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Optimization of PID Parameters Based on Genetic algorithm and interval algorithm

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Optimization of PID Parameters Based on Genetic Algorithm an...

Influence Analysis of Machining and Installation Errors on the Radial Stiffness of a Non-Pneumatic Mechanical Elastic Wheel

2009中国控制与决策会议

作者： SHAO Xiao-gen,XIAO Li-qing,HAN Cheng-chun Xuzhou Institute of Technology,Xuzhou 221008,China

To overcome simple genetic algorithm’s defects of worse local searching ability and premature convergence,a hybrid algorithm of genetic algorithm and interval algorithm was proposed,and applied to parameters optimization of PID controller by employing interval algorithm in population initialization of genetic algorithm.The simulation and experimental results show that the new algorithm is better than simple genetic algorithm,which can improve the convergence speed,overcome the premature convergence phenomena,reduce the influence of random initial population, improve the convergence precision,and has excellent convergence performance and optimization ability.

关键词： Genetic algorithm interval algorithm PID Controller Simulation

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Chinese Journal of Mechanical Engineering 2018年第4期31卷 92-100页

作者： You-Qun Zhao Zhen Xiao Fen Lin Ming-Min Zhu Yao-Ji Deng Department of Automotive Engineering College of Energy and Power EngineeringNanjing University of Aeronautics and Astronautics

Machining and installation errors are unavoidable in mechanical structures. However, the effect of errors on radial stiffness of the mechanical elastic wheel(ME-Wheel) is not considered in previous studies. To this end, the interval mathematical model and interval finite element model of the ME-Wheel were both established and compared with bench test results. The intercomparison of the influence of the machining and installation errors on the ME-Wheel radial stiffness revealed good consistency among the interval mathematical analysis, interval finite element simulation,and bench test results. Within the interval range of the ME-Wheel machining and installation errors, parametric analysis of the combined elastic rings was performed at different initial radial rigidity values. The results showed that the initial radial stiffness of the flexible tire body significantly influenced the ME-Wheel radial stiffness, and the inverse relationship between the hinge unit length or suspension hub and the radial stiffness was nonlinear. The radial stiffness of the ME-Wheel is predicted by using the interval algorithm for the first time, and the regularity of the radial stiffness between the error and the load on the ME-Wheel is studied, which will lay the foundation for the exact study of the ME-Wheel dynamic characteristics in the future.

关键词： Mechanical elastic wheel Stiffness characteristic Errors interval algorithm interval finite element

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Computational intelligence approach for uncertainty quantification using evidence theory

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Journal of Systems Engineering and Electronics 2013年第2期24卷 250-260页

作者： Bin Suo Yongsheng Cheng Chao Zeng Jun Li Institute of Electronic Engineering China Academy of Engineering Physics

As an alternative or complementary approach to the classical probability theory,the ability of the evidence theory in uncertainty quantification（UQ） analyses is subject of intense research in recent years.Two state-of-the-art numerical methods,the vertex method and the sampling method,are commonly used to calculate the resulting uncertainty based on the evidence theory.The vertex method is very effective for the monotonous system,but not for the non-monotonous one due to its high computational errors.The sampling method is applicable for both systems.But it always requires a high computational cost in UQ analyses,which makes it inefficient in most complex engineering systems.In this work,a computational intelligence approach is developed to reduce the computational cost and improve the practical utility of the evidence theory in UQ analyses.The method is demonstrated on two challenging problems proposed by Sandia National Laboratory.Simulation results show that the computational efficiency of the proposed method outperforms both the vertex method and the sampling method without decreasing the degree of accuracy.Especially,when the numbers of uncertain parameters and focal elements are large,and the system model is non-monotonic,the computational cost is five times less than that of the sampling method.

关键词： uncertainty quantification（UQ） evidence theory hybrid algorithm interval algorithm genetic algorithm（GA）.

维普期刊数据库

An interval Maximum Entropy Method for Quadratic Programming Problem

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International Journal of Mining Science and Technology 2005年第4期15卷 379-383页

作者： RUI Wen-juan, CAO De-xin, SONG Xie-wu School of Sciences, China University of Mining & Technology, Xuzhou, Jiangsu 221008, China School of Sciences China University of Mining & TechnologyXuzhou Jiangsu 221008China

With the idea of maximum entropy function and penalty function methods, we transform the quadratic programming problem into an unconstrained differentiable optimization problem, discuss the interval extension of the maximum entropy function, provide the region deletion test rules and design an interval maximum entropy algorithm for quadratic programming problem. The convergence of the method is proved and numerical results are presented. Both theoretical and numerical results show that the method is reliable and efficient.

关键词： quadratic programming interval algorithm penalty function maximum entropy function

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求连续minimax问题整体解的区间算法

高等学校计算数学学报 2002年第4期24卷 359-365页

作者：曹德欣李苏北吴彦强张洪斌太原030024 中国矿业大学理学院太原理工大学理学院徐州221008

1 引言 Minimax问题是一类重要的数学规划问题,它来源于实际并有极广泛的应用([1],[2]).用区间数学方法求解 minimax问题已取得了一些成果.文[1]对由 C2类函数构成的无约束连续 minimax问题进行了研究,建立了相应的区间算法,文[6]～[11... 详细信息

1 引言 Minimax问题是一类重要的数学规划问题,它来源于实际并有极广泛的应用([1],[2]).用区间数学方法求解 minimax问题已取得了一些成果.文[1]对由 C2类函数构成的无约束连续 minimax问题进行了研究,建立了相应的区间算法,文[6]～[11]分别讨论和建立了无约束和不等式约束的离散minimax问题的区间算法.文[12]、[13]则讨论了最坏

关键词： interval algorithm continuous minimax problem Lipschitz continu- ous function global solution.

Research on Improved Genetic algorithm for Low-dimensional and Multimodal Function Optimization

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Hybrid Genetic algorithm and Application to PID Controllers

2011 Chinese Control and Decision Conference（CCDC）

作者： Liqing Xiao1,2,Huaxiang Wang1,Xiaoju Xu2 1.School of Electrical Engineering & Automation,Tianjin University,Nankai District,Tianjin 300072,China 2.Department of Information & Electrical Engineering,Xuzhou Institute of Technology,Xuzhou Jiangsu 221111,China

In order to overcome the disadvantages that simple genetic algorithm had in low-dimensional and multimodal function optimization,an improved algorithm was proposed.The new algorithm divided the search interval into different areas by method of orthogonal decomposition,produced initial populations that had different individual distribution density by using interval algorithm,and gave each individual an attribute that marked the area which it was in and guaranteed there was more than one individual in each area during the operating process of the algorithm.Particle swarm algorithm was introduced to the mutation operation of genetic algorithm to overcome the shortage of genetic algorithm,that was,the poor search ability in local areas,especially those that had low individual distribution density.When applied to six-hump camel back function and Branin RCOS function optimization,the simulation results show that,under the same experiment condition,in contrast to other improved algorithms,the new one improves the probability of searching to all extreme points,and has stronger ability of low-dimensional multi-modal function optimization,on the premise that the convergence precision is not affected.

关键词： Genetic algorithm interval algorithm Particle Swarm algorithm Six-hump Camel Back Function Branin RCOS Function

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Hybrid Genetic Algorithm and Application to PID Controllers