is analyzed by commercial potential flow CFD codes, the stability is assessed by in-house code. The geometry variation output can be automatically used by the numerical solvers. By means of the design of experiment (DOE) technique, a neural network metamodel is trained to predict short term motion response of the derived ships efficiently. The system has been used in a seismic vessel's hull form optimization to minimize the resistance and maximize the long term seakeeping operability index. Meanwhile, the stability in waves is concerned as a constraint. The hybrid MIGA-NLPQL optimization algorithm is applied for a global-to-local search in resistance optimization. For the synthesis optimization, a Pareto optimal solution set has been obtained and the final solution is achieved by trade-off analysis of the solution set. The entire automatic optimization process can be used for the preliminary design of new high performance vessels.

关键词： Multi-disciplinary optimization Seakeeping neural network meta-model hybrid optimization algorithm iSIGHT CFD

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Developing an optimal layout design of a satellite system by considering natural frequency and attitude control constraints

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AEROSPACE SCIENCE AND TECHNOLOGY 2017年 71卷 172-188页

作者： Fakoor, Mahdi Zadeh, Parviz Mohammad Eskandari, Homa Momeni Univ Tehran Fac New Sci & Technol Tehran Iran

In recent years, there has been a growing research interest in layout design optimization of satellite systems. The layout design optimization of a satellite system is a complex process having a large number of design variables and constraints. This paper presents a hybrid optimization algorithm, which globally explores the design search space using Particle Swarm optimization (PSO) and gradient-based Sequential Quadratic Programming (SQP) to rapidly locate optimum design point. The majority of the previous research works mainly focused on finding reasonable placement of components in satellite layout design, with some specific requirements, which are essential for the satellite stability, control and performance such as attitude control, non-interference and overlap constraints. In this study, additional requirements such as structural stiffness and natural frequency constraints are also considered. The proposed approach is employed on a simplified international global communication satellite. The obtained results indicate that the consideration of natural frequency and attitude control constraints in the configuration layout design of a satellite system can significantly improve the stability and control of the satellite and thus frequency coupling between satellite and launcher can be prevented. In addition, the results indicate that the proposed method provides an effective way of solving layout design optimization problem of satellite systems. (C) 2017 Elsevier Masson SAS. All rights reserved.

关键词： Global optimization Layout design optimization hybrid optimization algorithm Sequential quadratic programming (Su) Particle swarm optimization (PSO)

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hybrid Differential Evolution algorithm Employed for the Optimum Design of a High-Speed PMSM Used for EV Propulsion

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2017年第12期64卷 9824-9833页

作者： Fodorean, Daniel Idoumghar, Lhassane Brevilliers, Mathieu Minciunescu, Paul Irimia, Cristi Tech Univ Cluj Napoca Dept Elect Machines & Drives Cluj Napoca 400114 Romania Univ Haute Alsace Math & Appl Informat Lab F-68100 Mulhouse France ICPE SA Bucharest 030138 Romania Siemens Ind Software Brasov 500203 Romania

This paper presents a new optimization approach, based on a hybrid algorithm, used for the design of a new high-speed permanent magnet synchronous machine (HS-PMSM). The HS-PMSM is used for the electromagnetic propulsion system (i.e., the machine will be connected to a magnetic gear) of an electric vehicle. The hybrid optimization algorithm combines two differential evolution methods and a memory mechanism, in order to increase the quality of the algorithm. The evolution of the rotor structure of the HS-PMSM (running at 22 000 r/min) is presented, as well as some aspects of the mechanical risks due to centrifugal forces. The best suited variant will be optimized based on the new optimization approach and the solution will be numerically and experimentally validated.

关键词： Electromagnetic propulsion system for electric vehicle (EV) high-speed permanent magnet synchronous machine (HS-PMSM) hybrid optimization algorithm

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A Novel hybrid optimization algorithm Based on Multi-agent and Particle Swarm

A Novel Hybrid Optimization Algorithm Based on Multi-agent a...

