自适应膜计算算法及在ABS系统中的应用
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摘要
针对传统方法设计的PID控制器在汽车防抱死控制系统(AntilockBrakingSystem,ABS)中,难以满足全工况运行的性能要求,提出一种自适应膜计算优化算法用于整定PID控制器参数。所提算法改进了变异规则在寻优过程中的自适应性,从而克服算法的收敛速率低及较差的抗欺骗能力。算法通过自适应变异规则、转位规则、交叉规则及交流规则对膜内可行解不断更新,最终求出最优解。通过4个不同维数的典型测试函数对算法的计算精度,收敛速率等性能的测试显示了该算法有效性,并明显优于4种对比算法。最后,将算法用于ABS系统中,整定的PID控制器参数实现了良好的全工况下制动控制性能。
Considering that the traditional design method of PID controller in the automotive antilock braking system(ABS) cannot meet the performance requirements for all operating conditions,a self-adaptive membrane computing optimization algorithm is proposed to tune PID controller parameters.The adaptability of the mutation rules in the optimization process is improved in the proposed algorithm,thus,low convergence rate and poor ability to resist cheating of the algorithm are overcome.Then,four types of rules including the adaptive mutation rule,the transformation rule,the crossover rule,and the communication rule in this algorithm are adopted to constantly update the feasible solution in membrane to obtain the optimal solution.The performance of the algorithm is tested in computing accuracy and convergence rate by four typical test functions with different dimensions,which shows that the proposed algorithm is effective and it is better than other four algorithms.Finally,the proposed algorithm is used for the antilock braking system and the parameters of the PID controller are adjusted to achieve good braking performance under all operating conditions.
Considering that the traditional design method of PID controller in the automotive antilock braking system(ABS) cannot meet the performance requirements for all operating conditions,a self-adaptive membrane computing optimization algorithm is proposed to tune PID controller parameters.The adaptability of the mutation rules in the optimization process is improved in the proposed algorithm,thus,low convergence rate and poor ability to resist cheating of the algorithm are overcome.Then,four types of rules including the adaptive mutation rule,the transformation rule,the crossover rule,and the communication rule in this algorithm are adopted to constantly update the feasible solution in membrane to obtain the optimal solution.The performance of the algorithm is tested in computing accuracy and convergence rate by four typical test functions with different dimensions,which shows that the proposed algorithm is effective and it is better than other four algorithms.Finally,the proposed algorithm is used for the antilock braking system and the parameters of the PID controller are adjusted to achieve good braking performance under all operating conditions.
引文
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[11]王建玉,陈俊伟,王宁.采用ds DNA-MC优化的非线性系统预测控制[J].计算机工程与应用,2010,46(31):225-227.Wang J Y,Chen J W,Wang N.Nonlinear Model Predictive Control using ds DNA-MC Optimization[J].Computer Engineering and Applications,2010,46(31):225-227.
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[15]余志生.汽车理论(第5版)[M].北京:机械工业出版社,2011.Yu Z S.Automobile Theory(Fifth Edition)[M].Beijing:China Machine Press,2011.
[2]张秀如.基于逻辑门限值控制的汽车防抱制动系统研究与开发[D]西安:长安大学,2011.Zhang X R.Anti-lock Braking System Research and Development of Logic Threshold Control[D].Xian:Changan University,2011.
[3]何芝强.PID控制器参数整定方法及其应用研究[D].杭州:浙江大学,2005.He Z Q.Parameters Tuning of PID Controller and its Application[D].Hangzhou:Zhejiang University,2005.
[4]杨广,韩建群.车载ABS模糊控制器仿真研究[J].控制工程,2009,16(S0):110-113.Yang G,Han J Q.Simulation and Research for Vehicle ABS(Anti-lock Brake Systems)Fuzzy Controller[J].Control Engineering of China,2009,16(S0):110-113.
[5]戴彦.汽车ABS滑移率的模糊滑模控制研究[J].机械设计与造,2015,6:80-82.Dai Y.Study on Fuzzy Sliding Mode Control of Anti-Skid-Brake System Based on Slip Ratio[J].Machinery Design&Manufacture,2015,6:80-82.
[6]晏蔚光,李果,余达太,等.基于模糊PID控制的汽车防抱制动系统控制算法研究[J].公路交通科技,2004,21(7):123-126.Yan W G,Li G,Yu D T,et al.Study on Fuzzy PID Control Algorithm for Antilock Brake System[J].Journal of Highway and Transportation Research and Development,2004,21(7):123-126.
[7]李远彬.基于遗传优化的模糊控制汽车ABS仿真研究[M].西安:长安大学,2013:49-56.Li Y B.Vehicle ABS Simulation Study based on Genetic Optimization of Fuzzy Control[M].Xian:Changan University,2013:49-56.
[8]杨智,陈颖.改进粒子群算法及其在PID整定中的应用[J].控制工程,2016,23(2):161-166.Yang Z,Chen Y.Improved Particle Swarm Optimization and its Application in PID Tuning[J].Control Engineering of China,2016,23(2):161-166.
[9]陶吉利.基于DNA计算的遗传算法及应用研究[D].杭州:浙江大学,2007.Tao J L.Research on DNA Computing Based Genetic Algorithms and its Applications[D].Hangzhou:Zhejiang University,2007.
[10]陈俊伟.基于膜计算优化方法的控制系统设计研究[M].杭州:浙江大学,2008.Chen J W.Research on Membrane Computing Optimization Methods for Control System Design[M].Hangzhou:Zhejiang University,2008.
[11]王建玉,陈俊伟,王宁.采用ds DNA-MC优化的非线性系统预测控制[J].计算机工程与应用,2010,46(31):225-227.Wang J Y,Chen J W,Wang N.Nonlinear Model Predictive Control using ds DNA-MC Optimization[J].Computer Engineering and Applications,2010,46(31):225-227.
[12]Neuhauser C,Krone S M.The Genealogy of Samples in Models with Selection[J].Genetics,1997,145(2):519-534.
[13]Tao J L,Wang N.DNA Computing based RNA Genetic Algorithm with Applications in Parameter Estimation of Chemical Engineering Processes[J].Computers Chemical Engineering,2007,31(12):1602-1618.
[14]Zhou F,Zhang G X,Rong H N,et al.A Particle Swarm Optimization based on P Systems[C].Sixth International Conference on Natural Computation,Yantai,China,2010,3003-3007.
[15]余志生.汽车理论(第5版)[M].北京:机械工业出版社,2011.Yu Z S.Automobile Theory(Fifth Edition)[M].Beijing:China Machine Press,2011.