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International Conference on Components, Packaging and Manufacturing Technology

作者： Shi Dejia Jiang Weijin Ding Xiaoling Hunan Univ Commerce Sch Comp & Elect Engn Changsha 410205 Hunan Peoples R China

ISBN: (纸本)9780878492138

A novel multi-agent particle swarm optimization algorithm (MAI'SO) is proposed for optimal reactive power dispatch and voltage control of power system. The method integrates multi-agent system (MAS) and particle swarm optimization algorithm (PSO). An agent in *** represents a particle to PSO and a candidate solution to the optimization problem. All agents live in a lattice-like environment, with each agent fixed on a lattice-point. In order to decrease fitness value, quickly, agents compete and cooperate with their neighbors. and they can also use knowledge. Making use of these agent interactions and evolution mechanism of ***. MAPSO realizes the purpose of minimizing the value of objective function. MAPSO applied for optimal reactive power is evaluated on an IEEE 30-bus power system. It is shown that the proposed approach converges to better solutions much faster than the earlier reported approaches

关键词： hybrid optimization algorithm multi-agent system particle swarm optimization algorithm

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Multi-objective hybrid PSO-APO algorithm based security constrained optimal power flow with wind and thermal generators

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ENGINEERING SCIENCE AND TECHNOLOGY-AN INTERNATIONAL JOURNAL-JESTECH 2017年第2期20卷 411-426页

作者： Teeparthi, Kiran Kumar, D. M. Vinod NIT Warangal Dept Elect Engn Warangal Andhra Pradesh India

In this paper, a new low level with teamwork heterogeneous hybrid particle swarm optimization and artificial physics optimization (HPSO-APO) algorithm is proposed to solve the multi-objective security constrained optimal power flow (MO-SCOPF) problem. Being engaged with the environmental and total production cost concerns, wind energy is highly penetrating to the main grid. The total production cost, active power losses and security index are considered as the objective functions. These are simultaneously optimized using the proposed algorithm for base case and contingency cases. Though PSO algorithm exhibits good convergence characteristic, fails to give near optimal solution. On the other hand, the APO algorithm shows the capability of improving diversity in search space and also to reach a near global optimum point, whereas, APO is prone to premature convergence. The proposed hybrid HPSO-APO algorithm combines both individual algorithm strengths, to get balance between global and local search capability. The APO algorithm is improving diversity in the search space of the PSO algorithm. The hybrid optimization algorithm is employed to alleviate the line overloads by generator rescheduling during contingencies. The standard IEEE 30-bus and Indian 75-bus practical test systems are considered to evaluate the robustness of the proposed method. The simulation results reveal that the proposed HPSO-APO method is more efficient and robust than the standard PSO and APO methods in terms of getting diverse Pareto optimal solutions. Hence, the proposed hybrid method can be used for the large interconnected power system to solve MO-SCOPF problem with integration of wind and thermal generators. (C) 2017 Karabuk University. Publishing services by Elsevier B.V.

关键词： Multi-objective hybrid optimization algorithm Security constrained optimal power flow Pareto optimal solution

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Study of Valuation Function and Search Strategy in Random Game

Study of Valuation Function and Search Strategy in Random Ga...

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第30届中国控制与决策会议

作者： Zhang Xiao-chuan Li Qin Wang Wan-wan Huang Long-chao Sun Yong-jie Peng Li-rong Li Yi School of Computer Science and Engineering Chongqing University of Technology Chongqing Industry Polytechnic College

The balance between attack and defense is one of the key problems of computer game *** EinStein Würfelt Nicht! game injects randomness of the game process with the aid of the dice for choosing,increasing the game fun,but also making valuations *** this reason,this paper selects two valuation factors of distance and probability,and designs the valuation function specially for this *** multi-thread technology,a hybrid optimization algorithm based on Monte Carlo algorithm and Expected Search algorithm is *** the perspective of system balance,the game system of EinStein Wiirfelt Nicht! is reconstructed based on the two aspects of attack and *** experimental results show that the improved game system continued to maintain a strong chess force and won the national championship.

关键词： valuation function hybrid optimization algorithm EinStein Wiirfelt Nicht! game

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